research paper on mobile technology

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Study and Investigation on 5G Technology: A Systematic Review

Ramraj dangi.

1 School of Computing Science and Engineering, VIT University Bhopal, Bhopal 466114, India; [email protected] (R.D.); [email protected] (P.L.)

Praveen Lalwani

Gaurav choudhary.

2 Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; moc.liamg@7777yrahduohcvaruag

3 Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea

Giovanni Pau

4 Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy; [email protected]

Associated Data

Not applicable.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

1. Introduction

Most recently, in three decades, rapid growth was marked in the field of wireless communication concerning the transition of 1G to 4G [ 1 , 2 ]. The main motto behind this research was the requirements of high bandwidth and very low latency. 5G provides a high data rate, improved quality of service (QoS), low-latency, high coverage, high reliability, and economically affordable services. 5G delivers services categorized into three categories: (1) Extreme mobile broadband (eMBB). It is a nonstandalone architecture that offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. (2) Massive machine type communication (eMTC), 3GPP releases it in its 13th specification. It provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. (3) ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. [ 3 ].

1.1. Evolution from 1G to 5G

First generation (1G): 1G cell phone was launched between the 1970s and 80s, based on analog technology, which works just like a landline phone. It suffers in various ways, such as poor battery life, voice quality, and dropped calls. In 1G, the maximum achievable speed was 2.4 Kbps.

Second Generation (2G): In 2G, the first digital system was offered in 1991, providing improved mobile voice communication over 1G. In addition, Code-Division Multiple Access (CDMA) and Global System for Mobile (GSM) concepts were also discussed. In 2G, the maximum achievable speed was 1 Mpbs.

Third Generation (3G): When technology ventured from 2G GSM frameworks into 3G universal mobile telecommunication system (UMTS) framework, users encountered higher system speed and quicker download speed making constant video calls. 3G was the first mobile broadband system that was formed to provide the voice with some multimedia. The technology behind 3G was high-speed packet access (HSPA/HSPA+). 3G used MIMO for multiplying the power of the wireless network, and it also used packet switching for fast data transmission.

Fourth Generation (4G): It is purely mobile broadband standard. In digital mobile communication, it was observed information rate that upgraded from 20 to 60 Mbps in 4G [ 4 ]. It works on LTE and WiMAX technologies, as well as provides wider bandwidth up to 100 Mhz. It was launched in 2010.

Fourth Generation LTE-A (4.5G): It is an advanced version of standard 4G LTE. LTE-A uses MIMO technology to combine multiple antennas for both transmitters as well as a receiver. Using MIMO, multiple signals and multiple antennas can work simultaneously, making LTE-A three times faster than standard 4G. LTE-A offered an improved system limit, decreased deferral in the application server, access triple traffic (Data, Voice, and Video) wirelessly at any time anywhere in the world.LTE-A delivers speeds of over 42 Mbps and up to 90 Mbps.

Fifth Generation (5G): 5G is a pillar of digital transformation; it is a real improvement on all the previous mobile generation networks. 5G brings three different services for end user like Extreme mobile broadband (eMBB). It offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. Massive machine type communication (eMTC), it provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. Ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. 5G faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability and scalability, and energy-efficient mobile communication technology [ 6 ]. 5G mainly divided in two parts 6 GHz 5G and Millimeter wave(mmWave) 5G.

6 GHz is a mid frequency band which works as a mid point between capacity and coverage to offer perfect environment for 5G connectivity. 6 GHz spectrum will provide high bandwidth with improved network performance. It offers continuous channels that will reduce the need for network densification when mid-band spectrum is not available and it makes 5G connectivity affordable at anytime, anywhere for everyone.

mmWave is an essential technology of 5G network which build high performance network. 5G mmWave offer diverse services that is why all network providers should add on this technology in their 5G deployment planning. There are lots of service providers who deployed 5G mmWave, and their simulation result shows that 5G mmwave is a far less used spectrum. It provides very high speed wireless communication and it also offers ultra-wide bandwidth for next generation mobile network.

The evolution of wireless mobile technologies are presented in Table 1 . The abbreviations used in this paper are mentioned in Table 2 .

Summary of Mobile Technology.

Table of Notations and Abbreviations.

1.2. Key Contributions

The objective of this survey is to provide a detailed guide of 5G key technologies, methods to researchers, and to help with understanding how the recent works addressed 5G problems and developed solutions to tackle the 5G challenges; i.e., what are new methods that must be applied and how can they solve problems? Highlights of the research article are as follows.

• This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
• In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
• This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
• This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
• we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
• Security challenges and considerations while developing 5G technology are discussed.
• Finally, the paper concludes with the future directives.

The existing survey focused on architecture, key concepts, and implementation challenges and issues. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products.

2. Existing Surveys and Their Applicability

In this paper, a detailed survey on various technologies of 5G networks is presented. Various researchers have worked on different technologies of 5G networks. In this section, Table 3 gives a tabular representation of existing surveys of 5G networks. Massive MIMO, NOMA, small cell, mmWave, beamforming, and MEC are the six main pillars that helped to implement 5G networks in real life.

A comparative overview of existing surveys on different technologies of 5G networks.

2.1. Limitations of Existing Surveys

The existing survey focused on architecture, key concepts, and implementation challenges and issues. The numerous current surveys focused on various 5G technologies with different parameters, and the authors did not cover all the technologies of the 5G network in detail with challenges and recent advancements. Few authors worked on MIMO (Non-Orthogonal Multiple Access) NOMA, MEC, small cell technologies. In contrast, some others worked on beamforming, Millimeter-wave (mmWave). But the existing survey did not cover all the technologies of the 5G network from a research and advancement perspective. No detailed survey is available in the market covering all the 5G network technologies and currently published research trade-offs. So, our main aim is to give a detailed study of all the technologies working on the 5G network. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products. This survey article collected key information about 5G technology and recent advancements, and it can be a kind of a guide for the reader. This survey provides an umbrella approach to bring multiple solutions and recent improvements in a single place to accelerate the 5G research with the latest key enabling solutions and reviews. A systematic layout representation of the survey in Figure 1 . We provide a state-of-the-art comparative overview of the existing surveys on different technologies of 5G networks in Table 3 .

Systematic layout representation of survey.

2.2. Article Organization

This article is organized under the following sections. Section 2 presents existing surveys and their applicability. In Section 3 , the preliminaries of 5G technology are presented. In Section 4 , recent advances of 5G technology based on Massive MIMO, NOMA, Millimeter Wave, 5G with IoT, machine learning for 5G, and Optimization in 5G are provided. In Section 5 , a description of novel 5G features over 4G is provided. Section 6 covered all the security concerns of the 5G network. Section 7 , 5G technology based on above-stated challenges summarize in tabular form. Finally, Section 8 and Section 9 conclude the study, which paves the path for future research.

3. Preliminary Section

3.1. emerging 5g paradigms and its features.

5G provides very high speed, low latency, and highly salable connectivity between multiple devices and IoT worldwide. 5G will provide a very flexible model to develop a modern generation of applications and industry goals [ 26 , 27 ]. There are many services offered by 5G network architecture are stated below:

Massive machine to machine communications: 5G offers novel, massive machine-to-machine communications [ 28 ], also known as the IoT [ 29 ], that provide connectivity between lots of machines without any involvement of humans. This service enhances the applications of 5G and provides connectivity between agriculture, construction, and industries [ 30 ].

Ultra-reliable low latency communications (URLLC): This service offers real-time management of machines, high-speed vehicle-to-vehicle connectivity, industrial connectivity and security principles, and highly secure transport system, and multiple autonomous actions. Low latency communications also clear up a different area where remote medical care, procedures, and operation are all achievable [ 31 ].

Enhanced mobile broadband: Enhance mobile broadband is an important use case of 5G system, which uses massive MIMO antenna, mmWave, beamforming techniques to offer very high-speed connectivity across a wide range of areas [ 32 ].

For communities: 5G provides a very flexible internet connection between lots of machines to make smart homes, smart schools, smart laboratories, safer and smart automobiles, and good health care centers [ 33 ].

For businesses and industry: As 5G works on higher spectrum ranges from 24 to 100 GHz. This higher frequency range provides secure low latency communication and high-speed wireless connectivity between IoT devices and industry 4.0, which opens a market for end-users to enhance their business models [ 34 ].

New and Emerging technologies: As 5G came up with many new technologies like beamforming, massive MIMO, mmWave, small cell, NOMA, MEC, and network slicing, it introduced many new features to the market. Like virtual reality (VR), users can experience the physical presence of people who are millions of kilometers away from them. Many new technologies like smart homes, smart workplaces, smart schools, smart sports academy also came into the market with this 5G Mobile network model [ 35 ].

3.2. Commercial Service Providers of 5G

5G provides high-speed internet browsing, streaming, and downloading with very high reliability and low latency. 5G network will change your working style, and it will increase new business opportunities and provide innovations that we cannot imagine. This section covers top service providers of 5G network [ 36 , 37 ].

Ericsson: Ericsson is a Swedish multinational networking and telecommunications company, investing around 25.62 billion USD in 5G network, which makes it the biggest telecommunication company. It claims that it is the only company working on all the continents to make the 5G network a global standard for the next generation wireless communication. Ericsson developed the first 5G radio prototype that enables the operators to set up the live field trials in their network, which helps operators understand how 5G reacts. It plays a vital role in the development of 5G hardware. It currently provides 5G services in over 27 countries with content providers like China Mobile, GCI, LGU+, AT&T, Rogers, and many more. It has 100 commercial agreements with different operators as of 2020.

Verizon: It is American multinational telecommunication which was founded in 1983. Verizon started offering 5G services in April 2020, and by December 2020, it has actively provided 5G services in 30 cities of the USA. They planned that by the end of 2021, they would deploy 5G in 30 more new cities. Verizon deployed a 5G network on mmWave, a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave is a faster and high-band spectrum that has a limited range. Verizon planned to increase its number of 5G cells by 500% by 2020. Verizon also has an ultra wide-band flagship 5G service which is the best 5G service that increases the market price of Verizon.

Nokia: Nokia is a Finnish multinational telecommunications company which was founded in 1865. Nokia is one of the companies which adopted 5G technology very early. It is developing, researching, and building partnerships with various 5G renders to offer 5G communication as soon as possible. Nokia collaborated with Deutsche Telekom and Hamburg Port Authority and provided them 8000-hectare site for their 5G MoNArch project. Nokia is the only company that supplies 5G technology to all the operators of different countries like AT&T, Sprint, T-Mobile US and Verizon in the USA, Korea Telecom, LG U+ and SK Telecom in South Korea and NTT DOCOMO, KDDI, and SoftBank in Japan. Presently, Nokia has around 150+ agreements and 29 live networks all over the world. Nokia is continuously working hard on 5G technology to expand 5G networks all over the globe.

AT&T: AT&T is an American multinational company that was the first to deploy a 5G network in reality in 2018. They built a gigabit 5G network connection in Waco, TX, Kalamazoo, MI, and South Bend to achieve this. It is the first company that archives 1–2 gigabit per second speed in 2019. AT&T claims that it provides a 5G network connection among 225 million people worldwide by using a 6 GHz spectrum band.

T-Mobile: T-Mobile US (TMUS) is an American wireless network operator which was the first service provider that offers a real 5G nationwide network. The company knew that high-band 5G was not feasible nationwide, so they used a 600 MHz spectrum to build a significant portion of its 5G network. TMUS is planning that by 2024 they will double the total capacity and triple the full 5G capacity of T-Mobile and Sprint combined. The sprint buyout is helping T-Mobile move forward the company’s current market price to 129.98 USD.

Samsung: Samsung started their research in 5G technology in 2011. In 2013, Samsung successfully developed the world’s first adaptive array transceiver technology operating in the millimeter-wave Ka bands for cellular communications. Samsung provides several hundred times faster data transmission than standard 4G for core 5G mobile communication systems. The company achieved a lot of success in the next generation of technology, and it is considered one of the leading companies in the 5G domain.

Qualcomm: Qualcomm is an American multinational corporation in San Diego, California. It is also one of the leading company which is working on 5G chip. Qualcomm’s first 5G modem chip was announced in October 2016, and a prototype was demonstrated in October 2017. Qualcomm mainly focuses on building products while other companies talk about 5G; Qualcomm is building the technologies. According to one magazine, Qualcomm was working on three main areas of 5G networks. Firstly, radios that would use bandwidth from any network it has access to; secondly, creating more extensive ranges of spectrum by combining smaller pieces; and thirdly, a set of services for internet applications.

ZTE Corporation: ZTE Corporation was founded in 1985. It is a partially Chinese state-owned technology company that works in telecommunication. It was a leading company that worked on 4G LTE, and it is still maintaining its value and doing research and tests on 5G. It is the first company that proposed Pre5G technology with some series of solutions.

NEC Corporation: NEC Corporation is a Japanese multinational information technology and electronics corporation headquartered in Minato, Tokyo. ZTE also started their research on 5G, and they introduced a new business concept. NEC’s main aim is to develop 5G NR for the global mobile system and create secure and intelligent technologies to realize 5G services.

Cisco: Cisco is a USA networking hardware company that also sleeves up for 5G network. Cisco’s primary focus is to support 5G in three ways: Service—enable 5G services faster so all service providers can increase their business. Infrastructure—build 5G-oriented infrastructure to implement 5G more quickly. Automation—make a more scalable, flexible, and reliable 5G network. The companies know the importance of 5G, and they want to connect more than 30 billion devices in the next couple of years. Cisco intends to work on network hardening as it is a vital part of 5G network. Cisco used AI with deep learning to develop a 5G Security Architecture, enabling Secure Network Transformation.

3.3. 5G Research Groups

Many research groups from all over the world are working on a 5G wireless mobile network [ 38 ]. These groups are continuously working on various aspects of 5G. The list of those research groups are presented as follows: 5GNOW (5th Generation Non-Orthogonal Waveform for Asynchronous Signaling), NEWCOM (Network of Excellence in Wireless Communication), 5GIC (5G Innovation Center), NYU (New York University) Wireless, 5GPPP (5G Infrastructure Public-Private Partnership), EMPHATIC (Enhanced Multi-carrier Technology for Professional Adhoc and Cell-Based Communication), ETRI(Electronics and Telecommunication Research Institute), METIS (Mobile and wireless communication Enablers for the Twenty-twenty Information Society) [ 39 ]. The various research groups along with the research area are presented in Table 4 .

Research groups working on 5G mobile networks.

3.4. 5G Applications

5G is faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability, greater scalablility, and energy-efficient mobile communication technology [ 6 ].

There are lots of applications of 5G mobile network are as follows:

• High-speed mobile network: 5G is an advancement on all the previous mobile network technologies, which offers very high speed downloading speeds 0 of up to 10 to 20 Gbps. The 5G wireless network works as a fiber optic internet connection. 5G is different from all the conventional mobile transmission technologies, and it offers both voice and high-speed data connectivity efficiently. 5G offers very low latency communication of less than a millisecond, useful for autonomous driving and mission-critical applications. 5G will use millimeter waves for data transmission, providing higher bandwidth and a massive data rate than lower LTE bands. As 5 Gis a fast mobile network technology, it will enable virtual access to high processing power and secure and safe access to cloud services and enterprise applications. Small cell is one of the best features of 5G, which brings lots of advantages like high coverage, high-speed data transfer, power saving, easy and fast cloud access, etc. [ 40 ].
• Entertainment and multimedia: In one analysis in 2015, it was found that more than 50 percent of mobile internet traffic was used for video downloading. This trend will surely increase in the future, which will make video streaming more common. 5G will offer High-speed streaming of 4K videos with crystal clear audio, and it will make a high definition virtual world on your mobile. 5G will benefit the entertainment industry as it offers 120 frames per second with high resolution and higher dynamic range video streaming, and HD TV channels can also be accessed on mobile devices without any interruptions. 5G provides low latency high definition communication so augmented reality (AR), and virtual reality (VR) will be very easily implemented in the future. Virtual reality games are trendy these days, and many companies are investing in HD virtual reality games. The 5G network will offer high-speed internet connectivity with a better gaming experience [ 41 ].
• Smart homes : smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high-speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network as it offers very high-speed low latency communication.
• Smart cities: 5G wireless network also helps develop smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy-saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.
• Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance, and logistics. 5G smart sensor technology also offers smarter, safer, cost-effective, and energy-saving industrial IoT operations.
• Smart Farming: 5G technology will play a crucial role in agriculture and smart farming. 5G sensors and GPS technology will help farmers track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation, pest, insect, and electricity control.
• Autonomous Driving: The 5G wireless network offers very low latency high-speed communication, significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects, and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is essential for autonomous vehicles, decision-making is done in microseconds to avoid accidents.
• Healthcare and mission-critical applications: 5G technology will bring modernization in medicine where doctors and practitioners can perform advanced medical procedures. The 5G network will provide connectivity between all classrooms, so attending seminars and lectures will be easier. Through 5G technology, patients can connect with doctors and take their advice. Scientists are building smart medical devices which can help people with chronic medical conditions. The 5G network will boost the healthcare industry with smart devices, the internet of medical things, smart sensors, HD medical imaging technologies, and smart analytics systems. 5G will help access cloud storage, so accessing healthcare data will be very easy from any location worldwide. Doctors and medical practitioners can easily store and share large files like MRI reports within seconds using the 5G network.
• Satellite Internet: In many remote areas, ground base stations are not available, so 5G will play a crucial role in providing connectivity in such areas. The 5G network will provide connectivity using satellite systems, and the satellite system uses a constellation of multiple small satellites to provide connectivity in urban and rural areas across the world.

4. 5G Technologies

This section describes recent advances of 5G Massive MIMO, 5G NOMA, 5G millimeter wave, 5G IOT, 5G with machine learning, and 5G optimization-based approaches. In addition, the summary is also presented in each subsection that paves the researchers for the future research direction.

4.1. 5G Massive MIMO

Multiple-input-multiple-out (MIMO) is a very important technology for wireless systems. It is used for sending and receiving multiple signals simultaneously over the same radio channel. MIMO plays a very big role in WI-FI, 3G, 4G, and 4G LTE-A networks. MIMO is mainly used to achieve high spectral efficiency and energy efficiency but it was not up to the mark MIMO provides low throughput and very low reliable connectivity. To resolve this, lots of MIMO technology like single user MIMO (SU-MIMO), multiuser MIMO (MU-MIMO) and network MIMO were used. However, these new MIMO also did not still fulfill the demand of end users. Massive MIMO is an advancement of MIMO technology used in the 5G network in which hundreds and thousands of antennas are attached with base stations to increase throughput and spectral efficiency. Multiple transmit and receive antennas are used in massive MIMO to increase the transmission rate and spectral efficiency. When multiple UEs generate downlink traffic simultaneously, massive MIMO gains higher capacity. Massive MIMO uses extra antennas to move energy into smaller regions of space to increase spectral efficiency and throughput [ 43 ]. In traditional systems data collection from smart sensors is a complex task as it increases latency, reduced data rate and reduced reliability. While massive MIMO with beamforming and huge multiplexing techniques can sense data from different sensors with low latency, high data rate and higher reliability. Massive MIMO will help in transmitting the data in real-time collected from different sensors to central monitoring locations for smart sensor applications like self-driving cars, healthcare centers, smart grids, smart cities, smart highways, smart homes, and smart enterprises [ 44 ].

Highlights of 5G Massive MIMO technology are as follows:

• Data rate: Massive MIMO is advised as the one of the dominant technologies to provide wireless high speed and high data rate in the gigabits per seconds.
• The relationship between wave frequency and antenna size: Both are inversely proportional to each other. It means lower frequency signals need a bigger antenna and vise versa.

Pictorial representation of multi-input and multi-output (MIMO).

• MIMO role in 5G: Massive MIMO will play a crucial role in the deployment of future 5G mobile communication as greater spectral and energy efficiency could be enabled.

State-of-the-Art Approaches

Plenty of approaches were proposed to resolve the issues of conventional MIMO [ 7 ].

The MIMO multirate, feed-forward controller is suggested by Mae et al. [ 46 ]. In the simulation, the proposed model generates the smooth control input, unlike the conventional MIMO, which generates oscillated control inputs. It also outperformed concerning the error rate. However, a combination of multirate and single rate can be used for better results.

The performance of stand-alone MIMO, distributed MIMO with and without corporation MIMO, was investigated by Panzner et al. [ 47 ]. In addition, an idea about the integration of large scale in the 5G technology was also presented. In the experimental analysis, different MIMO configurations are considered. The variation in the ratio of overall transmit antennas to spatial is deemed step-wise from equality to ten.

The simulation of massive MIMO noncooperative and cooperative systems for down-link behavior was performed by He et al. [ 48 ]. It depends on present LTE systems, which deal with various antennas in the base station set-up. It was observed that collaboration in different BS improves the system behaviors, whereas throughput is reduced slightly in this approach. However, a new method can be developed which can enhance both system behavior and throughput.

In [ 8 ], different approaches that increased the energy efficiency benefits provided by massive MIMO were presented. They analyzed the massive MIMO technology and described the detailed design of the energy consumption model for massive MIMO systems. This article has explored several techniques to enhance massive MIMO systems’ energy efficiency (EE) gains. This paper reviews standard EE-maximization approaches for the conventional massive MIMO systems, namely, scaling number of antennas, real-time implementing low-complexity operations at the base station (BS), power amplifier losses minimization, and radio frequency (RF) chain minimization requirements. In addition, open research direction is also identified.

In [ 49 ], various existing approaches based on different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling methods adopted in massive MIMO systems are presented in this paper. The objective of this survey article was to make awareness about the current research and future research direction in MIMO for systems. They analyzed that complete utilization of resources and bandwidth was the most crucial factor which enhances the sum rate.

In [ 50 ], authors discussed the development of various techniques for pilot contamination. To calculate the impact of pilot contamination in time division duplex (TDD) massive MIMO system, TDD and frequency division duplexing FDD patterns in massive MIMO techniques are used. They discussed different issues in pilot contamination in TDD massive MIMO systems with all the possible future directions of research. They also classified various techniques to generate the channel information for both pilot-based and subspace-based approaches.

In [ 19 ], the authors defined the uplink and downlink services for a massive MIMO system. In addition, it maintains a performance matrix that measures the impact of pilot contamination on different performances. They also examined the various application of massive MIMO such as small cells, orthogonal frequency-division multiplexing (OFDM) schemes, massive MIMO IEEE 802, 3rd generation partnership project (3GPP) specifications, and higher frequency bands. They considered their research work crucial for cutting edge massive MIMO and covered many issues like system throughput performance and channel state acquisition at higher frequencies.

In [ 13 ], various approaches were suggested for MIMO future generation wireless communication. They made a comparative study based on performance indicators such as peak data rate, energy efficiency, latency, throughput, etc. The key findings of this survey are as follows: (1) spatial multiplexing improves the energy efficiency; (2) design of MIMO play a vital role in the enhancement of throughput; (3) enhancement of mMIMO focusing on energy & spectral performance; (4) discussed the future challenges to improve the system design.

In [ 51 ], the study of large-scale MIMO systems for an energy-efficient system sharing method was presented. For the resource allocation, circuit energy and transmit energy expenditures were taken into consideration. In addition, the optimization techniques were applied for an energy-efficient resource sharing system to enlarge the energy efficiency for individual QoS and energy constraints. The author also examined the BS configuration, which includes homogeneous and heterogeneous UEs. While simulating, they discussed that the total number of transmit antennas plays a vital role in boosting energy efficiency. They highlighted that the highest energy efficiency was obtained when the BS was set up with 100 antennas that serve 20 UEs.

This section includes various works done on 5G MIMO technology by different author’s. Table 5 shows how different author’s worked on improvement of various parameters such as throughput, latency, energy efficiency, and spectral efficiency with 5G MIMO technology.

Summary of massive MIMO-based approaches in 5G technology.

4.2. 5G Non-Orthogonal Multiple Access (NOMA)

NOMA is a very important radio access technology used in next generation wireless communication. Compared to previous orthogonal multiple access techniques, NOMA offers lots of benefits like high spectrum efficiency, low latency with high reliability and high speed massive connectivity. NOMA mainly works on a baseline to serve multiple users with the same resources in terms of time, space and frequency. NOMA is mainly divided into two main categories one is code domain NOMA and another is power domain NOMA. Code-domain NOMA can improve the spectral efficiency of mMIMO, which improves the connectivity in 5G wireless communication. Code-domain NOMA was divided into some more multiple access techniques like sparse code multiple access, lattice-partition multiple access, multi-user shared access and pattern-division multiple access [ 52 ]. Power-domain NOMA is widely used in 5G wireless networks as it performs well with various wireless communication techniques such as MIMO, beamforming, space-time coding, network coding, full-duplex and cooperative communication etc. [ 53 ]. The conventional orthogonal frequency-division multiple access (OFDMA) used by 3GPP in 4G LTE network provides very low spectral efficiency when bandwidth resources are allocated to users with low channel state information (CSI). NOMA resolved this issue as it enables users to access all the subcarrier channels so bandwidth resources allocated to the users with low CSI can still be accessed by the users with strong CSI which increases the spectral efficiency. The 5G network will support heterogeneous architecture in which small cell and macro base stations work for spectrum sharing. NOMA is a key technology of the 5G wireless system which is very helpful for heterogeneous networks as multiple users can share their data in a small cell using the NOMA principle.The NOMA is helpful in various applications like ultra-dense networks (UDN), machine to machine (M2M) communication and massive machine type communication (mMTC). As NOMA provides lots of features it has some challenges too such as NOMA needs huge computational power for a large number of users at high data rates to run the SIC algorithms. Second, when users are moving from the networks, to manage power allocation optimization is a challenging task for NOMA [ 54 ]. Hybrid NOMA (HNOMA) is a combination of power-domain and code-domain NOMA. HNOMA uses both power differences and orthogonal resources for transmission among multiple users. As HNOMA is using both power-domain NOMA and code-domain NOMA it can achieve higher spectral efficiency than Power-domain NOMA and code-domain NOMA. In HNOMA multiple groups can simultaneously transmit signals at the same time. It uses a message passing algorithm (MPA) and successive interference cancellation (SIC)-based detection at the base station for these groups [ 55 ].

Highlights of 5G NOMA technology as follows:

Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).

• NOMA provides higher data rates and resolves all the loop holes of OMA that makes 5G mobile network more scalable and reliable.
• As multiple users use same frequency band simultaneously it increases the performance of whole network.
• To setup intracell and intercell interference NOMA provides nonorthogonal transmission on the transmitter end.
• The primary fundamental of NOMA is to improve the spectrum efficiency by strengthening the ramification of receiver.

State-of-the-Art of Approaches

A plenty of approaches were developed to address the various issues in NOMA.

A novel approach to address the multiple receiving signals at the same frequency is proposed in [ 22 ]. In NOMA, multiple users use the same sub-carrier, which improves the fairness and throughput of the system. As a nonorthogonal method is used among multiple users, at the time of retrieving the user’s signal at the receiver’s end, joint processing is required. They proposed solutions to optimize the receiver and the radio resource allocation of uplink NOMA. Firstly, the authors proposed an iterative MUDD which utilizes the information produced by the channel decoder to improve the performance of the multiuser detector. After that, the author suggested a power allocation and novel subcarrier that enhances the users’ weighted sum rate for the NOMA scheme. Their proposed model showed that NOMA performed well as compared to OFDM in terms of fairness and efficiency.

In [ 53 ], the author’s reviewed a power-domain NOMA that uses superposition coding (SC) and successive interference cancellation (SIC) at the transmitter and the receiver end. Lots of analyses were held that described that NOMA effectively satisfies user data rate demands and network-level of 5G technologies. The paper presented a complete review of recent advances in the 5G NOMA system. It showed the comparative analysis regarding allocation procedures, user fairness, state-of-the-art efficiency evaluation, user pairing pattern, etc. The study also analyzes NOMA’s behavior when working with other wireless communication techniques, namely, beamforming, MIMO, cooperative connections, network, space-time coding, etc.

In [ 9 ], the authors proposed NOMA with MEC, which improves the QoS as well as reduces the latency of the 5G wireless network. This model increases the uplink NOMA by decreasing the user’s uplink energy consumption. They formulated an optimized NOMA framework that reduces the energy consumption of MEC by using computing and communication resource allocation, user clustering, and transmit powers.

In [ 10 ], the authors proposed a model which investigates outage probability under average channel state information CSI and data rate in full CSI to resolve the problem of optimal power allocation, which increase the NOMA downlink system among users. They developed simple low-complexity algorithms to provide the optimal solution. The obtained simulation results showed NOMA’s efficiency, achieving higher performance fairness compared to the TDMA configurations. It was observed from the results that NOMA, through the appropriate power amplifiers (PA), ensures the high-performance fairness requirement for the future 5G wireless communication networks.

In [ 56 ], researchers discussed that the NOMA technology and waveform modulation techniques had been used in the 5G mobile network. Therefore, this research gave a detailed survey of non-orthogonal waveform modulation techniques and NOMA schemes for next-generation mobile networks. By analyzing and comparing multiple access technologies, they considered the future evolution of these technologies for 5G mobile communication.

In [ 57 ], the authors surveyed non-orthogonal multiple access (NOMA) from the development phase to the recent developments. They have also compared NOMA techniques with traditional OMA techniques concerning information theory. The author discussed the NOMA schemes categorically as power and code domain, including the design principles, operating principles, and features. Comparison is based upon the system’s performance, spectral efficiency, and the receiver’s complexity. Also discussed are the future challenges, open issues, and their expectations of NOMA and how it will support the key requirements of 5G mobile communication systems with massive connectivity and low latency.

In [ 17 ], authors present the first review of an elementary NOMA model with two users, which clarify its central precepts. After that, a general design with multicarrier supports with a random number of users on each sub-carrier is analyzed. In performance evaluation with the existing approaches, resource sharing and multiple-input multiple-output NOMA are examined. Furthermore, they took the key elements of NOMA and its potential research demands. Finally, they reviewed the two-user SC-NOMA design and a multi-user MC-NOMA design to highlight NOMA’s basic approaches and conventions. They also present the research study about the performance examination, resource assignment, and MIMO in NOMA.

In this section, various works by different authors done on 5G NOMA technology is covered. Table 6 shows how other authors worked on the improvement of various parameters such as spectral efficiency, fairness, and computing capacity with 5G NOMA technology.

Summary of NOMA-based approaches in 5G technology.

4.3. 5G Millimeter Wave (mmWave)

Millimeter wave is an extremely high frequency band, which is very useful for 5G wireless networks. MmWave uses 30 GHz to 300 GHz spectrum band for transmission. The frequency band between 30 GHz to 300 GHz is known as mmWave because these waves have wavelengths between 1 to 10 mm. Till now radar systems and satellites are only using mmWave as these are very fast frequency bands which provide very high speed wireless communication. Many mobile network providers also started mmWave for transmitting data between base stations. Using two ways the speed of data transmission can be improved one is by increasing spectrum utilization and second is by increasing spectrum bandwidth. Out of these two approaches increasing bandwidth is quite easy and better. The frequency band below 5 GHz is very crowded as many technologies are using it so to boost up the data transmission rate 5G wireless network uses mmWave technology which instead of increasing spectrum utilization, increases the spectrum bandwidth [ 58 ]. To maximize the signal bandwidth in wireless communication the carrier frequency should also be increased by 5% because the signal bandwidth is directly proportional to carrier frequencies. The frequency band between 28 GHz to 60 GHz is very useful for 5G wireless communication as 28 GHz frequency band offers up to 1 GHz spectrum bandwidth and 60 GHz frequency band offers 2 GHz spectrum bandwidth. 4G LTE provides 2 GHz carrier frequency which offers only 100 MHz spectrum bandwidth. However, the use of mmWave increases the spectrum bandwidth 10 times, which leads to better transmission speeds [ 59 , 60 ].

Highlights of 5G mmWave are as follows:

Pictorial representation of millimeter wave.

• The 5G mmWave offer three advantages: (1) MmWave is very less used new Band, (2) MmWave signals carry more data than lower frequency wave, and (3) MmWave can be incorporated with MIMO antenna with the potential to offer a higher magnitude capacity compared to current communication systems.

In [ 11 ], the authors presented the survey of mmWave communications for 5G. The advantage of mmWave communications is adaptability, i.e., it supports the architectures and protocols up-gradation, which consists of integrated circuits, systems, etc. The authors over-viewed the present solutions and examined them concerning effectiveness, performance, and complexity. They also discussed the open research issues of mmWave communications in 5G concerning the software-defined network (SDN) architecture, network state information, efficient regulation techniques, and the heterogeneous system.

In [ 61 ], the authors present the recent work done by investigators in 5G; they discussed the design issues and demands of mmWave 5G antennas for cellular handsets. After that, they designed a small size and low-profile 60 GHz array of antenna units that contain 3D planer mesh-grid antenna elements. For the future prospect, a framework is designed in which antenna components are used to operate cellular handsets on mmWave 5G smartphones. In addition, they cross-checked the mesh-grid array of antennas with the polarized beam for upcoming hardware challenges.

In [ 12 ], the authors considered the suitability of the mmWave band for 5G cellular systems. They suggested a resource allocation system for concurrent D2D communications in mmWave 5G cellular systems, and it improves network efficiency and maintains network connectivity. This research article can serve as guidance for simulating D2D communications in mmWave 5G cellular systems. Massive mmWave BS may be set up to obtain a high delivery rate and aggregate efficiency. Therefore, many wireless users can hand off frequently between the mmWave base terminals, and it emerges the demand to search the neighbor having better network connectivity.

In [ 62 ], the authors provided a brief description of the cellular spectrum which ranges from 1 GHz to 3 GHz and is very crowed. In addition, they presented various noteworthy factors to set up mmWave communications in 5G, namely, channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous, and rain. In addition, hybrid beamforming architecture in the mmWave technique is analyzed. They also suggested methods for the blockage effect in mmWave communications due to penetration damage. Finally, the authors have studied designing the mmWave transmission with small beams in nonorthogonal device-to-device communication.

This section covered various works done on 5G mmWave technology. The Table 7 shows how different author’s worked on the improvement of various parameters i.e., transmission rate, coverage, and cost, with 5G mmWave technology.

Summary of existing mmWave-based approaches in 5G technology.

4.4. 5G IoT Based Approaches

The 5G mobile network plays a big role in developing the Internet of Things (IoT). IoT will connect lots of things with the internet like appliances, sensors, devices, objects, and applications. These applications will collect lots of data from different devices and sensors. 5G will provide very high speed internet connectivity for data collection, transmission, control, and processing. 5G is a flexible network with unused spectrum availability and it offers very low cost deployment that is why it is the most efficient technology for IoT [ 63 ]. In many areas, 5G provides benefits to IoT, and below are some examples:

Smart homes: smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network, as it offers very high speed low latency communication.

Smart cities: 5G wireless network also helps in developing smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.

Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance and logistics. 5G smart sensor technology also offers smarter, safer, cost effective, and energy-saving industrial operation for industrial IoT.

Smart Farming: 5G technology will play a crucial role for agriculture and smart farming. 5G sensors and GPS technology will help farmers to track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation control, pest control, insect control, and electricity control.

Autonomous Driving: 5G wireless network offers very low latency high speed communication which is very significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is important for autonomous vehicles, decision taking is performed in microseconds to avoid accidents [ 64 ].

Highlights of 5G IoT are as follows:

Pictorial representation of IoT with 5G.

• 5G with IoT is a new feature of next-generation mobile communication, which provides a high-speed internet connection between moderated devices. 5G IoT also offers smart homes, smart devices, sensors, smart transportation systems, smart industries, etc., for end-users to make them smarter.
• IoT deals with moderate devices which connect through the internet. The approach of the IoT has made the consideration of the research associated with the outcome of providing wearable, smart-phones, sensors, smart transportation systems, smart devices, washing machines, tablets, etc., and these diverse systems are associated to a common interface with the intelligence to connect.
• Significant IoT applications include private healthcare systems, traffic management, industrial management, and tactile internet, etc.

Plenty of approaches is devised to address the issues of IoT [ 14 , 65 , 66 ].

In [ 65 ], the paper focuses on 5G mobile systems due to the emerging trends and developing technologies, which results in the exponential traffic growth in IoT. The author surveyed the challenges and demands during deployment of the massive IoT applications with the main focus on mobile networking. The author reviewed the features of standard IoT infrastructure, along with the cellular-based, low-power wide-area technologies (LPWA) such as eMTC, extended coverage (EC)-GSM-IoT, as well as noncellular, low-power wide-area (LPWA) technologies such as SigFox, LoRa etc.

In [ 14 ], the authors presented how 5G technology copes with the various issues of IoT today. It provides a brief review of existing and forming 5G architectures. The survey indicates the role of 5G in the foundation of the IoT ecosystem. IoT and 5G can easily combine with improved wireless technologies to set up the same ecosystem that can fulfill the current requirement for IoT devices. 5G can alter nature and will help to expand the development of IoT devices. As the process of 5G unfolds, global associations will find essentials for setting up a cross-industry engagement in determining and enlarging the 5G system.

In [ 66 ], the author introduced an IoT authentication scheme in a 5G network, with more excellent reliability and dynamic. The scheme proposed a privacy-protected procedure for selecting slices; it provided an additional fog node for proper data transmission and service types of the subscribers, along with service-oriented authentication and key understanding to maintain the secrecy, precision of users, and confidentiality of service factors. Users anonymously identify the IoT servers and develop a vital channel for service accessibility and data cached on local fog nodes and remote IoT servers. The author performed a simulation to manifest the security and privacy preservation of the user over the network.

This section covered various works done on 5G IoT by multiple authors. Table 8 shows how different author’s worked on the improvement of numerous parameters, i.e., data rate, security requirement, and performance with 5G IoT.

Summary of IoT-based approaches in 5G technology.

4.5. Machine Learning Techniques for 5G

Various machine learning (ML) techniques were applied in 5G networks and mobile communication. It provides a solution to multiple complex problems, which requires a lot of hand-tuning. ML techniques can be broadly classified as supervised, unsupervised, and reinforcement learning. Let’s discuss each learning technique separately and where it impacts the 5G network.

Supervised Learning, where user works with labeled data; some 5G network problems can be further categorized as classification and regression problems. Some regression problems such as scheduling nodes in 5G and energy availability can be predicted using Linear Regression (LR) algorithm. To accurately predict the bandwidth and frequency allocation Statistical Logistic Regression (SLR) is applied. Some supervised classifiers are applied to predict the network demand and allocate network resources based on the connectivity performance; it signifies the topology setup and bit rates. Support Vector Machine (SVM) and NN-based approximation algorithms are used for channel learning based on observable channel state information. Deep Neural Network (DNN) is also employed to extract solutions for predicting beamforming vectors at the BS’s by taking mapping functions and uplink pilot signals into considerations.

In unsupervised Learning, where the user works with unlabeled data, various clustering techniques are applied to enhance network performance and connectivity without interruptions. K-means clustering reduces the data travel by storing data centers content into clusters. It optimizes the handover estimation based on mobility pattern and selection of relay nodes in the V2V network. Hierarchical clustering reduces network failure by detecting the intrusion in the mobile wireless network; unsupervised soft clustering helps in reducing latency by clustering fog nodes. The nonparametric Bayesian unsupervised learning technique reduces traffic in the network by actively serving the user’s requests and demands. Other unsupervised learning techniques such as Adversarial Auto Encoders (AAE) and Affinity Propagation Clustering techniques detect irregular behavior in the wireless spectrum and manage resources for ultradense small cells, respectively.

In case of an uncertain environment in the 5G wireless network, reinforcement learning (RL) techniques are employed to solve some problems. Actor-critic reinforcement learning is used for user scheduling and resource allocation in the network. Markov decision process (MDP) and Partially Observable MDP (POMDP) is used for Quality of Experience (QoE)-based handover decision-making for Hetnets. Controls packet call admission in HetNets and channel access process for secondary users in a Cognitive Radio Network (CRN). Deep RL is applied to decide the communication channel and mobility and speeds up the secondary user’s learning rate using an antijamming strategy. Deep RL is employed in various 5G network application parameters such as resource allocation and security [ 67 ]. Table 9 shows the state-of-the-art ML-based solution for 5G network.

The state-of-the-art ML-based solution for 5G network.

Highlights of machine learning techniques for 5G are as follows:

Pictorial representation of machine learning (ML) in 5G.

• In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.
• ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices.

In [ 79 ], author’s firstly describes the demands for the traditional authentication procedures and benefits of intelligent authentication. The intelligent authentication method was established to improve security practice in 5G-and-beyond wireless communication systems. Thereafter, the machine learning paradigms for intelligent authentication were organized into parametric and non-parametric research methods, as well as supervised, unsupervised, and reinforcement learning approaches. As a outcome, machine learning techniques provide a new paradigm into authentication under diverse network conditions and unstable dynamics. In addition, prompt intelligence to the security management to obtain cost-effective, better reliable, model-free, continuous, and situation-aware authentication.

In [ 68 ], the authors proposed a machine learning-based model to predict the traffic load at a particular location. They used a mobile network traffic dataset to train a model that can calculate the total number of user requests at a time. To launch access and mobility management function (AMF) instances according to the requirement as there were no predictions of user request the performance automatically degrade as AMF does not handle these requests at a time. Earlier threshold-based techniques were used to predict the traffic load, but that approach took too much time; therefore, the authors proposed RNN algorithm-based ML to predict the traffic load, which gives efficient results.

In [ 15 ], authors discussed the issue of network slice admission, resource allocation among subscribers, and how to maximize the profit of infrastructure providers. The author proposed a network slice admission control algorithm based on SMDP (decision-making process) that guarantees the subscribers’ best acceptance policies and satisfiability (tenants). They also suggested novel N3AC, a neural network-based algorithm that optimizes performance under various configurations, significantly outperforms practical and straightforward approaches.

This section includes various works done on 5G ML by different authors. Table 10 shows the state-of-the-art work on the improvement of various parameters such as energy efficiency, Quality of Services (QoS), and latency with 5G ML.

The state-of-the-art ML-based approaches in 5G technology.

4.6. Optimization Techniques for 5G

Optimization techniques may be applied to capture NP-Complete or NP-Hard problems in 5G technology. This section briefly describes various research works suggested for 5G technology based on optimization techniques.

In [ 80 ], Massive MIMO technology is used in 5G mobile network to make it more flexible and scalable. The MIMO implementation in 5G needs a significant number of radio frequencies is required in the RF circuit that increases the cost and energy consumption of the 5G network. This paper provides a solution that increases the cost efficiency and energy efficiency with many radio frequency chains for a 5G wireless communication network. They give an optimized energy efficient technique for MIMO antenna and mmWave technologies based 5G mobile communication network. The proposed Energy Efficient Hybrid Precoding (EEHP) algorithm to increase the energy efficiency for the 5G wireless network. This algorithm minimizes the cost of an RF circuit with a large number of RF chains.

In [ 16 ], authors have discussed the growing demand for energy efficiency in the next-generation networks. In the last decade, they have figured out the things in wireless transmissions, which proved a change towards pursuing green communication for the next generation system. The importance of adopting the correct EE metric was also reviewed. Further, they worked through the different approaches that can be applied in the future for increasing the network’s energy and posed a summary of the work that was completed previously to enhance the energy productivity of the network using these capabilities. A system design for EE development using relay selection was also characterized, along with an observation of distinct algorithms applied for EE in relay-based ecosystems.

In [ 81 ], authors presented how AI-based approach is used to the setup of Self Organizing Network (SON) functionalities for radio access network (RAN) design and optimization. They used a machine learning approach to predict the results for 5G SON functionalities. Firstly, the input was taken from various sources; then, prediction and clustering-based machine learning models were applied to produce the results. Multiple AI-based devices were used to extract the knowledge analysis to execute SON functionalities smoothly. Based on results, they tested how self-optimization, self-testing, and self-designing are done for SON. The author also describes how the proposed mechanism classifies in different orders.

In [ 82 ], investigators examined the working of OFDM in various channel environments. They also figured out the changes in frame duration of the 5G TDD frame design. Subcarrier spacing is beneficial to obtain a small frame length with control overhead. They provided various techniques to reduce the growing guard period (GP) and cyclic prefix (CP) like complete utilization of multiple subcarrier spacing, management and data parts of frame at receiver end, various uses of timing advance (TA) or total control of flexible CP size.

This section includes various works that were done on 5G optimization by different authors. Table 11 shows how other authors worked on the improvement of multiple parameters such as energy efficiency, power optimization, and latency with 5G optimization.

Summary of Optimization Based Approaches in 5G Technology.

5. Description of Novel 5G Features over 4G

This section presents descriptions of various novel features of 5G, namely, the concept of small cell, beamforming, and MEC.

5.1. Small Cell

Small cells are low-powered cellular radio access nodes which work in the range of 10 meters to a few kilometers. Small cells play a very important role in implementation of the 5G wireless network. Small cells are low power base stations which cover small areas. Small cells are quite similar with all the previous cells used in various wireless networks. However, these cells have some advantages like they can work with low power and they are also capable of working with high data rates. Small cells help in rollout of 5G network with ultra high speed and low latency communication. Small cells in the 5G network use some new technologies like MIMO, beamforming, and mmWave for high speed data transmission. The design of small cells hardware is very simple so its implementation is quite easier and faster. There are three types of small cell tower available in the market. Femtocells, picocells, and microcells [ 83 ]. As shown in the Table 12 .

Types of Small cells.

MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.

Pictorial representation of communication with and without small cells.

5.2. Beamforming

Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].

Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.

Pictorial Representation of communication with and without using beamforming.

5.3. Mobile Edge Computing

Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .

Pictorial representation of cloud computing vs. mobile edge computing.

6. 5G Security

Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].

AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].

Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].

Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].

Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].

7. Summary of 5G Technology Based on Above-Stated Challenges

In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.

Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).

8. Conclusions

This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.

9. Future Findings

This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.

Acknowledgments

Author contributions.

Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.

This paper was supported by Soonchunhyang University.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Article Contents

Background and theory, predictions and empirical overview, general discussion, data collection information.

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The Smartphone as a Pacifying Technology

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Shiri Melumad, Michel Tuan Pham, The Smartphone as a Pacifying Technology, Journal of Consumer Research , Volume 47, Issue 2, August 2020, Pages 237–255, https://doi.org/10.1093/jcr/ucaa005

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In light of consumers’ growing dependence on their smartphones, this article investigates the nature of the relationship that consumers form with their smartphone and its underlying mechanisms. We propose that in addition to obvious functional benefits, consumers in fact derive emotional benefits from their smartphone—in particular, feelings of psychological comfort and, if needed, actual stress relief. In other words, in a sense, smartphones are not unlike adult pacifiers. This psychological comfort arises from a unique combination of properties that turn smartphones into a reassuring presence for their owners: the portability of the device, its personal nature, the subjective sense of privacy experienced while on the device, and the haptic gratification it affords. Results from one large-scale field study and three laboratory experiments support the proposed underlying mechanisms and document downstream consequences of the psychological comfort that smartphones provide. The findings show, for example, that (a) in moments of stress, consumers exhibit a greater tendency to seek out their smartphone (study 2); and (b) engaging with one’s smartphone provides greater stress relief than engaging in the same activity with a comparable device such as one’s laptop (study 3) or a similar smartphone belonging to someone else (study 4).

Arguably, no recent technological innovation has had a more transformative effect on consumers’ lives than the virtually indispensable smartphone. Eighty-one percent of adult Americans own the device ( Pew Research Center 2019 ), with one-third of all consumer purchases—over $1 trillion—now occurring on mobile platforms ( Wu 2018 ). Virtually everywhere, whether on public transit, at dinner, in bed, or even while crossing the street, consumers can be found engrossed in their devices, calling or texting friends, listening to music, or viewing the latest content posted on social media. Indeed, the extent of smartphone usage has become so immense that one-half of owners describe their device as something that they “could not live without” ( Perrin 2017 ).

In spite of the central role that these devices play in the consumption economy, one question has received surprisingly little attention in consumer research: What is the nature of consumers’ relationship to their smartphone? The purpose of this article is to explore this issue by shedding new light on the characteristics and underpinnings of this relationship. Drawing on results from a large field study and three controlled laboratory experiments, we offer evidence that consumers are drawn to their smartphones not just because of the immense array of practical benefits they provide, but also because of a deeper emotional benefit: smartphones can serve as a source of psychological comfort for their owners. In a sense, one’s smartphone is not unlike an adult pacifier.

Consistent with this general proposition, we show that consumers are especially drawn to their smartphone in moments of stress, and that once engaged with, smartphones are sufficiently comforting to alleviate the stress. Moreover, this effect is specific to feelings of comfort in particular—not just any type of positive affect. We also offer findings that shed light on the drivers of this relationship, showing that smartphones are particularly comforting because of a unique combination of properties: (a) they are highly personal objects; (b) they are highly portable; (c) they provide a private space where users can escape their external environment; (d) they possess haptic properties that consumers find pleasurable—all of which allow phones to (e) provide a reassuring presence for owners. The sense of reassurance afforded by one’s phone, in turn, enables the device to act as a general source of psychological comfort.

We divide our presentation into four sections. We begin by reviewing prior work on which we base our predictions and propose a theoretical account of how the unique combination of physical and functional properties available on smartphones allows the device to serve as a source of psychological comfort for owners. We then report the results of four studies that test our hypotheses. We conclude by discussing the implications of our findings for consumer welfare, marketers, and the broader study of consumer product attachment.

How Do We Relate to Our Smartphones?

In recent years, an emerging body of academic literature—and much popular press—has discussed the relationship that people seem to develop with their smartphone ( Alter 2017 ; Fullwood et al. 2017 ; Melumad, Inman, and Pham 2019 ; Wilmer, Sherman, and Chein 2017 ). Perhaps the most common account of this relationship is that it resembles a behavioral addiction ( Alter 2017 ; Bernroider, Krumay, and Margiol 2014 ; De-Sola Gutiérrez, Rodríguez de Fonseca, and Rubio 2016 ; Grant et al. 2010 ; Roberts, Pullig, and Manolis 2015 )—a compulsive desire to engage in a behavior despite the risks of social, physical, or financial harm that it might impose ( Albrecht, Kirschner, and Grüsser 2007 ). As an illustration of this, prior work shows that respondents report a variety of problematic behaviors with their smartphone, such as use of the device that hinders productivity (e.g., using one’s phone at work), the degradation of interpersonal interactions (e.g., using one’s phone at dinner with a friend), or a generally unsafe style of usage (e.g., texting while driving; Bianchi and Phillips 2005 ; Vahedi and Saiphoo 2017 ; Yen et al. 2009 ). Relatedly, in one of the only studies of smartphone use in consumer research, Ward et al. (2017) found that participants restricted from their smartphones experienced cognitive load and consequently demonstrated impaired performance on a cognitive task. Likewise, research outside marketing consistently shows that people experience heightened anxiety and stress when restricted from interacting with their phones ( Cheever et al. 2014 ; Clayton, Leshner, and Almond 2015 ; Hunter et al. 2018 ; Panova and Lleras 2016 ).

While research on cellphone addiction has been useful in documenting the apparent dependency of some consumers on the device, it is important to note that excessive smartphone use is not recognized as a clinical form of addiction according to the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) , and debate exists over whether it could be clinically characterized as such ( Panova and Carbonell 2018 ). More importantly, while extant research describes the class of behaviors some users demonstrate with their phone, it is relatively silent on the psychological mechanisms that give rise to this dependence. To the degree that such origins have been explored, the focus has been to examine covariation between personality types and reliance on specific functional applications of the devices—such as how extraversion versus introversion relates to users’ dependence on text messaging ( Bianchi and Phillips 2005 ; Igarashi et al. 2008 ), social media ( van Koningsbruggen et al. 2017 ), and gaming ( Cole and Hooley 2013 ). In fact, an important feature of this work is the assumption that there is nothing unique about smartphones per se that make them addictive; rather, the so-called addiction is thought to arise from the specific functionalities the phones provide (e.g., access to social media), not the device itself. Hence, in principle, this account would predict that the same “addictive” needs could be satisfied by any device offering similar functionalities—whether it be a laptop, tablet, or smartphone—and regardless of whether one owns the device or it belongs to someone else.

While functionalities undoubtedly play an important role in explaining why consumers form attachments to their smartphone, in this research we argue that there is a deeper psychological explanation for this special relationship. We propose that consumers are drawn to their phone because it offers a unique combination of functional, haptic, and personal ownership benefits that allow the device to serve as a source of psychological comfort. Thus, while the very notion of “smartphone addiction” frames the relationship that people have with their phone in an exclusively negative light, here we argue that consumers also derive emotional and psychological benefits from use of their device.

Smartphones as a Source of Psychological Comfort

The idea that individuals can develop deep emotional bonds with material objects has a long tradition in both consumer research and psychology ( Ball and Tasaki 1992 ; Belk 1988 ; Nedelisky and Steele 2009 ; Schifferstein and Zwartkruis-Pelgrim 2008 ). For example, young children often develop ties to transitional or attachment objects, such as blankets and teddy bears, that give them comfort in moments of stress and produce separation anxiety when unavailable ( Passman 1977 ; Winnicott 1953 ). For children, these ties are presumed to have a developmental function, allowing the child to transition away from the comfort and security of the primary caregiver ( Bowlby 1969 ; Winnicott 1953 ). While the developmental drivers that give rise to childhood attachment objects are typically outgrown by early adolescence, adults too can develop emotional attachments to material objects that have similar behavioral earmarks as children’s attachments to transitional objects ( Bachar et al. 1998 ; Keefer, Landau, and Sullivan 2014 ). For example, prior work shows that most adults report having “special possessions” that are both highly cared for and provide feelings of warmth and security ( Schultz, Kleine, and Kernan 1989 ; Wallendorf and Arnould 1988 ).

Notably, even if the analogy is only paramorphic (see, e.g., Hoffman 1960 , for a discussion of the distinction between isomorphic and paramorphic representations of psychological processes), consumers’ smartphones possess properties that are parallel to those that characterize attachment objects for children. For one, much like a child’s attachment object is small and lightweight enough to be carried around for use across various contexts ( Lehman et al. 1992 ; Winnicott 1953 ), a smartphone is highly portable, enabling the owner to access its benefits virtually always. Attachment objects also tend to have a tactile quality, with their benefits primarily derived through physical touch—such as a child self-soothing by gripping and stroking a teddy bear ( Busch et al. 1973 ; Lehman et al. 1992 ). Similarly, most smartphones are ergonomically designed to enhance and facilitate the user’s tactile experience with the device ( Aquino 2016 ), and consumers must physically interact with their device through its touchscreen interface to access its benefits. In combination with the item exhibiting the key physical traits of an attachment object, the child must expect it to provide certain positive outcomes—a learned association that develops for fixed or constant objects that consistently or reliably provide a particular set of positive outcomes ( Cairns 1966 ). Consumers likewise come to expect their smartphone to deliver a specific combination of positive outcomes, such as social interaction with loved ones or informational updates, in an immediate and consistent fashion ( Aoki and Downes 2003 ; Oulasvirta et al. 2012 ). Finally, similar to the highly personal nature of attachment objects (e.g., children have their own security blanket, pacifier, or stuffed animal that is not to be shared with others), smartphones are also highly personal objects; for example, one’s phone is rarely shared with anyone else and is often highly customized (e.g., personalized case; unique set of apps).

The central thesis of this article is that smartphones are endowed with a unique combination of properties that lead them to be viewed not just as pragmatic tools, but also as sources of comfort for owners—not unlike pacifiers for children. This thesis, in turn, makes specific predictions about downstream consequences of using the device. For example, owners will show a heightened tendency to seek out and engage with the device in moments of stress; and, once engaged with, the device will provide relief from stress. This proposition is compatible with other phenomena documented with the device, such as the anxiety ( Cheever et al. 2014 ) and cognitive load ( Ward et al. 2017 ) that users experience when separated from their smartphone.

Figure 1 illustrates the hypothesized mechanisms by which smartphones come to provide psychological comfort to their owners. In particular, we argue that as a result of four particular properties of the device—its portability, associated sense of privacy, personal nature, and haptic benefits—smartphones provide a reassuring presence for their owners, which leads the device to serve as a source of psychological comfort. We review these properties in turn:

CONCEPTUAL MODEL: SMARTPHONES AS A SOURCE OF PSYCHOLOGICAL COMFORT

Smartphones are portable. An essential property of smartphones that allows them to be a reassuring presence for owners is their portability. Their inherently compact nature enables these devices to be carried around by owners practically everywhere and at all times. As a result, the vast array of functionalities available on the device—such as communication features, social media, entertainment, and news updates—can be accessed at virtually any time and place, making one’s device dependable and readily available.

Smartphones afford a sense of privacy. A second critical property of smartphones is the sense of privacy that users experience while engaging with the device. One’s smartphone creates a private space in which users can immerse themselves in activities of their choosing—not unlike a teenager retreating to her room to listen to music or an adult retreating to his “man cave” to play video games. This sense of privacy is reinforced by the small screen of these devices, which encourages users to immerse themselves in their device and away from their external environment. In addition, the relatively small screen of a smartphone makes users feel as though their activities are less observable to others around them. The idea that use of a smartphone provides a heightened sense of privacy is consistent with some authors’ conceptualization of mobile phones as a form of “refuge” ( Trub and Barbot 2016 ). It is also consistent with research showing that computer-mediated environments facilitate the disclosure of personal information by enhancing users’ sense of privacy ( Joinson 2001 ).

Smartphones are highly personal possessions . Smartphones have properties that make them highly personal objects for owners. For example, as mentioned above, today’s smartphones involve a great deal of customization (e.g., selected apps, organization of content, personalized cases) and are in many ways connected to a person’s identity: unlike landline numbers, cellphone numbers are typically linked to a single person, and the device typically contains highly personal content such as personal messages, cherished photos, and favorite songs. Further, many owners tend to use the device for very personal reasons such as communicating with family, checking private messages, and interacting with friends on social media. Moreover, its portability implies that owners carry the device around on their person throughout most of the day, and many even keep it close to their bedside at night—features that further enhance the personal nature of the device relative to other personal objects ( Fullwood et al. 2017 ).

Smartphones provide haptic benefits. Another defining feature is the ergonomic design that makes smartphones easy and pleasant to hold in one’s hands. Moreover, most interactions with the device occur through physically touching and swiping its touchscreen interface. Importantly, such haptic qualities have been shown to generate hedonic benefits in the form of comfort and pleasure ( Peck and Childers 2003 ; Shu and Peck 2011 ; Vaucelle, Bonanni, and Ishii 2009 ).

We propose that this unique combination of properties—the knowledge that, whenever and wherever they want, consumers can retreat to a “private space” that is highly personal and functional and even provides haptic pleasure—enables the device to serve as a reassuring presence for owners. The reassuring presence provided by one’s phone, in turn, leads the device to play a special role for owners: that of providing feelings of psychological comfort when needed. The goal of the empirical work that follows is to substantiate this general thesis, which yields three specific empirical predictions:

P1: The psychological comfort that consumers derive from their smartphone arises from a combination of four properties that render it a reassuring presence in their lives: (a) its highly personal nature, (b) its portability, (c) the sense of privacy it provides, and (d) its rich haptic qualities. P2: In moments of stress, consumers show an increased tendency to seek out and engage with their smartphone as a means of coping with their discomfort (even when other objects are at their disposal). P3: Because of the psychological comfort that smartphones provide, even brief engagement with one’s phone can afford relief from a stressful situation.   P3A: This effect is greater when using one’s smartphone than when using another personal device with comparable functionality: one’s laptop.   P3B: This effect is greater when using one’s smartphone than when using an otherwise similar smartphone belonging to someone else.

We test these predictions across four studies. In the first study we obtain correlational evidence for the hypothesized drivers of the enhanced psychological comfort associated with smartphones as well as its downstream consequences ( figure 1 ). We then report the results of three controlled experiments that demonstrate the palliative effects of using one’s smartphone, showing that the device is sought out in moments of stress (study 2) and that, once engaged with, the device indeed provides greater relief than comparable devices (studies 3 and 4). In web appendix 1 we additionally report the results of a fifth study that lend further support for our hypotheses in a real-world context of stress, showing that consumers who recently quit smoking rely more heavily on their smartphone as a substitute for the palliative effects afforded by cigarettes.

The purpose of this first study was twofold: (a) to test the mechanisms hypothesized to underlie the role of smartphones as sources of psychological comfort, and (b) to assess some downstream consequences of this psychological comfort. As explained earlier, we theorize that smartphones come to serve as a key source of psychological comfort for consumers as a result of four specific properties: smartphones (a) tend to be highly personal, (b) are very portable, (c) can provide a heightened sense of privacy, and (d) have rich haptic qualities. These properties combine to make smartphones a reassuring presence for owners, ultimately enabling the device to enhance feelings of psychological comfort when the consumer engages with it (prediction 1).

We also examined whether the extent to which smartphones provide psychological comfort varies across consumers. For example, consistent with our conceptualization (see prediction 2), the more external stress people experience in their lives, the more we expect them to rely on their phone as a source of stress relief. Demographic traits may also play a role: older consumers, for instance, may be less dependent on their phone as a source of comfort than younger consumers, since older individuals are more likely to have developed alternate means of coping with stress (prior to the introduction of the smartphone). Likewise, consumers who have fewer positive associations with their smartphone—such as those who primarily use the device for work—may derive less psychological comfort from it.

To test these predictions, 885 participants from the Amazon Mechanical Turk (MTurk) panel (46% female) were surveyed about their use of and attitudes toward their smartphone. In addition, to obtain a comparison baseline, a separate sample of 470 MTurk participants responded to the same survey but with the questions rephrased to refer to their primary PC (e.g., laptop). These different sample sizes were based on a priori power calculations that reflected the different types of analyses planned for the two samples: given that participants’ responses about their smartphone were the primary focus of interest (e.g., examining the relationship between smartphone usage and levels of daily stress), we sought a sample size that would be large enough to detect small effects ( d = .2 with 80% power at α = .05). In contrast, given that the PC sample was collected solely as a basis for simple contrasts with the smartphone sample, we anticipated larger effect sizes (e.g., d = .3–.5) for which a smaller sample size was required to achieve the same power.

The survey, reproduced in web appendix 2 , was composed of four sections designed to measure the theorized psychological constructs depicted in figure 1 , their downstream consequences, as well as possible correlates of the effects. Each set of measures will be reviewed in turn.

Main Dependent Measure

The degree to which smartphones provide psychological comfort to their owners was measured through five seven-point items such as “Using my smartphone provides a source of comfort” and “When using my smartphone I feel safe and secure” (1 = “Not at all” to 7 = “Very much so”; α = .94).

Antecedents of Psychological Comfort

The first proposed antecedent of psychological comfort—the reassuring presence afforded by one’s phone—was measured on a four-item scale with items such as “Whenever I need my phone I know it will be there for me” and “I think of my phone as a reliable companion” (on a scale of 1 = “Not at all” to 7 = “Very much so”; α = .88). This construct was expected to arise from four hypothesized properties of the device: its perceived portability, sense of privacy, personal nature, and haptic pleasure (all of which were measured on the same seven-point scale). The perceived portability of the device was measured as a five-item scale with items such as “It is easy to reach for my phone whenever I need it” and “Wherever I go, my phone goes” (α = .88). The sense of privacy afforded by the device was measured as a four-item scale with items such as “My phone enables me to retreat to my private space” and “When I use my phone I feel like I am in my own safe space” (α = .94). The extent to which the device has a personal nature was measured as a five-item scale with items such as “I think of my smartphone as a very personal object” and “I would feel uncomfortable if someone used my smartphone” (α = .85). Finally, the haptic pleasure derived from interacting with the device was measured as a four-item scale with items such as “I enjoy the physical feeling of touching or holding my phone” and “Touching or swiping my phone’s screen/keypad feels pleasant” (α = .95).

Downstream Consequence: Use of Phone as Relief from Stress

To examine a potential downstream consequence of the psychological comfort expected to arise from smartphones, participants were asked to answer four items assessing the degree to which they used their phone as a means of coping with different exogenous sources of stress on a scale of 1 = “Not at all” to 7 = “Very much so”: “Using my phone helps me escape my daily pressures,” “I often turn to my phone in a moment of stress or anxiety,” “If I am in an uncomfortable social situation I turn to my phone,” and “I use my phone as a way of comforting myself when I feel stressed” (“stress relief” index; α = .91).

Individual Differences

To measure the extent to which usage contexts predicted the degree of comfort associated with one’s phone, participants were asked to indicate the degree to which they relied on their phone for social, entertainment, and work-related purposes. We theorized that consumers who use their phones more for “hedonic” purposes, such as communicating with friends/family and entertainment, would generally derive greater comfort from their smartphone than those who use it for more “utilitarian” reasons—namely, work-related purposes. We were also interested in whether the extent to which one’s phone is used to alleviate stress differs across different types of stress—specifically personal stresses (e.g., breakups, loneliness) and stress from work-related problems (e.g., meeting a late-night deadline). We therefore asked participants to rate the extent to which they were subject to different types of external stresses including health, financial, family, and work-related stress. Items related to the first three domains were averaged to form an index of “personal stress” (α = .82), and items related to the latter domain were averaged into an index of “work-related stress” (α = .65). Finally, participants were asked a number of demographic questions including their age and gender, as well as general device usage questions such as length of ownership and estimated hours of use per day.

Results and Discussion

We report and discuss this study’s findings in three stages. We begin by offering model-free evidence of the degree to which participants perceive their phone as a source of comfort, and the extent to which they report using it as a means of relieving stress. We then report the results of two structural equation models that test our theoretical predictions. The first tests our central hypothesis about the antecedents of the psychological comfort provided by smartphones as well as a key downstream consequence: the use of one’s phone for stress relief. The second model examines how the degree of comfort derived from one’s phone and its use for stress relief covaries with individual-difference factors, such as the degree of daily stress faced by owners and contexts in which the device is used (work vs. personal). A correlation matrix of all variables used in the analysis is reported in web appendix 3 .

Model-Free Evidence: Do Consumers Derive Comfort from Their Smartphone?

As theorized, participants indicated that their smartphone serves as a source of psychological comfort for them, rating it significantly above the scale midpoint ( M = 4.51; t (884) = 9.53, p < .001). Moreover, participants assessing their smartphone rated the device as a stronger source of psychological comfort than did participants assessing their PC ( M PC = 3.62; F (1, 1353) = 88.63; η 2 = .06; p < .001).

Similar support was observed for the predicted antecedents of psychological comfort. First, participants evaluating their smartphone rated it as significantly above the scale midpoint in terms of providing a reassuring presence ( M Reassurance = 5.18; t (884) = 25.11, p < .001), haptic pleasure ( M Haptic = 4.65; t (884) = 12.59, p < .001), feelings of privacy ( M Private = 4.69; t (884) = 12.33, p < .001), being a particularly personal object ( M Personal = 5.25; t (884) = 26.39, p < .001), and being highly portable ( M Portable = 6.21; t (884) = 67.16, p < .001). Smartphones were also rated as exhibiting most of these properties to a greater extent than PCs. Compared to PCs, smartphones were seen as providing more of a reassuring presence ( M PC = 4.50; F (1, 1353) = 66.26; η 2 = .05; p < .001), conveying greater haptic pleasure ( M PC = 4.42; F (1, 1353) = 6.05; η 2 = .004; p = .014), being more portable ( M PC = 4.09; F (1, 1353) = 875.88; η 2 = .39; p < .001), and being a more personal object ( M PC = 4.86; F (1, 1353) = 20.93; η 2 = .02; p < .001). The only dimension on which participants reported no difference was in the degree to which the devices provide a sense of privacy ( M PC = 4.72; F  < 1), suggesting that while users indeed derive a sense of privacy from using their phone, this may be a benefit afforded by one’s PC as well.

Participants also reported experiencing the hypothesized downstream consequence of psychological comfort. Participants rated the extent to which they used their smartphone as a means of relieving stress as significantly higher than the midpoint on average ( M Stress relief = 4.61; t (884) = 31.86, p < .001), and additionally, they reported exhibiting this behavior more with their smartphone than with their PC ( M PC = 4.12; F (1, 1353) = 27.60; η 2 = .02; p < .001).

Testing the Theoretical Model

As depicted in figure 1 , we hypothesize that four distinctive properties of smartphones—their portability, personal nature, haptic benefits, and capacity to provide a sense of privacy—make them a reassuring presence in the lives of consumers, which results in enhanced feelings of psychological comfort when using the device. This enhanced psychological comfort, in turn, allows the device to serve as a source of relief from stress. To test this account, we submitted the measures of the various theoretical constructs to a structural path model of the hypothesized process (using SAS’s Proc CALIS). Standardized maximum-likelihood estimates of the parameters of the model are reported in figure 2A .

(A) STUDY 1: PARAMETERS OF HYPOTHESIZED STRUCTURAL MODEL OF DRIVERS OF PSYCHOLOGICAL COMFORT FROM SMARTPHONE USE and ITS DOWNSTREAM CONSEQUENCE. (B) STUDY 1: PARAMETERS OF THE EFFECTS OF INDIVIDUAL DIFFERENCES ON COMFORT and STRESS RELIEF.

** DENOTES p ( t ) < .01; * p ( t ) < .05

The estimated model provides a good fit to the data (Bentler Comparative Fit Index [BCFI] = .88; standardized root mean squared residual [SRMSR] = .08), with estimates of the parameters supporting the hypothesized path structure. As predicted, the model supports the proposition that the reassuring presence afforded by one’s smartphone leads to enhanced psychological comfort from the device ( r Reassurance→Comfort = .48, t  =   21.61, p < .001). This reassuring presence, in turn, is driven by perceptions of the portability of the device ( b Portable→Reassurance = .23, t  =   9.60, p < .001), perceptions of the phone as a personal object ( b Personal→Reassurance = .10, t  =   3.77, p < .001), perceptions of privacy when using a phone ( b Privacy→Reassurance = .28, t  =   9.28, p < .001), and the perceived haptic benefits it affords ( b Haptic→Reassurance = .36, t  =   12.82, p < .001). The analysis shows that these perceived haptic benefits do not just contribute to the reassuring presence afforded by the device but also directly affect the psychological comfort it provides ( b Haptic→Comfort = .46, t  =   12.82, p < .001). Finally, the analysis supports the proposition that the more comfort derived from the device, the more it serves as a source of relief from stress ( b Comfort→Stress relief = .64, t  =   32.81, p < .001).

Examining Individual Differences

To examine whether the degree of comfort and stress relief afforded by one’s phone varies across different individual factors, we estimated an expanded form of the proposed process model that included two sets of additional paths. One set of paths estimated how much the degree of psychological comfort derived from a smartphone depends on the person’s age, gender, use for work, and use for social or entertainment purposes. A second set of paths estimated how much the tendency to rely on a smartphone for stress relief depends on personal sources of stress (family, health, financial) and on work-related stress.

The resulting model, depicted in figure 2B , again provides a good fit to the data (BCFI = .89; SRMS = .06). As expected, the more participants relied on their smartphone for “hedonic” purposes (e.g., entertainment), the more comfort they reported deriving from the device ( b Hedonic use→Comfort = .07, t  =   3.30, p < .001). In contrast, the use of one’s smartphone for work purposes was unrelated to the psychological comfort derived from the device ( b Work use→Comfort = .01, t = .45, NS). These results suggest that the psychological comfort that many consumers derive from their smartphone is not driven by its form factor alone: it is also driven by the types of activities that users tend to engage in on the device. Participants’ age had a negative correlation with psychological comfort ( b Age→Comfort = –.05, t = –2.86, p = .004), indicating that younger consumers are more prone to associate their smartphones with psychological comfort than older consumers. There was no relation between gender and degree of comfort derived from the device ( b Gender→Comfort = –.00, NS), suggesting that men and women are similarly likely to derive psychological comfort from their smartphone.

Consistent with our general conceptualization, participants who reported relying most on their phone as a means of coping with stress tended to be those under the greatest level of personal stress (e.g., health, family) ( b Personal stress→Stress relief = .21, t  =   7.61, p < .001). While work-related stress showed a similar relationship, this effect was not as pronounced as it was for personal stress ( b Work stress→Stress relief = .06, t  =   2.08, p = .038), suggesting that the psychological comfort afforded by one’s phone might better alleviate stress arising from personal issues than from work-related problems.

An obvious limitation of the first study is that the findings are correlational in nature, limiting our ability to draw causal conclusions. The purpose of the second study was therefore to test our general thesis—that an important role of smartphones is to provide psychological comfort when needed—in a controlled experimental setting. Specifically, we test the prediction that in moments of stress, consumers will show an increased tendency to seek out and engage with their smartphone (prediction 2). To examine this, in study 2 we manipulated participants’ level of stress and then unobtrusively filmed their behavior while they waited for the next part of the study. To the extent that smartphones indeed serve as a source of comfort for their owners, we predicted that compared to those under low stress, participants under high stress would be more likely to reach for and engage with their smartphones over other objects available in their vicinity as a means of coping with their discomfort.

Seventy-eight students from a large US East Coast university (69% women) were randomly assigned to one of two conditions of a single-factor (high stress vs. low stress), between-subjects design. The study was conducted in a controlled lab setting, with one participant per session, in sessions lasting 40 minutes. Participants were each paid $12.

The lab room was split into two separate areas: a waiting area containing a single chair alongside a small table with newspapers, and a survey area containing a single desk and chair. Upon arrival, all participants were asked to place their belongings, including their “smartphone and anything else that could be distracting,” in the waiting area. They were then led to the survey area, where they were asked to complete a series of paper-and-pencil tasks.

First, they were asked to rate their momentary feelings (with pencil and paper), including their level of felt comfort (measures described below). Next, depending on their assigned condition, participants completed a task designed to either increase their level of stress (in the high-stress condition) or not (in the low-stress condition). After the stress manipulation, participants were instructed to return to the waiting area and sit until the experimenter returned. All participants then sat alone in the waiting area for a predetermined amount of time while, unbeknownst to them, their behavior was surreptitiously filmed by a hidden camera located in a wall clock facing their chair. Upon the experimenter’s return, participants were led back to the survey area, where they were asked to complete another paper-and-pencil questionnaire in which they reported their felt comfort for a second time and answered a series of control and demographic questions. They were then debriefed, asked for permission to use their data, and paid.

Stress Manipulation

The stress manipulation was adapted from the classic Trier Social Stress Test ( Kassam, Koslov, and Mendes 2009 ; Kirschbaum, Pirke, and Hellhammer 1993 ) and was administered on paper. Participants were randomly assigned to either a high-stress or low-stress condition. In the high-stress condition, participants were given five minutes to prepare in writing a speech about why they are the perfect candidate for a particular job, under the cover of a “Job Interview Preparation Study.” They were led to believe that they would subsequently have to recite this speech from memory on camera so that a video analysis of their speech could be conducted at a later time. To boost the cover story, a video camera was placed in the survey area, facing participants as they prepared their speech. In contrast, in the low-stress condition, the task was positioned as a “Job Preparation Study.” Participants in this condition were given five minutes to write about advice they would give to someone who was starting the same position as described in the high-stress condition. Unlike in the high-stress condition, participants in this condition were not led to believe that they would need to present their writing on camera, and correspondingly there was no visible camera in the survey area. The exact instructions used in the stress manipulations are reproduced in web appendix 4 .

Unobtrusive Measurement of Behavior

After participants completed their assigned job-preparation task, the experimenter asked them to sit in the waiting area for “about 10 minutes.” In the high-stress condition, the alleged rationale for this waiting period was that a PhD student needed to review their speech to generate follow-up questions for them to answer on camera. In the low-stress condition, the alleged rationale was that the research assistant simply needed to transcribe their writing. In reality, during this 10-minute period, participants were unobtrusively filmed as they waited alone for the next part of the study.

During this time, participants had access to their personal belongings, including their smartphone and any other items that they had brought with them to the study (e.g., backpacks, books, laptop). In addition, two newspapers (the New York Times and Wall Street Journal ) were intentionally placed on the small table beside the chair, providing participants the option of engaging with alternative stimuli. Newspapers were chosen because they are commonly available in waiting areas and therefore serve as a natural, externally valid object with which participants could potentially engage.

Behavioral Dependent Measures

The video footage of participants’ behavior during the waiting period was subsequently coded by two independent judges who were blind to the study’s hypothesis and to participants’ conditions. The judges were instructed to code the footage for a set of objective aspects of the participant’s behavior (e.g., what time the participant reached for the first object during the waiting period; what the first object was). From these observable indicators we calculated a battery of behavioral measures designed to capture participants’ propensity to preferentially seek out and engage with their smartphone (e.g., the time elapsed before the participant first reached for his or her phone, if at all; the proportion of waiting time spent on the phone). Table 1 provides summary statistics of the key measured variables with respect to smartphone behavior across the two conditions.

STUDY 2: FREQUENCIES, MEANS, and INTERRATER RELIABILITIES FOR ALL BEHAVIORS DURING THE WAITING PERIOD

Means reported were calculated after two coders (blind to both condition and hypothesis) reconciled the measures they had originally disagreed on. Cronbach’s alphas reported in the table reflect the interrater reliability prior to reconciliation of measures.

Felt Comfort and Other Measures

Participants’ level of felt comfort was assessed at two points in time: once upon arrival (time 1) and a second time after the waiting period (time 2). Specifically, participants were asked to rate their agreement with 13 statements about their momentary feelings (see web appendix 5 ), five of which focused on their felt comfort: “I feel relaxed,” “I feel calm,” “I feel at ease,” “I feel a sense of comfort,” and “I feel anxious” (reverse-coded) on a scale of 1 = “Not at all” to 7 = “Very much so” ( Kolcaba and Kolcaba 1991 ; Marteau and Bekker 1992 ). Responses to these five items were averaged to create a felt-comfort measure for times 1 and 2. The change in felt comfort from time 1 (α = .86) to time 2 (α = .90) provided a check of the stress manipulation (albeit an imperfect one).

As control measures, participants additionally reported how frequently they use their phone per day, how long they have owned their current smartphone, specific behaviors surrounding the device (e.g., how much they paid for their phone case), and how emotionally connected they are to their phone (four items, α = .70). Participants were also asked to indicate the last time they used their smartphone and to describe what they did on their phone while in the waiting area (see web appendix 6 ). This latter measure was gathered to address the alternative explanation that it is solely the social functionality afforded by phones—rather than the phones themselves—that engenders usage under stress.

Preliminary Analyses

Upon debriefing, two participants (one in the high-stress condition) refused to have their data included in the study, and another five (two in the high-stress condition) were excluded for not having their smartphone with them at the study, thus leaving 71 participants for analysis. Participants did not differ across conditions in terms of their momentary feelings upon arrival to the study, the number of daily hours spent on the device, the length of time they owned the device, emotional connection to their smartphone, reported behaviors involving their device, or demographics (all F -values < 1). This suggests that randomization across conditions was effective. As a tentative check of the stress manipulation, while participants in the two conditions reported similar levels of felt comfort at time 1 ( M High-Stress = 4.19 vs. M Low-Stress = 4.29; F  <   1), at time 2 high-stress participants reported significantly lower levels of comfort ( M  =   3.44) than did low-stress participants ( M  =   4.71; F (1, 69) = 21.68; η 2 = .24; p < .001), suggesting that the manipulation was effective.

Main Analyses

Based on prediction 2, we predicted that high-stress (vs. low-stress) participants would be more likely to seek out their smartphones over other available objects, and to exhibit greater engagement with the device. Consistent with this prediction, the results showed that high-stress participants were indeed more likely to engage with their smartphone first—that is, before other available objects (63.9%)—than were low-stress participants (34.3%; x 2 (1) = 5.09, p = .024; see table 1 ). In addition, among participants who did reach for their phone at some point during the waiting period (72.2% in the high-stress condition and 60.0% in the low-stress condition), high-stress participants reached for their phone much sooner than did low-stress participants (Poisson regression β = –1.37, p < .001). Specifically, on average, high-stress participants reached for their phones only 23.9 sec after first sitting down, whereas low-stress participants waited 89.7 sec before first reaching for their phone.

With respect to the degree of sustained attention on the device, we first tested for differences in the average time spent per interaction with one’s smartphone during the waiting period. As predicted, high-stress participants spent significantly more time per interaction with their device ( M  =   299.32 seconds per interaction) than did low-stress participants on average ( M  =   165.54 seconds; Poisson regression β   = .37, p < .001). Relatedly, high-stress participants also showed greater engagement with their smartphone, spending a greater proportion of the total waiting time on their device ( M  =   51.3%) than low-stress participants ( M  =   31.3%; t  =   1.96, p = .054).

An additional analysis shows that high-stress participants were much more likely to reach for their smartphone first than for any of their other personal belongings (e.g., laptop, book) available during the waiting period ( M  =   13.9%; z  = 3.56, p < .001), suggesting that smartphones have special status as an object of comfort relative to other personal belongings.

Social Contact as an Alternative Explanation

One possible alternative explanation is that high-stress (vs. low-stress) participants sought out and engaged with their smartphones not for their comforting effects per se, but because they were in search of social contact—one of the many functions available on the device (e.g., writing a text message to a friend). Inconsistent with this account, high-stress participants were no more likely to make social contact on their phone during the waiting period (30.8%) than were low-stress participants who used their device (23.8%; x 2 (1) = 0.04, NS).

The findings of the first lab experiment support the prediction that moments of greater stress make consumers more likely to seek out and engage with their smartphone as a means of coping with their discomfort (prediction 2). Specifically, we found that compared to low-stress participants, high-stress participants were quicker to reach for their smartphone first, and they engaged with the device more intensely. In addition, high-stress participants preferentially sought out their phone over other personal objects they brought with them, such as items in their backpack (e.g., their laptop), as well as newspapers made available to them in the waiting area. This suggests that the palliative effect provided by one’s smartphone is not equally afforded by any potential source of distraction (e.g., one’s laptop, newspapers). The results also show that the tendency for participants to seek out their phones under greater stress cannot be accounted for by preexisting differences in participants’ situational feelings upon arrival, general emotional connection to their smartphones, or demographic factors, nor did it appear to be driven by differences in the desire to engage in social contact.

The results of the first lab experiment confirmed that in moments of stress, consumers show an enhanced tendency to seek out and engage with their smartphone, even when other objects are at their disposal (prediction 2). The purpose of the next two studies was to examine in a controlled lab experimental setting whether engagement with one’s smartphone does indeed provide psychological comfort when needed. Specifically, in study 3 we test the prediction that even brief engagement with one’s smartphone can provide relief from a stressful situation—more so than engaging with another personal device with comparable functionality: one’s laptop (prediction 3A). Laptops provide a meaningful comparison as a control condition for several methodological, theoretical, and substantive reasons. From a methodological standpoint, laptops and smartphones can be used to perform many of the same activities, which allows us to hold constant the task and information consumed across conditions. In addition, laptops and smartphones have similar ownership and usage rates among US consumers, which helps address possible alternative explanations related to device familiarity. (Tablets such as iPads, which exhibit lower ownership and usage rates among US consumers, were not selected for this reason.) From a theoretical standpoint, laptops and smartphones share many functionalities (e.g., browsing, social media, email), which is helpful in testing the idea that the special relationship that consumers form with their smartphone cannot be solely explained by its functionalities. At the same time, laptops differ from smartphones in several ways that are theoretically meaningful with respect to our conceptualization; namely, they are less portable, less haptic, and potentially less personal than smartphones (prediction 1). Finally, from a substantive standpoint, the comparison with laptops is a natural one, often discussed by marketers and firms as part of the “mobile revolution.”

In this study, all participants first underwent a stress induction and were then instructed to browse the same web page either on their smartphone in one condition, or on their laptop in the other condition. Participants’ momentary feelings were measured at three points during the study session: (1) prior to the stress induction, (2) after the stress induction but before participants used their assigned device, and (3) after participants used their assigned device.

We predicted that participants who used their smartphone would show greater recovery from discomfort due to stress than participants who performed the same task on their laptop. We additionally predicted that smartphone usage would be uniquely associated with enhanced feelings of comfort as opposed to other emotions. In other words, we expected smartphone use to enhance feelings of psychological comfort in particular rather than positive emotions in general (e.g., satisfaction).

Fifty students from the same university participant pool as in study 2 were randomly assigned to the conditions of a 2 (device: smartphone vs. laptop) × 3 (time: time 1 vs. time 2 vs. time 3) mixed design, with device as a between-subjects factor and time as a within-subject factor. We note here that, given that participants needed to be run one at a time per session, the sample size of study 3 was constrained by available lab resources; nevertheless, the within-subject nature of the mixed design lent reasonable power to the analysis. Specifically, an a priori power analysis using SAS PROC GLMPOWER concluded that the design study had an 85% chance of correctly rejecting a false null hypothesis of no time-by-device interaction at p = .05 (assuming a standard deviation and serial correlation of measures of .6, and an expected post-stress difference between devices of .5).

All participants were required to bring both their smartphone and their laptop with them to the session. To control for potential distractions posed by the presence of other participants, the study was administered one participant at a time. To ensure that the presence of the devices would not impact participants’ feelings prior to the device manipulation, upon arrival participants were asked to put their smartphone and laptop in the adjacent cubicle. They each received $8 for 30 minutes of participation.

Felt Comfort Measure (Time 1)

At the beginning of the study, participants were told that they would be participating in two (allegedly) unrelated studies that were combined for greater efficiency. Before beginning the “first” study, participants were asked to rate their agreement with the same 13 statements about their momentary feelings as in study 2 (see web appendix 5 ), including the five statements focusing on participants’ felt comfort (“I feel relaxed,” “I feel calm,” “I feel at ease,” “I feel a sense of comfort,” and “I feel anxious” [reverse-coded]). Responses to these five items were averaged to create a measure of felt comfort at time 1 (α = .88) as part of the main dependent variable. To test the prediction that it is felt comfort in particular that is enhanced by smartphone (vs. laptop) use rather than other types of feelings in general, the remaining nine statements were included to assess a variety of other momentary emotions.

Stress Induction

Next, all participants completed “study 1,” which was cast as a task performance study but actually served as a stress induction. To induce stress among participants, we used a standard stress procedure in the literature, which consists of administering a series of cognitive tasks under time constraints ( Boyes and French 2010 ). Based on two pretests, one of them with students from the same pool as the main study, we selected three sets of cognitive tasks for the stress induction: (a) 15 GMAT math problems, (b) 18 Remote Associates Test (RAT) items ( Mednick and Mednick 1967 ), and (c) 18 anagrams. The three sets of tasks were presented in increasing order of task difficulty, as were the individual items within each set. Participants in the main study carried out the tasks on paper and were given three minutes to complete each task, which pretests had shown was greatly insufficient. To intensify the stressful aspects of the overall procedure, the experimenter set a timer to ring loudly every minute. Pretest results indicated that the overall procedure was effective in inducing stress among participants. The problem sets are reproduced in web appendix 7 .

Felt Comfort Measure (Time 2)

After completing the stress induction, participants were again asked to report their momentary feelings on the same items as at time 1, with responses to the five comfort-related items averaged into an index of felt comfort at time 2 (α = .85). Changes in felt comfort from time 1 to time 2 served as a check of the stress induction.

Device Manipulation

Next, participants completed “study 2,” which was ostensibly about social media but in fact administered the device manipulation. Participants were randomly assigned to browse a specific social media site either on their smartphone in the experimental condition, or on their laptop in the control condition. To minimize the possibility that any effects observed might be driven by differences in the content consumed across conditions, all participants were asked to browse a page called “Things Fitting Perfectly into Other Things” on Tumblr. This specific web page was chosen for two reasons. First, Tumblr has comparable interfaces across its mobile and web-based formats, and second, this particular Tumblr blog displays simple images with minimal or no text, making the content similarly amenable to browsing on both laptop and smartphone devices. As a check, at the end of the study participants across conditions were asked to rate how user-friendly they found the browsing experience to be. All participants were given five minutes to browse the site “Things Fitting Perfectly into Other Things,” allegedly in order to search for images that they particularly liked on the page.

Felt Comfort Measure (Time 3)

After five minutes had passed, the experimenter instructed participants to stop browsing and handed out the final set of questions that measured participants’ felt comfort after using their assigned device. Participants responded to the same questions presented at times 2 and 3, yielding a third five-item index of felt comfort (α = .78). Increases in felt comfort from time 2 to time 3 were interpreted as relief from stress following device usage, which was the primary focus of the study.

Finally, participants were asked to indicate their preexisting familiarity with the Tumblr site (whether they had a Tumblr account prior to the study) and how user-friendly they found the Tumblr application or website to be on a scale of 1 (“Not user-friendly at all”) to 5 (“Very user-friendly”). They also answered a series of control questions about demographics, frequency of smartphone use (i.e., average number of hours spent on the device per day), as well as the perceived difficulty of the stress-induction tasks (i.e., how difficult they found each of the three problem sets to be, and how much more time they would have liked to complete the tasks) (see web appendix 8 ).

The results confirmed no differences between device conditions in terms of participants’ demographics, smartphone usage frequency, and preexisting familiarity with Tumblr. Of the 13 items assessing momentary feelings at time 1 (prior to the stress induction), only one—felt frustration—indicated an unexpected initial difference between conditions, with participants in the smartphone condition reporting a marginally higher level of frustration upon arrival ( M  =   2.60) than those in the laptop condition ( M PC = 1.88; F (1, 48) = 3.96, p = .051; see web appendix 9 for all means). However, none of the five items assessing the dependent measure of interest—felt comfort—showed any initial difference.

A check of the stress induction confirmed a significant decrease in participants’ felt comfort from time 1 (upon arrival; M  =   4.87) to time 2 (immediately following the stress induction; M  =   3.54; F (1, 48) = 93.08; η 2 = .66; p < .001). Importantly, between time 1 and time 2, there was no time × device interaction ( F  <   1), confirming that the stress induction had parallel effects across conditions. There were no significant differences across conditions in the reported difficulty of each stress-induction task (largest F (1, 48) = 2.16, NS), the additional amount of time participants would have liked in order to complete the tasks ( F (1, 48) = 2.84, NS), or in the number of questions attempted in each task (all F -values < 1). These latter findings suggest that the randomization was largely effective in equating participants across conditions prior to the device-usage manipulation.

Stress Relief Due to Device Usage

To test the prediction that using one’s smartphone provides greater relief from stress than using another personal device with comparable functionality (one’s laptop), measures of participants’ felt comfort at times 1, 2, and 3 were submitted to a mixed ANOVA with time as a within-subject factor and device as a between-subjects factor. A significant main effect of time ( F (2, 96) = 64.80; η 2 = .57; p < .001) showed a decrease in participants’ felt comfort from time 1 ( M  =   4.87) to time 2 ( M  =   3.55), as reported earlier ( F (1, 48) = 83.40; η 2 = .63; p < .001), followed by an increase in felt comfort from time 2 to time 3 ( M  =   5.11; F (1, 48) = 98.64; η 2 = .64; p < .001).

More importantly, this effect was qualified by a significant time × device interaction ( F (2, 96) = 4.16; η 2 = .04; p = .018). Focusing on changes in comfort between time 2 and time 3, a planned interaction contrast reveals, as predicted, a greater increase in felt comfort from time 2 to time 3 among participants who used their smartphone ( M Time 2 = 3.37 vs. M Time 3 = 5.33; F (1, 24) = 68.32; η 2 = .74; p < .001) than among participants who browsed the same content on their laptop M Time 2 = 3.73 vs. M Time 3 = 4.88; F (1, 24) = 31.67; η 2 = .57; p < .001; interaction contrast: F (1, 48) = 6.55; η 2 = .04; p = .014).

In fact, participants in the smartphone condition reported even greater comfort at time 3 ( M  =   5.33) than they did at time 1 ( M  =   4.77; F (1, 24) = 7.97; η 2 = .25; p = .013), whereas participants in the laptop condition only returned to the same level of felt comfort at time 3 ( M  =   4.89) as they reported at time 1 ( M  =   4.97; F  <   1; interaction contrast: F (1,48) = 5.23; η 2 = .09; p = .027). Therefore, not only did the use of their smartphone help participants recover from stress better than did the use of their laptop, it actually raised participants’ overall sense of comfort over and above the initial state they were in prior to the stress induction.

Mixed ANOVAs of other feelings measured at times 1, 2, and 3 show main effects of time on feelings of confidence, satisfaction, focus, frustration, happiness, and sadness (largest F (2, 96) = 52.04, p < .001; see web appendix 6 for all means). However, none of these effects was moderated by the type of device (largest F (2, 96) = 1.40, NS), suggesting that it is feelings of comfort (and stress alleviation) in particular that smartphones enhance, rather than positive affect in general.

The results of study 3 support the proposition that consumers not only preferentially seek out their smartphone in moments of stress (as shown in study 2), but also derive psychological comfort from their device when needed. Specifically, the study shows that compared to the use of another personal device with comparable functionality, the mere use of one’s smartphone to perform the same brief task is sufficiently comforting to provide relief from a recent stressful experience. The results of this experiment are noteworthy in three respects. First, methodologically, the fact that the task and associated content (the web page) were held constant across conditions means that any observed difference in comfort and stress relief cannot be attributed to mere differences in information consumed across devices. Second, from a more theoretical perspective, the fact that the effects cannot be attributed to differences in content means that the observed sense of comfort arises from the device itself. Third, the fact that the sense of comfort and stress relief provided by the use of one’s smartphone exceeds that afforded by the use of one’s laptop—a device with comparable functionalities—is consistent with a general view that the relationship that consumers have with their smartphone is a special one that cannot be strictly reduced to the device’s functional value.

Although personal laptops provide a natural point of comparison for testing whether smartphones serve as distinct sources of comfort for owners, a limitation of study 3 is that laptops may have been less stress-relieving for reasons that are unrelated to our theorizing. For example, while laptops differ in terms of two of the theorized drivers of psychological comfort—their portability and haptic nature (prediction 1)—it is possible, for example, that consumers may use their laptop more for work and less for leisure than their smartphone, or that the effects are driven in part by differences in their physical form that are not accounted for by our theory. Thus, a more stringent test of our theorizing would hold the type of device constant.

The purpose of the final lab experiment is to show that the use of one’s own smartphone to engage in a given activity helps alleviate stress to a greater extent than the use of an otherwise similar smartphone belonging to someone else (prediction 3B). Such a finding would lend support to our theorizing in several ways. First, it would provide further evidence that the comfort that people derive from interacting with their smartphone does not strictly arise from the sheer functionalities available on the device. Second, it would provide support for our proposition that the psychological comfort derived from smartphones is driven in part by the highly personal nature of the device (prediction 1).

The general design of this study was similar to that of study 3. All participants first underwent a stress induction and then were asked to browse the same content on a smartphone. In one condition, it was their own smartphone; in the other condition, it was an otherwise similar phone belonging to the lab. We predicted that compared to participants engaging in the task on the lab’s smartphone, participants engaging in the same task on their own smartphone would derive greater psychological comfort and thus exhibit greater recovery from stress.

Seventy-five participants from a different university than in studies 2 and 3 (71% women) were randomly assigned to the conditions of a 2 (ownership: own smartphone vs. lab smartphone) × 3 (time: time 1 vs. time 2 vs. time 3) mixed design, with ownership as a between-subjects factor and time as a within-subject factor. The effect sizes observed in study 3 provided guidance for determining the sample size for study 4, which was planned using SAS’s PROC GLMPOWER. Assuming means and standard deviations similar to those observed in study 3 as well as the same mixed design, we sought a sample size that would have a 90% probability of correctly rejecting the null hypothesis of no interaction between device and time 2-to-3 at α = .05. The final sample size of 75 had an a priori power of .93.

The study was again conducted in a controlled lab setting, one participant at a time, in sessions lasting 30 minutes for which participants were paid $10. All participants were required to bring their smartphone to the lab. They were led to believe that they were completing two separate surveys combined for greater efficiency. As part of “study 1,” which was cast as a task performance study, participants were first asked to report their momentary feelings along the same 13 items as in studies 2 and 3 (see web appendix 5 ), plus an additional item measuring felt stress (“I feel stressed”). The five comfort-related items from the prior studies and the additional stress-related item (reverse-coded) were combined to form a six-item index of felt comfort at time 1 (α = .90).

All participants were then administered the same stress induction as were the high-stress participants in study 2. That is, under the cover of a “Job Interview Preparation Study,” all participants were given five minutes to prepare in writing a speech about why they are the perfect candidate for a particular job, and were led to believe that they would have to recite this speech on camera. After completing the stress induction, participants were again asked to report their momentary feelings on the same items as at time 1, which was used to construct the six-item index of felt comfort at time 2 (α = .91). Changes in felt comfort from time 1 to time 2 served as a check of the stress induction.

Next, participants completed “study 2,” ostensibly about the user experience of different devices, which actually served as the ownership manipulation. Participants were led to believe that the experimenters were interested in “how users’ reactions to the use of the lab’s devices compare to their reactions to their own personal devices.” Participants in the own-phone condition were asked to take out their smartphone to browse the “Things Fitting Perfectly” Tumblr page for five minutes (as in study 3), whereas participants in the lab-phone condition were asked to complete the same browsing task on the lab’s smartphone—either on an iPhone 6 or Samsung Galaxy S5 (an exploratory survey revealed that these two smartphone models were the two most commonly owned models among the lab participant pool). To ensure that participants in the lab-phone condition did not need to search through an unfamiliar interface to locate the content, the Tumblr page was already open on the device in this condition.

After five minutes had elapsed, participants’ momentary feelings were measured for a third time, providing an index of felt comfort at time 3 (α = .88). Again, an increase in felt comfort from time 2 to time 3 was interpreted as relief from stress following device usage, which was the primary focus of the study. Participants then responded to the same control questions about demographics and frequency of smartphone use (i.e., average number of hours spent on the device per day) as in study 3. Finally, lab-phone participants were additionally asked to rate how similar the lab’s phone was to their own device along the following dimensions (on a scale of 1: “Completely different” to 7: “Exactly the same”): physical comfort, ease of use, brightness, and vividness/clarity (see web appendix 10 ). These four measures were combined to form a lab-phone similarity index (α = .84).

As in study 3 there were no differences across conditions in terms of participants’ demographics or smartphone usage frequency. In addition, there were no initial differences along any of the 14 items assessing momentary feelings at time 1 prior to the stress induction. Participants in the lab-phone condition reported perceiving the lab’s device as similar to their own, with the mean perceived similarity significantly above the midpoint of the seven-point scale ( M  =   5.92; t (28) = 8.02, p < .001). Finally, a check of the stress induction confirmed a significant decrease in participants’ felt comfort from time 1 (upon arrival; M  =   4.54) to time 2 (immediately following the stress induction; M  =   3.33; F (1, 73) = 91.43; η 2 = .52; p < .001; see web appendix 11 for all means).

To test the central prediction that using one’s smartphone provides greater relief from stress than an otherwise similar phone belonging to someone else, measures of participants’ felt comfort at times 1, 2, and 3 were submitted to a mixed ANOVA with time as a within-subject factor and device as a between-subjects factor. A significant main effect of time ( F (2, 146) = 98.33; η 2 = .55; p < .001) showed that the decrease reported above in participants’ felt comfort from time 1 ( M  =   4.54) to time 2 ( M  =   3.33) was followed by an increase in felt comfort from time 2 to time 3 ( M Time 2 = 3.33 vs. M Time 3 = 5.25; F (1, 73) = 137.50; η 2 = .65; p < .001).

More importantly, as in study 3, this effect was qualified by a significant time × device interaction ( F (2, 146) = 8.15; η 2 = .05; p < .001). Examining the interaction contrast for time 2 to time 3 ( F (1, 73) = 12.24; η 2 = .05; p < .001), we see that the results reveal a significantly greater increase in felt comfort among participants who used their own smartphone ( M Time 2 = 2.90 vs. M Time 3 = 5.36; F (1, 37) = 94.20; η 2 = .72; p < .001) than among those who browsed the same content on the lab’s smartphone ( M Time 2 = 3.76 vs. M Time 3 = 5.14; F (1, 36) = 64.65; η 2 = .64; p < .001). These results thus provide a conceptual replication of those observed in study 3. In addition, on average, the degree of comfort reported immediately after use of the device was greater than that reported at the onset of the study ( M Time 3 = 5.25 vs. M Time 1 = 4.54; F (1, 73) = 28.09; η 2 = .27; p < .001), although here the time-by-device interaction was not significant (own-phone: M Time 3 = 5.36 vs. M Time 1 = 4.51; lab-phone: M Time 3 = 5.14 vs. M Time 1 = 4.57; interaction: F (1, 73) = 1.07; η 2 = .01; NS).

One factor that potentially complicates this analysis, however, is that the effect of the stress manipulation was somewhat stronger for those in the own-phone condition compared to the lab-phone condition, such that participants in the own-phone condition reported lower comfort after the manipulation than those in the lab-phone condition (time 2: M own = 2.90 vs. M lab = 3.76; F (1, 73) = 8.50; η 2 = .10; p = .005). While this difference would presumably make it more difficult to observe greater stress relief in the own-phone condition, to ensure that the effects were not influenced by the difference in the strength of the manipulation we reanalyzed the data in a mixed-model analysis using SAS Proc Mixed that controlled for differences in felt comfort at time 2, treating participants as a nested random effect. The analysis confirmed the original findings, again revealing a significant time-by-ownership interaction after controlling for time 2 differences ( F (1, 73) = 15.75; η 2 = .04; p < .001). Specifically, participants who used their own smartphone still experienced a greater rate of recovery from stress from time 2 to time 3 ( LSM Time 2 = 3.18 vs. LSM Time 3 = 5.63; F (1, 37) = 133.43; η 2 = .89; p < .001) relative to participants who engaged with the lab’s smartphone ( LSM Time 2 = 3.47 vs. LSM Time 3 = 4.86; F (1, 36) = 82.32; η 2 = .27; p < .001).

The results of this study conceptually replicate those of study 3 and extend them in an important way. They again show that the mere use of one’s smartphone to perform a simple task for a few minutes is sufficiently emotionally comforting to provide relief from a recent stressful experience, replicating the results of study 3 within the smartphone (vs. laptop) condition. More importantly, the results additionally seem to suggest that the comforting effects of using a smartphone are stronger for one’s own smartphone than for an otherwise similar smartphone belonging to someone else. This finding is consistent with our theory that the psychological comfort afforded by one’s phone is partly driven by the personal nature of the device, which enables it to serve as a reassuring presence for owners and thus increase their sense of comfort (prediction 1). Studies 3 and 4 tested all the proposed components of our theoretical model.

The purpose of this research was to develop a better understanding of the nature of the relationship that many consumers form with their smartphone—a device that in the span of only a few years has become one of the most ubiquitous and frequently used products among consumers, as well as the primary device through which online consumption activities take place. While in recent years a descriptive literature has emerged on people’s self-reported smartphone use ( Bianchi and Phillips 2005 ; De-Sola Gutiérrez et al. 2016 ), theoretical and experimental investigations into this relationship have been limited. In this work we provide one of the first theoretical accounts of many consumers’ relationship with their smartphone, including the antecedents that underlie it as well as downstream consequences. Our central thesis is that, for many consumers, smartphones serve as more than just practical tools: consumers also experience enhanced psychological comfort from engaging with their device, which allows it to serve as a palliative aid for owners during moments of stress—not unlike how pacifiers (and other attachment objects) provide psychological comfort to young children.

Also central to our theory is the proposition that a smartphone affords feelings of comfort not just because of its functionalities, but rather because of a unique combination of properties: its role as a reassuring presence in the daily lives of consumers, which arises from its portability, highly personal nature, the sense of privacy it invokes when engaged, and the haptic pleasure users derive from handling their device. The role of the device as a reassuring presence, in turn, allows the device to enhance feelings of psychological comfort when consumers engage with it.

In this article we report the results of four studies, including a large-scale field study and three controlled laboratory experiments, that lend support to these ideas. The first field study offered evidence for the proposed theoretical model about how the various properties of one’s smartphone lead it to represent a source of psychological comfort, as well as the downstream consequences of this comfort. Next, a lab experiment showed that participants who underwent greater stress were more likely to seek out their phone and to show greater engagement with the device (even with other objects at their disposal), presumably as a means of coping with stress. The final two controlled lab experiments then provided direct evidence for the role of smartphones as a source of psychological comfort, showing that participants who engaged with their smartphone reported a greater enhancement in comfort after stress relative to those using the same feature on their personal laptop (study 3) and even those using an otherwise similar smartphone belonging to someone else (study 4). An additional study reported in web appendix 1 (study 5) provides further support for our general thesis.

Might Other Conceptualizations Better Describe Consumers’ Relationship to Their Smartphone?

As discussed at the outset of this article, the majority of work on the topic of consumers’ relationship to their phone has argued that it resembles a behavioral addiction ( Alter 2017 ; De-Sola Gutiérrez et al. 2016 ; Hostetler and Ryabinin 2012 ). We believe, however, that “addiction” is an inadequate conceptualization of consumers’ relationship to the device. While the term can be used to label a certain set of behaviors with the device, it is a strictly negativist framing of consumers’ relationship to their phone and, more importantly, does not provide insight into the psychological mechanisms that give rise to this relationship. In this work we offer evidence that there is a positive emotional side to individuals’ relationship with their phone: namely, its ability to serve as a source of comfort for many consumers. We posit that these associations of comfort apply to a broader segment of consumers than “addiction,” which can be understood as a narrower behavioral phenomenon. Moreover, we propose and test a theory that explains the origins of this proposed relationship.

One question that might arise is whether the use of one’s phone as a source of comfort results from its ability to serve as a means of distraction—something that could be similarly satisfied by a number of objects or substances (e.g., smoking or eating, as shown in study 5, web appendix 1 ). Consistent with this, participants in study 1 indicated that they often used their phone to distract themselves when they felt bored ( M =  5.42 on a seven-point scale; significantly above the midpoint: t (884) = 25.60, p < .001). That said, our findings suggest that distraction is only one part of the story. In study 2, for example, participants who felt greater stress were more likely to seek out their smartphone over other objects at their disposal that could serve as a means of distraction (e.g., their laptop, newspapers). Likewise, in studies 3 and 4 we found, for example, that browsing the same distracting content (a particular Tumblr page) after a stress induction indeed helped participants recover from their discomfort, but that the rate of recovery was greater when participants browsed the content on their smartphone than on other devices. Taken together, these results suggest that distraction alone cannot fully account for the palliative benefits afforded by the device.

More generally, another possible conceptualization of the nature of consumers’ relationship with their smartphone is that they view the device not as a source of comfort per se, but rather as an extension of themselves ( Belk 1988 ; Schifferstein and Zwartkruis-Pelgrim 2008 ). To examine this possibility, in study 1 we asked participants to indicate (on a seven-point scale) the degree to which they thought of their smartphone or PC as an extension of themselves. We used a modified version of Ball and Tasaki’s (1992) self-extension scale, which included the items: “My phone (PC) reminds me of who I am,” “If my phone (PC) was praised by others I would feel as if I were praised,” “If someone ridiculed my phone (PC) I would feel attacked,” and “If I lost my phone (PC) I would feel like I lost a little of myself” (α = .87). The results are inconsistent with a self-extension account of consumers’ relationship to their device. First, participants tended to disagree when asked if they saw their smartphone as an extension of themselves ( M Smartphone = 3.42 out of 7; significantly below the scale midpoint; t (884) = –10.44, p < .001). Second, to the degree that they did view their phones as self-extension, it was to a lesser extent than their PC ( M PC = 3.70; contrast F (1, 1353) = 8.12; p = .004).

Implications for Consumer Welfare and Practitioners

Our findings show that, in addition to deriving functional benefits from the device, phone owners seem to also derive emotional benefits that even Steve Jobs may have failed to foresee: a device with the capacity to provide comfort and relief in times of stress. In a field study reported in web appendix 1 (study 5), we provide real-world evidence that consumers particularly susceptible to stress—those who recently quit smoking—were more likely to show emotional and behavioral attachment to their phone, suggesting that the device may serve as a means of compensating for the stress relief previously afforded by cigarettes. The finding that people who recently quit smoking made greater use of their smartphone suggests that such behavior might actually be encouraged by health professionals as a means to reduce stress across a variety of contexts. While our results imply that adults can derive emotional benefits from engaging with their smartphone, as noted above, much of the extant research on people’s relationship to the device has focused on the potential dark side to this attachment—the possibility that, for some, an emotional connection to their phone might develop into an apparent addiction to the device, with negative social and emotional consequences ( Hostetler and Ryabinin 2012 ). An important area for future research, therefore, would be to better understand the conditions under which the comforting benefits of smartphone ownership might transform into an unhealthy dependence on the device and, just as critically, the kinds of design interventions that might be taken to diminish—rather than enhance—attachment in such cases.

Our results also have important practical implications for firms and marketers, who over the past few years have been responding to the “mobile revolution” by diverting more of their budgets to mobile advertising ( eMarketer 2016 ) and attempting to pursue “mobile-first” digital strategies ( Kepes 2015 ). The findings shed light on the unique emotional mind-set that consumers experience while on the device. For one, whereas mobile phone companies focus their persuasive messaging almost exclusively on features available on the device (e.g., battery life, display resolution), our findings suggest that marketers might additionally emphasize the psychological feeling of comfort and reassurance that comes with having one’s smartphone in hand. To the extent that people are more open to processing information when in a relaxed state ( Pham, Hung, and Gorn 2011 ), retailers could leverage this insight by investing more aggressively in beacons and other technology that enable them to reach customers on their smartphone in-store.

Finally, our theoretical model provides insights for smartphone brands that, for example, might be interested in understanding why consumers would be eager to upgrade their current phone even though the device serves as a source of comfort for them. Within our model, feelings of comfort are theorized to flow not just from mere ownership but also from the portability, customizability, and haptic nature of the device—attributes that tend to improve with each new generation of smartphone models. Thus, if a newer model offers more opportunities for personalization and a more ergonomic and haptic interface, for example, then our model would predict that consumers may be willing to abandon their current smartphone for a potentially more comforting one.

Limitations and Future Research

As one of the first theoretically driven attempts to understand the psychology that underlies consumers’ relationship to their smartphone, our research was intentionally limited in scope. For example, study 1 showed that one of the antecedents of consumers’ relationship to their phone is the haptic pleasure that arises from its use, which was further substantiated by the results of study 3 wherein participants derived more comfort from engaging with their smartphone than their PC, a less haptic personal device. An interesting avenue of future research would be to further explore the role of haptics in the effect—for example, whether similar psychological effects arise for other electronic devices that consumers have constant tactile contact with, such as “wearable tech” (e.g., Fitbits, Apple watches).

Future work could also investigate the relation of our findings to the literature on adult attachment theory, which has focused on people’s style of attachments to close others. Notably, prior work has shown that people with insecure attachment (anxious or avoidant) tend to be more likely to rely on childhood attachment objects (such as a teddy bear) in adulthood ( Nedelisky and Steele 2009 ). While a full empirical examination of this issue is outside the scope of the current investigation, as an initial exploration of this issue in study 1 we examined the degree to which participants’ attachment styles correlated with the extent to which they viewed their phone as a source of psychological comfort (using the six-item scale described in study 2). We thus asked participants in study 1 to respond to Hazan and Shaver’s (1987) adult attachment style scale (using rewording suggested by Collins and Read 1990 ), which measures the degree to which individuals exhibit three styles of interpersonal attachments: secure attachment, characterized by trust and friendship; anxious attachment, characterized by fear of being abandoned or unloved; and avoidant attachment, characterized fear of closeness. The results supported the expected associations. Participants who exhibited more of an avoidant attachment style were most likely to rely on their phone as a source of comfort ( r = .18, p < .001), followed by those exhibiting greater anxious attachment ( r = .10, p = .003). In contrast, there was only a weak association between secure attachment and degree of comfort derived from the device ( r = .06, p = .077). These preliminary results suggest that people may rely on their smartphone as a surrogate for the comfort derived from interpersonal relationships, which is generally consistent with our broader conceptualization of smartphones as exhibiting similar properties as attachment objects. We see a more complete investigation of the relationship between people’s attachment styles and the comfort they derive from their smartphone as a fruitful avenue for future research.

Moreover, we show that smartphones yield greater comfort by having all participants browse the same, relatively neutral content across devices (a particular page on Tumblr). We do not suggest, however, that this effect would hold across all types of content or activities. For example, in study 1 we show that people tend to derive greater comfort from their phone if they tend to use the device for more positive purposes such as social/entertainment, but derive relatively less comfort if they rely on it primarily for work. Future research could more directly test the boundaries of the effect, for example, by varying the valence of the content presented to participants across devices. Future work could also test for additional downstream consequences of the effects documented in this article—for example, whether the increased feeling of comfort associated with smartphone use will lead certain persuasive messaging (e.g., messages with more comforting language or ads for comfort-related products) to be particularly effective when targeted to users on their smartphones. Finally, if consumers are indeed more easily persuaded by certain messaging on their smartphone, as previous work suggests ( Pham et al. 2011 ), this can be seen as a potential threat to more vulnerable populations—most notably young people for whom the problem of “smartphone addiction” is seen as quite real ( Walsh et al. 2011 ). Another area for future research might thus be to investigate whether the factors that drive attachment for younger segments of the population differ from those driving attachment among older consumers.

The first author supervised the collection of and analyzed all of the data. Study 1 was an MTurk survey conducted in March 2019. Lab studies 2 and 3 were conducted in the behavioral lab of Columbia Business School. Study 2 was conducted in February through April 2017, and study 3 was conducted in June 2015, and then continued running in January through March 2016. Study 4 was conducted in the behavioral lab of the Wharton School of the University of Pennsylvania in June 2018. Study 5 (reported in the web appendix ) is an MTurk survey that was conducted in May 2016. All data can be accessed on the Open Science Framework at https://osf.io/z36ru/? view_only=03e059d0e7064bde89bc5bf01242bf73 .

This article is based on the first author’s doctoral dissertation completed under the second author’s guidance at Columbia University. The authors thank the other members of the dissertation committee—Jeffrey Inman, Robert Meyer, Oded Netzer, and Olivier Toubia—for their very useful input at various stages of this project. They also thank the Wharton Behavioral Lab and the various members of the Research on Emotions and Decisions (RED) lab at Columbia for their input on some of the studies.

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Health applications for mobile and wearable devices continue to experience tremendous growth both in the commercial and research sectors, but their impact on healthcare has yet to be fully realized. This commentary introduces three articles in a special issue that provides guidance on how to successfully address translational barriers to bringing mobile health technologies into clinical research and care. We also discuss how the cross-organizational sharing of data, software, and other digital resources can lower such barriers and accelerate progress across mobile health.

Introduction

Mobile devices have been a disruptive technology in many industries, but their impact on healthcare has yet to be fully realized. This is not due to a lack of interest. There are ~85,000 health apps 1 , 2 available for download, and over $8 billion was invested in “digital health” in 2018 3 . Novel miniaturized sensors are being developed to continuously detect biomarkers (e.g., from sweat 4 , tear fluid 5 ) that have traditionally been measured within a clinic. These developments are creating new possibilities for a vision of medicine that is more data-driven and personalized (e.g., ref. 6 ). In this article, we will refer to the use of such sensors and apps to collect personalized data for health in a ubiquitous manner as mobile health (mHealth).

As has been noted elsewhere 7 , 8 , data collection is only the first step in developing mHealth solutions that improve health outcomes. Clinicians and other stakeholders need to be convinced of the benefits of mHealth, and to-date it has been challenging to draw clear conclusions about the efficacy of these solutions, given the conflicting outcomes and heterogeneity in the implementation of mHealth interventions. This holds true whether assessing the impact of mHealth on hospital admission rates among patients with heart failure, adherence to prescribed rehabilitation exercises or lifestyle changes, or health outcomes like weight and blood pressure 9 , 10 . Myriad other factors, such as integration into the clinical or research study workflow, cost of implementation, usability of the device, and adequacy of privacy protections, also affect the likelihood of a solution transitioning from the prototype stage to routine use within research and clinics. Coming out of a multi-disciplinary workshop called mHealth Connect, three articles in this issue explicate some of these factors and provide guidance on how to successfully address translational barriers for different use cases. Specifically, the articles describe considerations when (1) selecting a suitable wearable sensor for a given application; (2) analyzing observational health behavior data generated by mHealth apps and devices; and (3) integrating these technologies into the clinical environment.

Their recommendations demonstrate the critical role data has in this new paradigm, so in addition to introducing the three articles, this paper calls for cross-organizational sharing of digital resources to accelerate progress within mHealth. Drawing on examples from other biomedical domains, we describe the positive impacts of sharing for three different types of resources and identify early efforts to encourage this behavior within mHealth. Thus, the insights offered through this and the other three articles in this issue can catalyze diverse activities to bring mHealth capabilities into clinical research and care.

mHealth Connect workshop

Despite the growing body of literature on consumer-oriented mHealth devices, there is a paucity of strong evidence for their benefit 9 . Few applications have made the leap from prototype to routine use for research or clinical purposes. mHealth Connect ( http://mobilize.stanford.edu/mhealthconnect/ ) was a workshop that brought together key stakeholder leaders across industry, clinical systems, and academia to collaboratively identify and overcome barriers to this translation. The workshop was launched in 2016 by two of the National Institutes of Health’s (NIH) Big Data to Knowledge (BD2K) Centers of Excellence–the Mobilize Center 11 and the Mobile-Sensor-to-Knowledge Center (MD2K) 12 —in response to concerns voiced by many BD2K researchers that many commercial mobile devices and apps on the market are poorly validated, without compelling clinical use cases, and are opaque and restrictive about data sharing. mHealth Connect enabled discussions around these and other critical issues to take place with a balance of stakeholders at the table and seeded collaborations to advance the field. The three mHealth papers in this issue arise from those discussions and the needs identified during them.

Scope of mHealth covered

While mHealth comprises a broad range of topics, as an outgrowth of two NIH Big Data to Knowledge Centers, mHealth Connect’s focus is on accelerating the use of data collected from mobile and wireless devices, such as wearable sensors, in clinical research and care. Because of the personal ubiquitous nature of mobile devices, the greatest new opportunity is in using mHealth to directly measure and improve patient health and health states outside the traditional confines of the hospital and clinic. The scope for this and the accompanying three papers thus excludes the following topics: (1) sensors and devices designed exclusively for the hospital or clinic setting and are intended solely to inform clinical decision-making (e.g., a Holter monitor, which would be excluded, versus AliveCor’s KardiaMobile device, which would be included), (2) strictly educational apps that are one-way channels for fixed media, (3) electronic health records (EHR) apps, and (4) apps for navigating the healthcare system (e.g., finding doctors, scheduling appointments) rather than for managing health or disease.

What these three papers do focus on are mobile apps and sensors used by patients in their daily lives to manage their health, with or without co-management by clinical team members or friends and family. These include devices measuring novel biomarkers, as well as consumer versions of traditional clinical equipment, such as blood pressure cuffs and spirometers, which an individual can use to collect measurements whenever and wherever they desire independent of clinical indications. The devices may be integrated into a clinical healthcare workflow, but they are not designed exclusively or primarily for that environment. The emphasis is on the availability of dynamic personalized data captured either passively or through active self-report, and the consequent value of this data for informing patient and clinician action to improve health and manage acute or chronic disease.

Guidelines for developing and deploying mHealth solutions

Recent years have seen a rise in resources providing guidelines to evaluate mHealth solutions, including from the U.S. Food and Drug Administration (FDA) 13 , 14 , 15 , 16 . Evaluation criteria assess a broad range of factors, including adherence to privacy laws, data security, interoperability with existing infrastructure and workflows, cost, usability, and validity of the content or intervention. Nascent efforts, such as Express Scripts’ planned digital health formulary, a list of approved digital health technologies to guide consumers and payers, are emerging to reinforce these guidelines 17 . While efforts to increase rigor in the evaluation of mHealth solutions are still taking shape, many questions remain on best practices and frameworks for mHealth development upstream of final regulatory or formulary approval.

The Clinical Trials Transformation Initiative (CTTI) provides one of the more comprehensive sets of guidelines for developing a mobile device-based solution, including the development of novel endpoints from mobile device data and the design of protocols that use mobile devices for data capture 18 . CTTI’s guidelines are intended for the relatively controlled conditions and limited durations of clinical trials, and therefore, necessarily exclude considerations for broad-scale clinical deployment. Nonetheless, they provide a useful path for individuals launching mHealth endeavors in general. Below we introduce a collection of articles based on our series of mHealth Connect meetings that augment existing guidelines provided by CTTI and others 18 , 19 , 20 .

Device selection for wellness, healthcare, and research applications

Regardless of the application, defining the target use case is critical for success. This definition is a fundamental tenet of many mHealth guidelines 18 , 19 , 20 , and it requires a process of user-centered design incorporating clinical, engineering, behavioral science, ethical, and disparities considerations (e.g., language, numeracy, literacy, and disabilities). All mHealth projects, even noncommercial ones, should have a clear business case detailing how continued use of the solution will be financially and logistically sustainable. The paper by Caulfield, et al. presents a framework for optimizing the match between sensors and classes of use cases, for refining the use case requirements, and then evaluating available devices against those requirements 21 .

Analysis of digital biomarkers for predictive models and unique insights

Digital biomarkers are clinically meaningful measures derived from mobile and wearable devices that correlate with or predict disease states. They can be analogues of traditional clinical quantities, such as heart rate, or novel indicators of health states. The full impact of mHealth comes from simulation or predictive models that combine digital biomarkers potentially with other data sources. An example is the cStress model, which blends real-time data streams on heart rate, heart rate variability, and interbeat interval data to derive a probability of stress in a given 1-min time window 22 . Developed using MD2K’s Cerebral Cortex, a cloud infrastructure for big data analysis of high-volume high-frequency data streams 12 , cStress utilized a prospective approach and actively recruited participants to collect data for its development.

Data analysis and model building can also be done retrospectively on observational datasets to gain insights that are challenging to obtain through traditional studies. In some cases, these datasets contain upwards of hundreds of thousands of individuals, enabling analysis about health and behavior on an unprecedented scale 23 , 24 . While such datasets can be a windfall, they present their own set of unique challenges for obtaining reliable results. The paper by Hicks, et al. presents a set of best practices for analyzing these large-scale, observational digital biomarker datasets from commercial personal technologies 25 .

Deploying mHealth solutions within clinical care

The necessity of a well-defined use case and business case becomes especially evident when it comes to the adoption and scaling of a mHealth solution. Is the mobile technology to be used by people with or without their clinicians? Is the intent to deploy locally in one care setting or to scale to global use? Particularly where clinician use is envisioned, integration into the clinical workflow is a prerequisite for adoption. To help guide expansion of mHealth technology into clinical care delivery, the paper by Smuck, et al. presents common factors driving successful utilization of wearables in the clinical care environment, as shown by two examples 26 .

Resource sharing to accelerate mHealth adoption

These papers aim to increase the likelihood of mHealth projects to achieve their aims, whether that is integrating mHealth technologies into the clinical workflow or developing a model to accurately predict health outcomes from mHealth data. The recommendations are intended to advance the work of individual groups, but they also point to opportunities for collective efforts that would advance activities across the entire community. In particular, we highlight the impact of sharing digital resources. Echoing Hicks, et al., we encourage “sharing models, software, datasets, and other digital resources whenever possible” 25 . Below we describe three categories of shared resources that can accelerate mHealth’s leap to research and the clinics: raw and processed data from devices; software and models used to analyze and interpret data; and evaluation results. We call attention to the positive impact the sharing of such resources has had in other biomedical domains and highlight initial efforts to bring these practices to mHealth.

The benefits of sharing experimental data, software, and models are well-delineated: enhanced transparency, the ability to more rapidly and easily extend existing efforts, and decreased duplication of efforts 27 , 28 . Large biomedical datasets that have been established specifically as research resources, such as the UK Biobank and the Osteoarthritis Initiative, have demonstrated the value of sharing, having supported hundreds of published research studies 29 , 30 . Smaller datasets from independent research labs can also positively impact a field. Previously shared data have served as benchmarks for comparing algorithms and aided in the validation of new models 31 , 32 , 33 . And pooling these datasets, such as in the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) project, has increased the statistical power of analyses and led to discoveries that would not have been possible from a single dataset alone 34 .

Such capabilities are critical for advancing mHealth. Having readily accessible datasets, both large and small, will facilitate the development of new digital biomarkers or robust mHealth-based predictive models, such as those described by Hicks, et al. We are starting to see some organized efforts to promote data sharing among the mHealth community. The International Children’s Accelerometry Database (ICAD) has created a pooled dataset, similar to ENIGMA 35 . Vivli, a platform for sharing data from clinical trials including trials with digital biomarkers, was launched in 2018 36 , and SimTK, a repository for the biocomputation and movement communities, recently added support for the sharing of mobile and other experimental data 37 .

Although invaluable, shared data alone will not propel mHealth applications into routine research and clinical use. Software methods and models are needed to glean insights from the data and thus, it is just as important that they also be shared, ideally with an open-source license to encourage modification and reuse for new applications. The programming language Python is a testament to the power of open-source with over 100,000 community-developed extensions 38 that make it a popular tool within bioinformatics and scientific computing. In biomechanics, researchers are sharing software for analyzing movement data within the open-source OpenSim simulation platform, extending the community’s ability to derive new insights 39 . While some mHealth software is being shared 40 , 41 , large, active communities have yet to develop around them. Initiatives, such as Open mHealth 42 as well as Shimmer and Nextbridge Exchange’s industry-based open-source effort to share analysis tools for wearable sensor data 43 , may help change the culture.

Similar initiatives would be useful throughout the mHealth development process, including the sharing of evaluation results, for example when evaluating devices during study design, as described by Caulfield, et al. 21 , and also when developing a reimbursement model to implement wearable technology into patient care, as mentioned by Smuck, et al. 26 . If individuals made their evaluation results available for others to leverage, we could appreciably streamline these processes. The CTTI Feasibility Studies Database 44 is a step towards this. The database compiles a list of devices, along with relevant evaluation criteria such as outcome measures and sample size, from publications examining the feasibility of mHealth in clinical trials. In a similar vein, the Digital Medicine Society provides a crowdsourced library of digital endpoints being used in industry-sponsored studies 45 . While there are some concerns about resource sharing—for example, potential misuse of shared resources and privacy breaches—technological and policy solutions can be implemented to mitigate them 46 , 47 . Compiling mHealth knowledge, data, and methods with such safeguards will accelerate the widespread adoption of mHealth for research and clinical care, and we urge individuals to contribute to such efforts.

It has been 13 years since the first iPhone was released, and 11 years since the first FitBit. In the intervening years, smartphone adoption has skyrocketed, fitness bands and smartwatches are commonplace, and “mobile health” NIH grants have grown from tens per year to over 610 in 2019. It has been said that digital health is now at “the end of the beginning” 48 . The mHealth Connect events have highlighted ways to go beyond the beginning: develop cross-disciplinary collaborations, pay attention to purpose, and consider factors beyond the technology itself. The papers in this series are intended as a guide for mHealth’s journey ahead and highlight ways in which we can collectively accelerate our progress along the path to clinical research and care.

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• Raúl J. Chang-Tam   ORCID: orcid.org/0000-0001-7812-3893 11 , 13 ,
• Pedro R. Palos-Sánchez   ORCID: orcid.org/0000-0001-9966-0698 12 , 13 &
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Advances in technology have facilitated and generated confidence in the use of mobile payment in new economic environments becomes a basic requirement for carrying out commercial activities. Objective: Present the results of a literature analysis conducted to evaluate the adoption of smartphone technology as a business strategy. What factors influence people in the decision to use the cell phone as a means of payment. The continuous improvements and emergence of ecosystems ranging from IT service providers, financiers, distributors, marketers and retailers who see potential in using mobile payment for economic development. Method: To determine the various factors that influence or inhibit the acceptance of payment technology, a Systematic Literature Review (SLR) was carried out, revealing expectations of performance and usefulness, significant determinants for using the mobile phone as a means of payment motivated by the ease of use. Results: The selection of 63 articles was the result according to the established criteria. Only 40 address specific strategic aspects of business and mobile payment in the evaluated context. Additionally, security in open connectivity service platforms was found to be a major inhibitor to strategic use in businesses using mobile payment. Conclusions: The approaches found are mostly related to the mobile payment transaction and only partially cover strategic business use as a business technological instrument.

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Chang-Tam, R.J., Palos-Sánchez, P.R., Fernández, J.A.F. (2024). New Economic Trends and Adoption of Mobile Payments: A Systematic Review of the Literature. In: Alareeni, B., Hamdan, A. (eds) Navigating the Technological Tide: The Evolution and Challenges of Business Model Innovation. ICBT 2024. Lecture Notes in Networks and Systems, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-031-67444-0_47

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Mobile energy-storage technology in power grid: a review of models and applications.

1. Introduction

2. mess modeling, 2.1. mobility model.

2.2. Battery Energy Model

3. grid application of mess, 3.1. mess planning, 3.2. mess operation.

3.3. Business Model

4. research and application prospect, 4.1. modeling and solution of mess operation problem, 4.2. comprehensive application of mess in power grids, 4.3. business model, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Lu, Z.; Xu, X.; Yan, Z.; Han, D.; Xia, S. Mobile Energy-Storage Technology in Power Grid: A Review of Models and Applications. Sustainability 2024 , 16 , 6857. https://doi.org/10.3390/su16166857

Lu Z, Xu X, Yan Z, Han D, Xia S. Mobile Energy-Storage Technology in Power Grid: A Review of Models and Applications. Sustainability . 2024; 16(16):6857. https://doi.org/10.3390/su16166857

Lu, Zhuoxin, Xiaoyuan Xu, Zheng Yan, Dong Han, and Shiwei Xia. 2024. "Mobile Energy-Storage Technology in Power Grid: A Review of Models and Applications" Sustainability 16, no. 16: 6857. https://doi.org/10.3390/su16166857

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