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Review & Tutorial Papers

Purpose and scope.

The IEEE IoT Journal (IoT-J) , launched in 2014 (“ Genesis of the IoT-J “), publishes papers on the latest advances, as well as review articles, on the various aspects of IoT. Topics include IoT system architecture, IoT enabling technologies, IoT communication and networking protocols, IoT services and applications, and the social implications of IoT. Examples are IoT demands, impacts, and implications on sensors technologies, big data management, and future internet design for various IoT use cases, such as smart cities, smart environments, smart homes, etc. The fields of interest include:

  • IoT architectures such as things-centric, data-centric, service-centric architecture, CPS and SCADA platforms, future Internet design for IoT, cloud-based IoT, and system security and manageability.
  • IoT enabling technologies such as sensors, radio frequency identification, low power and energy harvesting, sensor networks, machine-type communication, resource-constrained networks, real-time systems, IoT data analytics, in situ processing, and embedded software.
  • IoT services, applications, standards, and test-beds such as streaming data management and mining platforms, service middleware, open service platform, semantic service management, security and privacy-preserving protocols, design examples of smart services and applications, and IoT application support.

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Nei Kato, Tohoku University, Japan (Email: [email protected] )

  • Survey Paper
  • Open access
  • Published: 09 December 2019

Internet of Things is a revolutionary approach for future technology enhancement: a review

  • Sachin Kumar   ORCID: orcid.org/0000-0003-3949-0302 1 ,
  • Prayag Tiwari 2 &
  • Mikhail Zymbler 1  

Journal of Big Data volume  6 , Article number:  111 ( 2019 ) Cite this article

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Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.

Introduction

The Internet of Things (IoT) is an emerging paradigm that enables the communication between electronic devices and sensors through the internet in order to facilitate our lives. IoT use smart devices and internet to provide innovative solutions to various challenges and issues related to various business, governmental and public/private industries across the world [ 1 ]. IoT is progressively becoming an important aspect of our life that can be sensed everywhere around us. In whole, IoT is an innovation that puts together extensive variety of smart systems, frameworks and intelligent devices and sensors (Fig.  1 ). Moreover, it takes advantage of quantum and nanotechnology in terms of storage, sensing and processing speed which were not conceivable beforehand [ 2 ]. Extensive research studies have been done and available in terms of scientific articles, press reports both on internet and in the form of printed materials to illustrate the potential effectiveness and applicability of IoT transformations. It could be utilized as a preparatory work before making novel innovative business plans while considering the security, assurance and interoperability.

figure 1

General architecture of IoT

A great transformation can be observed in our daily routine life along with the increasing involvement of IoT devices and technology. One such development of IoT is the concept of Smart Home Systems (SHS) and appliances that consist of internet based devices, automation system for homes and reliable energy management system [ 3 ]. Besides, another important achievement of IoT is Smart Health Sensing system (SHSS). SHSS incorporates small intelligent equipment and devices to support the health of the human being. These devices can be used both indoors and outdoors to check and monitor the different health issues and fitness level or the amount of calories burned in the fitness center etc. Also, it is being used to monitor the critical health conditions in the hospitals and trauma centers as well. Hence, it has changed the entire scenario of the medical domain by facilitating it with high technology and smart devices [ 4 , 5 ]. Moreover, IoT developers and researchers are actively involved to uplift the life style of the disabled and senior age group people. IoT has shown a drastic performance in this area and has provided a new direction for the normal life of such people. As these devices and equipment are very cost effective in terms of development cost and easily available within a normal price range, hence most of the people are availing them [ 6 ]. Thanks to IoT, as they can live a normal life. Another important aspect of our life is transportation. IoT has brought up some new advancements to make it more efficient, comfortable and reliable. Intelligent sensors, drone devices are now controlling the traffic at different signalized intersections across major cities. In addition, vehicles are being launched in markets with pre-installed sensing devices that are able to sense the upcoming heavy traffic congestions on the map and may suggest you another route with low traffic congestion [ 7 ]. Therefore IoT has a lot to serve in various aspects of life and technology. We may conclude that IoT has a lot of scope both in terms of technology enhancement and facilitate the humankind.

IoT has also shown its importance and potential in the economic and industrial growth of a developing region. Also, in trade and stock exchange market, it is being considered as a revolutionary step. However, security of data and information is an important concern and highly desirable, which is a major challenging issue to deal with [ 5 ]. Internet being a largest source of security threats and cyber-attacks has opened the various doors for hackers and thus made the data and information insecure. However, IoT is committed to provide the best possible solutions to deal with security issues of data and information. Hence, the most important concern of IoT in trade and economy is security. Therefore, the development of a secure path for collaboration between social networks and privacy concerns is a hot topic in IoT and IoT developers are working hard for this.

The remaining part of the article is organized as follows: “ Literature survey ” section will provide state of art on important studies that addressed various challenges and issues in IoT. “ IoT architecture and technologies ” section discussed the IoT functional blocks, architecture in detail. In “ Major key issues and challenges of IoT ” section, important key issues and challenges of IoT is discussed. “ Major IoT applications ” section provides emerging application domains of IoT. In “ Importance of big data analytics in IoT ” section, the role and importance of big data and its analysis is discussed. Finally, the article concluded in “ Conclusions ” section.

Literature survey

IoT has a multidisciplinary vision to provide its benefit to several domains such as environmental, industrial, public/private, medical, transportation etc. Different researchers have explained the IoT differently with respect to specific interests and aspects. The potential and power of IoT can be seen in several application domains. Figure  2 illustrates few of the application domains of IoTs potentials.

figure 2

Some of the potential application domains of IoT

Various important IoT projects have taken charge over the market in last few years. Some of the important IoT projects that have captured most of the market are shown in Fig.  3 . In Fig.  3 , a global distribution of these IoT projects is shown among American, European and Asia/Pacific region. It can be seen that American continent are contributing more in the health care and smart supply chain projects whereas contribution of European continent is more in the smart city projects [ 8 ].

figure 3

Global distribution of IoT projects among America (USA, South America and Canada), Europe and APAC (Asia and Pacific region) [ 8 ]

Figure  4 , illustrates the global market share of IoT projects worldwide [ 8 ]. It is evident that industry, smart city, smart energy and smart vehicle based IoT projects have a big market share in comparison to others.

figure 4

Global share of IoT projects across the world [ 8 ]

Smart city is one of the trendy application areas of IoT that incorporates smart homes as well. Smart home consists of IoT enabled home appliances, air-conditioning/heating system, television, audio/video streaming devices, and security systems which are communicating with each other in order to provide best comfort, security and reduced energy consumption. All this communication takes place through IoT based central control unit using Internet. The concept of smart city gained popularity in the last decade and attracted a lot of research activities [ 9 ]. The smart home business economy is about to cross the 100 billion dollars by 2022 [ 10 ]. Smart home does not only provide the in-house comfort but also benefits the house owner in cost cutting in several aspects i.e. low energy consumption will results in comparatively lower electricity bill. Besides smart homes, another category that comes within smart city is smart vehicles. Modern cars are equipped with intelligent devices and sensors that control most of the components from the headlights of the car to the engine [ 11 ]. The IoT is committed towards developing a new smart car systems that incorporates wireless communication between car-to-car and car-to-driver to ensure predictive maintenance with comfortable and safe driving experience [ 12 ].

Khajenasiri et al. [ 10 ] performed a survey on the IoT solutions for smart energy control to benefit the smart city applications. They stated that at present IoT has been deployed in very few application areas to serve the technology and people. The scope of IoT is very wide and in near future IoT is able to capture almost all application areas. They mentioned that energy saving is one of the important part of the society and IoT can assist in developing a smart energy control system that will save both energy and money. They described an IoT architecture with respect to smart city concept. The authors also discussed that one of the challenging task in achieving this is the immaturity of IoT hardware and software. They suggested that these issues must be resolved to ensure a reliable, efficient and user friendly IoT system.

Alavi et al. [ 13 ] addressed the urbanization issue in the cities. The movement of people from rural to urban atmosphere resulting in growing population of the cities. Therefore, there is a need to provide smart solutions for mobility, energy, healthcare and infrastructure. Smart city is one of the important application areas for IoT developers. It explores several issues such as traffic management, air quality management, public safety solutions, smart parking, smart lightning and smart waste collection (Fig.  5 ). They mentioned that IoT is working hard to tackle these challenging issues. The need for improved smart city infrastructure with growing urbanization has opened the doors for entrepreneurs in the field of smart city technologies. The authors concluded that IoT enabled technology is very important for the development of sustainable smart cities.

figure 5

Potential IoT application areas for smart cities

Another important issue of IoT that requires attention and a lot of research is security and privacy. Weber [ 14 ] focused on these issues and suggested that a private organization availing IoT must incorporate data authentication, access control, resilience to attacks and client privacy into their business activities that would be an additional advantage. Weber suggested that in order to define global security and privacy issues, IoT developers must take into account the geographical limitations of the different countries. A generic framework needs to be designed to fit the global needs in terms of privacy and security. It is highly recommended to investigate and recognize the issues and challenges in privacy and security before developing the full fledge working IoT framework.

Later, Heer et al. [ 15 ] came up with a security issue in IP based IoT system. They mentioned that internet is backbone for the communication among devices that takes place in an IoT system. Therefore, security issues in IP based IoT systems are an important concern. In addition, security architecture should be designed considering the life cycle and capabilities of any object in the IoT system. It also includes the involvement of the trusted third party and the security protocols. The security architecture with scalability potential to serve the small-scale to large-scale things in IoT is highly desirable. The study pointed out that IoT gave rise to a new way of communication among several things across the network therefore traditional end to end internet protocol are not able to provide required support to this communication. Therefore, new protocols must be designed considering the translations at the gateways to ensure end-to-end security. Moreover, all the layers responsible for communication has their own security issues and requirements. Therefore, satisfying the requirements for one particular layers will leave the system into a vulnerable state and security should be ensured for all the layers.

Authentication and access control is another issue in IoT that needs promising solutions to strengthen the security. Liu et al. [ 16 ] brought up a solution to handle authentication and access control. Authentication is very important to verify the communicating parties to prevent the loss of confidential information. Liu et al. [ 16 ] provided an authentication scheme based on Elliptic Curve Cryptosystem and verified it on different security threats i.e. eavesdropping, man-in-the-middle attack, key control and replay attack. They claimed that there proposed schemes are able to provide better authentication and access control in IoT based communication. Later, Kothmayr et al. [ 17 ] proposed a two-way authentication scheme based of datagram transport layer security (DTLS) for IoT. The attackers over the internet are always active to steal the secured information. The proposed approach are able to provide message security, integrity, authenticity and confidentiality, memory overhead and end-to-end latency in the IoT based communication network.

Li et al. [ 18 ] proposed a dynamic approach for data centric IoT applications with respect to cloud platforms. The need of an appropriate device, software configuration and infrastructure requires efficient solutions to support massive amount of IoT applications that are running on cloud platforms. IoT developers and researchers are actively engaged in developing solutions considering both massive platforms and heterogeneous nature of IoT objects and devices. Olivier et al. [ 19 ] explained the concept of software defined networking (SDN) based architecture that performs well even if a well-defined architecture is not available. They proposed that SDN based security architecture is more flexible and efficient for IoT.

Luk et al. [ 20 ] stated that the main task of a secure sensor network (SSN) is to provide data privacy, protection from replay attacks and authentication. They discussed two popular SSN services namely TinySec [ 21 ] and ZigBee [ 22 ]. They mentioned that although both the SSN services are efficient and reliable, however, ZigBee is comparatively provides higher security but consumes high energy whereas TinySec consumes low energy but not as highly secured as ZigBee. They proposed another architecture MiniSec to support high security and low energy consumption and demonstrated its performance for the Telos platform. Yan et al. [ 23 ] stated that trust management is an important issue in IoT. Trust management helps people to understand and trust IoT services and applications without worrying about uncertainty issues and risks [ 24 ]. They investigated different issues in trust management and discussed its importance with respect to IoT developers and users.

Noura et al. [ 25 ] stated the importance of interoperability in IoT as it allows integration of devices, services from different heterogeneous platforms to provide the efficient and reliable service. Several other studies focused on the importance of interoperability and discussed several challenges that interoperability issue is facing in IoT [ 26 , 27 , 28 ]. Kim et al. [ 29 ] addressed the issue of climate change and proposed an IoT based ecological monitoring system. They mentioned that existing approaches are time consuming and required a lot of human intervention. Also, a routine visit is required to collect the information from the sensors installed at the site under investigation. Also, some information remained missing which leads to not highly accurate analysis. Therefore, IoT based framework is able to solve this problem and can provide high accuracy in analysis and prediction. Later, Wang et al. [ 30 ] shows their concern for domestic waste water treatment. They discussed several deficiencies in the process of waste water treatment and dynamic monitoring system and suggested effective solutions based on IoT. They stated that IoT can be very effective in the waste water treatment and process monitoring.

Agriculture is one of the important domain around the world. Agriculture depends on several factors i.e. geographical, ecological etc. Qiu et al. [ 31 ] stated that technology that is being used for ecosystem control is immature with low intelligence level. They mentioned that it could be a good application area for IoT developers and researchers.

Qiu et al. [ 31 ] proposed an intelligent monitoring platform framework for facility agriculture ecosystem based on IoT that consists of four layer mechanism to manage the agriculture ecosystem. Each layer is responsible for specific task and together the framework is able to achieve a better ecosystem with reduced human intervention.

Another important concern around the world is climate change due to global warming. Fang et al. [ 32 ] introduced an integrated information system (IIS) that integrates IoT, geo-informatics, cloud computing, global positioning system (GPS), geographical information system (GIS) and e-science in order to provide an effective environmental monitoring and control system. They mentioned that the proposed IIS provides improved data collection, analysis and decision making for climate control. Air pollution is another important concern worldwide. Various tools and techniques are available to air quality measures and control. Cheng et al. [ 33 ] proposed AirCloud which is a cloud based air quality and monitoring system. They deployed AirCloud and evaluated its performance using 5 months data for the continuous duration of 2 months.

Temglit et al. [ 34 ] considered Quality of Service (QoS) as an important challenge and a complex task in evaluation and selection of IoT devices, protocols and services. QoS is very important criteria to attract and gain trust of users towards IoT services and devices. They came up with an interesting distributed QoS selection approach. This approach was based on distributed constraint optimization problem and multi-agent paradigm. Further, the approach was evaluated based on several experiments under realistic distributed environments. Another important aspect of IoT is its applicability to the environmental and agriculture standards. Talavera et al. [ 35 ] focused in this direction and presented the fundamental efforts of IoT for agro-industrial and environmental aspects in a survey study. They mentioned that the efforts of IoT in these areas are noticeable. IoT is strengthening the current technology and benefiting the farmers and society. Jara et al. [ 36 ] discussed the importance of IoT based monitoring of patients health. They suggested that IoT devices and sensors with the help of internet can assist health monitoring of patients. They also proposed a framework and protocol to achieve their objective. Table 1 provides a summary of the important studies and the direction of research with a comparison of studies on certain evaluation parameters.

IoT architecture and technologies

The IoT architecture consists of five important layers that defines all the functionalities of IoT systems. These layers are perception layer, network layer, middleware layer, application layer, business layer. At the bottom of IoT architecture, perception layer exists that consists of physical devices i.e. sensors, RFID chips, barcodes etc. and other physical objects connected in IoT network. These devices collects information in order to deliver it to the network layer. Network layer works as a transmission medium to deliver the information from perception layer to the information processing system. This transmission of information may use any wired/wireless medium along with 3G/4G, Wi-Fi, Bluetooth etc. Next level layer is known as middleware layer. The main task of this layer is to process the information received from the network layer and make decisions based on the results achieved from ubiquitous computing. Next, this processed information is used by application layer for global device management. On the top of the architecture, there is a business layer which control the overall IoT system, its applications and services. The business layer visualizes the information and statistics received from the application layer and further used this knowledge to plan future targets and strategies. Furthermore, the IoT architectures can be modified according to the need and application domain [ 19 , 20 , 37 ]. Besides layered framework, IoT system consists of several functional blocks that supports various IoT activities such as sensing mechanism, authentication and identification, control and management [ 38 ]. Figure  6 illustrates such functional blocks of IoT architecture.

figure 6

A generic function module of IoT system

There are several important functional blocks responsible for I/O operations, connectivity issues, processing, audio/video monitoring and storage management. All these functional block together incorporates an efficient IoT system which are important for optimum performance. Although, there are several reference architectures proposed with the technical specifications, but these are still far from the standard architecture that is suitable for global IoT [ 39 ]. Therefore, a suitable architecture is still needsvk to be designed that could satisfy the global IoT needs. The generic working structure of IoT system is shown in Fig.  7 . Figure  7 shows a dependency of IoT on particular application parameters. IoT gateways have an important role in IoT communication as it allows connectivity between IoT servers and IoT devices related to several applications [ 40 ].

figure 7

Working structure of IoT

Scalability, modularity, interoperability and openness are the key design issues for an efficient IoT architecture in a heterogenous environment. The IoT architecture must be designed with an objective to fulfil the requirements of cross domain interactions, multi-system integration with the potential of simple and scalable management functionalities, big data analytics and storage, and user friendly applications. Also, the architecture should be able to scaleup the functionality and add some intelligence and automation among the IoT devices in the system.

Moreover, increasing amount of massive data being generated through the communication between IoT sensors and devices is a new challenge. Therefore, an efficient architecture is required to deal with massive amount of streaming data in IoT system. Two popular IoT system architectures are cloud and fog/edge computing that supports with the handling, monitoring and analysis of huge amount of data in IoT systems. Therefore, a modern IoT architecture can be defined as a 4 stage architecture as shown in Fig.  8 .

figure 8

Four stage IoT architecture to deal with massive data

In stage 1 of the architecture, sensors and actuators plays an important role. Real world is comprised of environment, humans, animals, electronic gadgets, smart vehicles, and buildings etc. Sensors detect the signals and data flow from these real world entities and transforms into data which could further be used for analysis. Moreover, actuators is able to intervene the reality i.e. to control the temperature of the room, to slow down the vehicle speed, to turn off the music and light etc. Therefore, stage 1 assist in collecting data from real world which could be useful for further analysis. Stage 2 is responsible to collaborate with sensors and actuators along with gateways and data acquisition systems. In this stage, massive amount of data generated in stage 1 is aggregated and optimized in a structured way suitable for processing. Once the massive amount of data is aggregated and structured then it is ready to be passed to stage 3 which is edge computing. Edge computing can be defined as an open architecture in distributed fashion which allows use of IoT technologies and massive computing power from different locations worldwide. It is very powerful approach for streaming data processing and thus suitable for IoT systems. In stage 3, edge computing technologies deals with massive amount of data and provides various functionalities such as visualization, integration of data from other sources, analysis using machine learning methods etc. The last stage comprises of several important activities such as in depth processing and analysis, sending feedback to improve the precision and accuracy of the entire system. Everything at this stage will be performed on cloud server or data centre. Big data framework such as Hadoop and Spark may be utilized to handle this large streaming data and machine learning approaches can be used to develop better prediction models which could help in a more accurate and reliable IoT system to meet the demand of present time.

Major key issues and challenges of IoT

The involvement of IoT based systems in all aspects of human lives and various technologies involved in data transfer between embedded devices made it complex and gave rise to several issues and challenges. These issues are also a challenge for the IoT developers in the advanced smart tech society. As technology is growing, challenges and need for advanced IoT system is also growing. Therefore, IoT developers need to think of new issues arising and should provide solutions for them.

Security and privacy issues

One of the most important and challenging issues in the IoT is the security and privacy due to several threats, cyber attacks, risks and vulnerabilities [ 41 ]. The issues that give rise to device level privacy are insufficient authorization and authentication, insecure software, firmware, web interface and poor transport layer encryption [ 42 ]. Security and privacy issues are very important parameters to develop confidence in IoT Systems with respect to various aspects [ 43 ]. Security mechanisms must be embedded at every layer of IoT architecture to prevent security threats and attacks [ 23 ]. Several protocols are developed and efficiently deployed on every layer of communication channel to ensure the security and privacy in IoT based systems [ 44 , 45 ]. Secure Socket Layer (SSL) and Datagram Transport Layer Security (DTLS) are one of the cryptographic protocols that are implemented between transport and application layer to provide security solutions in various IoT systems [ 44 ]. However, some IoT applications require different methods to ensure the security in communication between IoT devices. Besides this, if communication takes place using wireless technologies within the IoT system, it becomes more vulnerable to security risks. Therefore, certain methods should be deployed to detect malicious actions and for self healing or recovery. Privacy on the other hand is another important concern which allows users to feel secure and comfortable while using IoT solutions. Therefore, it is required to maintain the authorization and authentication over a secure network to establish the communication between trusted parties [ 46 ]. Another issue is the different privacy policies for different objects communicating within the IoT system. Therefore, each object should be able to verify the privacy policies of other objects in IoT system before transmitting the data.

Interoperability/standard issues

Interoperability is the feasibility to exchange the information among different IoT devices and systems. This exchange of information does not rely on the deployed software and hardware. The interoperability issue arises due to the heterogeneous nature of different technology and solutions used for IoT development. The four interoperability levels are technical, semantic, syntactic and organizational [ 47 ]. Various functionalities are being provided by IoT systems to improve the interoperability that ensures communication between different objects in a heterogeneous environment. Additionally, it is possible to merge different IoT platforms based on their functionalities to provide various solutions for IoT users [ 48 ]. Considering interoperability an important issue, researchers approved several solutions that are also know as interoperability handling approaches [ 49 ]. These solutions could be adapaters/gateways based, virtual networks/overlay based, service oriented architecture based etc. Although interoperability handling approaches ease some pressure on IoT systems but there are still certain challenges remain with interoperability that could be a scope for future studies [ 25 ].

Ethics, law and regulatory rights

Another issue for IoT developers is the ethics, law and regulatory rights. There are certain rules and regulations to maintain the standard, moral values and to prevent the people from violating them. Ethics and law are very similar term with the only difference is that ethics are standards that people believes and laws are certain restrictions decided by the government. However, both ethics and laws are designed to maintain the standard, quality and prevent people from illegal use. With the development of IoT, several real life problems are solved but it has also given rise to critical ethical and legal challenges [ 50 ]. Data security, privacy protection, trust and safety, data usability are some of those challenges. It has also been observed that majority of IoT users are supporting government norms and regulations with respect to data protection, privacy and safety due to the lack of trust in IoT devices. Therefore, this issue must be taken into consideration to maintain and improve the trust among people for the use of IoT devices and systems.

Scalability, availability and reliability

A system is scalable if it is possible to add new services, equipments and devices without degrading its performance. The main issue with IoT is to support a large number of devices with different memory, processing, storage power and bandwidth [ 28 ]. Another important issue that must be taken into consideration is the availability. Scalability and availability both should be deployed together in the layered framework of IoT. A great example of scalability is cloud based IoT systems which provide sufficient support to scale the IoT network by adding up new devices, storage and processing power as required.

However, this global distributed IoT network gives rise to a new research paradigm to develop a smooth IoT framework that satisfy global needs [ 51 ]. Another key challenge is the availability of resources to the authentic objects regardless of their location and time of the requirement. In a distributed fashion, several small IoT networks are timely attached to the global IoT platforms to utilize their resources and services. Therefore, availability is an important concern [ 52 ]. Due to the use of different data transmission channels i.e. satellite communication, some services and availability of resources may be interrupted. Therefore, an independent and reliable data transmission channel is required for uninterrupted availability of resources and services.

Quality of Service (QoS)

Quality of Service (QoS) is another important factor for IoT. QoS can be defined as a measure to evaluate the quality, efficiency and performance of IoT devices, systems and architecture [ 34 ]. The important and required QoS metrics for IoT applications are reliability, cost, energy consumption, security, availability and service time [ 53 ]. A smarter IoT ecosystem must fulfill the requirements of QoS standards. Also, to ensure the reliability of any IoT service and device, its QoS metrics must be defined first. Further, users may also be able to specifiy their needs and requirements accordingly. Several approaches can be deployed for QoS assessment, however as mentioned by White et al. [ 54 ] there is a trade-off between quality factors and approaches. Therefore, good quality models must be deployed to overcome this trade-off. There are certain good quality models available in literature such as ISO/IEC25010 [ 55 ] and OASIS-WSQM [ 56 ] which can be used to evaluate the approaches used for QoS assessment. These models provides a wide range of quality factors that is quite sufficient for QoS assessment for IoT services. Table  2 summarizes the different studies with respect to IoT key challenges and issues discussed above.

Major IoT applications

Emerging economy, environmental and health-care.

IoT is completely devoted to provide emerging public and financial benefits and development to the society and people. This includes a wide range of public facilities i.e. economic development, water quality maintenance, well-being, industrialization etc. Overall, IoT is working hard to accomplish the social, health and economic goals of United Nations advancement step. Environmental sustainability is another important concern. IoT developers must be concerned about environmental impact of the IoT systems and devices to overcome the negative impact [ 48 ]. Energy consumption by IoT devices is one of the challenges related to environmental impact. Energy consumption is increasing at a high rate due to internet enabled services and edge cutting devices. This area needs research for the development of high quality materials in order to create new IoT devices with lower energy consumption rate. Also, green technologies can be adopted to create efficient energy efficient devices for future use. It is not only environmental friendly but also advantageous for human health. Researchers and engineers are engaged in developing highly efficient IoT devices to monitor several health issues such as diabetes, obesity or depression [ 57 ]. Several issues related to environment, energy and healthcare are considered by several studies.

Smart city, transport and vehicles

IoT is transforming the traditional civil structure of the society into high tech structure with the concept of smart city, smart home and smart vehicles and transport. Rapid improvements are being done with the help of supporting technologies such as machine learning, natural language processing to understand the need and use of technology at home [ 58 ]. Various technologies such as cloud server technology, wireless sensor networks that must be used with IoT servers to provide an efficient smart city. Another important issue is to think about environmental aspect of smart city. Therefore, energy efficient technologies and Green technologies should also be considered for the design and planning of smart city infrastructure. Further, smart devices which are being incorporated into newly launched vehicles are able to detect traffic congestions on the road and thus can suggest an optimum alternate route to the driver. This can help to lower down the congestion in the city. Furthermore, smart devices with optimum cost should be designed to be incorporated in all range vehicles to monitor the activity of engine. IoT is also very effective in maintaining the vehicle’s health. Self driving cars have the potential to communicate with other self driving vehicles by the means of intelligent sensors. This would make the traffic flow smoother than human-driven cars who used to drive in a stop and go manner. This procedure will take time to be implemented all over the world. Till the time, IoT devices can help by sensing traffic congestion ahead and can take appropriate actions. Therefore, a transport manufacturing company should incorporate IoT devices into their manufactured vehicles to provide its advantage to the society.

Agriculture and industry automation

The world’s growing population is estimated to reach approximate 10 billion by 2050. Agriculture plays an important role in our lives. In order to feed such a massive population, we need to advance the current agriculture approaches. Therefore, there is a need to combine agriculture with technology so that the production can be improved in an efficient way. Greenhouse technology is one of the possible approaches in this direction. It provides a way to control the environmental parameters in order to improve the production. However, manual control of this technology is less effective, need manual efforts and cost, and results in energy loss and less production. With the advancement of IoT, smart devices and sensors makes it easier to control the climate inside the chamber and monitor the process which results in energy saving and improved production (Fig.  9 ). Automatization of industries is another advantage of IoT. IoT has been providing game changing solutions for factory digitalization, inventory management, quality control, logistics and supply chain optimization and management.

figure 9

A working structure of IoT system in agriculture production

Importance of big data analytics in IoT

An IoT system comprises of a huge number of devices and sensors that communicates with each other. With the extensive growth and expansion of IoT network, the number of these sensors and devices are increasing rapidly. These devices communicate with each other and transfer a massive amount of data over internet. This data is very huge and streaming every second and thus qualified to be called as big data. Continuous expansion of IoT based networks gives rise to complex issue such as management and collection of data, storage and processing and analytics. IoT big data framework for smart buildings is very useful to deal with several issues of smart buildings such as managing oxygen level, to measure the smoke/hazardous gases and luminosity [ 59 ]. Such framework is capable to collect the data from the sensors installed in the buildings and performs data analytics for decision making. Moreover, industrial production can be improved using an IoT based cyber physical system that is equipped with an information analysis and knowledge acquisition techniques [ 60 ]. Traffic congestion is an important issue with smart cities. The real time traffic information can be collected through IoT devices and sensors installed in traffic signals and this information can be analyzed in an IoT based traffic management system [ 61 ]. In healthcare analysis, the IoT sensors used with patients generate a lot of information about the health condition of patients every second. This large amount of information needs to be integrated at one database and must be processed in real time to take quick decision with high accuracy and big data technology is the best solution for this job [ 62 ]. IoT along with big data analytics can also help to transform the traditional approaches used in manufacturing industries into the modern one [ 63 ]. The sensing devices generates information which can be analyzed using big data approaches and may help in various decision making tasks. Furthermore, use of cloud computing and analytics can benefit the energy development and conservation with reduced cost and customer satisfaction [ 64 ]. IoT devices generate a huge amount of streaming data which needs to be stored effectively and needs further analysis for decision making in real time. Deep learning is very effective to deal with such a large information and can provide results with high accuracy [ 65 ]. Therefore, IoT, Big data analytics and Deep learning together is very important to develop a high tech society.

Conclusions

Recent advancements in IoT have drawn attention of researchers and developers worldwide. IoT developers and researchers are working together to extend the technology on large scale and to benefit the society to the highest possible level. However, improvements are possible only if we consider the various issues and shortcomings in the present technical approaches. In this survey article, we presented several issues and challenges that IoT developer must take into account to develop an improved model. Also, important application areas of IoT is also discussed where IoT developers and researchers are engaged. As IoT is not only providing services but also generates a huge amount of data. Hence, the importance of big data analytics is also discussed which can provide accurate decisions that could be utilized to develop an improved IoT system.

Availability of data and materials

Not applicable.

Abbreviations

Internet of Things

Quality of Service

Web of Things

Cloud of Things

Smart Home System

Smart Health Sensing System

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Acknowledgements

This work was financially supported by the Ministry of Education and Science of Russian Federation (government order 2.7905.2017/8.9).

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Kumar, S., Tiwari, P. & Zymbler, M. Internet of Things is a revolutionary approach for future technology enhancement: a review. J Big Data 6 , 111 (2019). https://doi.org/10.1186/s40537-019-0268-2

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The Internet of Things: Impact and Implications for Health Care Delivery

Jaimon t kelly.

1 Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia

2 Centre of Applied Health Economics, Griffith University, Brisbane, Australia

Katrina L Campbell

3 Metro North Hospital and Health Service, Brisbane, Australia

Enying Gong

4 School of Population and Global Health, The University of Melbourne, Melbourne, Australia

Paul Scuffham

Associated data.

Examples of Internet of Things devices that can support health service delivery.

Examples of how smart homes can improve health care delivery.

Scenarios where Internet of Things can be used to improve health system efficiency.

The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. The potential of IoT-based health care is expanded upon to theorize how IoT can improve the accessibility of preventative public health services and transition our current secondary and tertiary health care to be a more proactive, continuous, and coordinated system. Finally, this paper will deal with the potential issues that IoT-based health care generates, barriers to market adoption from health care professionals and patients alike, confidence and acceptability, privacy and security, interoperability, standardization and remuneration, data storage, and control and ownership. Corresponding enablers of IoT in current health care will rely on policy support, cybersecurity-focused guidelines, careful strategic planning, and transparent policies within health care organizations. IoT-based health care has great potential to improve the efficiency of the health system and improve population health.

Introduction

The challenges presented by an aging population with multiple chronic conditions are ubiquitous worldwide [ 1 ]. The medical, lifestyle, and personal health needs across aging populations will continue to place a burden on health care resources. Meeting these challenges requires a focus on empowering populations to self-manage their health through health innovation to improve well-being and attenuate health resource burden [ 2 ].

Background of Digital Devices and the Internet of Things

Entering the 2020 decade, more devices are connected to the internet than ever before, and this will continue to grow at a rapid trajectory. Worldwide, more than 21 billion devices have been estimated to be connected to the internet in 2020, which is 5 times the number of devices 4 years prior [ 3 ]. The Internet of Things (IoT) can be defined in its simplest scenario as a network that connects uniquely identifiable devices (or things ) to the internet, enabling them to collect, send, store, and receive data [ 4 ]. From a health care perspective, IoT can be considered as any device that can collect health-related data from individuals, including computing devices, mobile phones, smart bands and wearables, digital medications, implantable surgical devices, or other portable devices, which can measure health data and connect to the internet [ 5 ].

The growth of IoT technology has driven interest in a wide range of health practices to improve population health more specifically [ 6 ]. Recent reviews have overviewed the various services and applications of IoT in health care (eg, eHealth, mobile health [mHealth], ambient assisted living, semantic devices, wearable devices and smartphones, and community-based health care) [ 5 , 7 ]. These services have been detailed extensively and can have many applications across single condition and cluster condition management, including, for example, the ability to track and monitor health progress remotely by health care professionals, improve self-management of chronic conditions, assist in the early detection of abnormalities, fast-track symptom identification and clinical diagnoses, deliver early intervention, and improve adherence to prescriptions [ 8 ]. These applications can make better use of health care resources and provide quality and low-cost medical care.

Health Systems Are Changing

With the 2020 public health response to the novel COVID-19 pandemic to effectively shut down traditional modes of health service delivery worldwide, efforts to reduce implementation barriers to technology-supported health delivery highlight the potential to reframe traditional models of care into virtual and distance modalities [ 9 ]. In response, many countries have successfully implemented technology-supported services to maintain health care practices and social distancing [ 10 ]. As global leaders consider policies that potentially provide more access to technology-supported health services in response to (and considerations post) the current COVID-19 crisis, it is becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care in either a complementary or an alternative way during times of crisis or health epidemics [ 11 ].

This viewpoint paper will overview current technologies in health care, outline how IoT devices are improving health service delivery, and outline how IoT technologies can affect global health care in the next decade. This viewpoint paper also overviews how the disruption in health care from IoT can lead to improved access and equitable primary, secondary, and tertiary smart health care, which is more proactive, continuous, and coordinated.

IoT-Based Health Care Architecture

The architecture of IoT in health care delivery essentially consists of 3 basic layers [ 12 ]: (1) the perception layer, (2) the network layer, and (3) the application layer. It is not our intention to extensively detail these layers; however, a summary and the related health implications are provided in the following sections.

Perception Layer: Sensing Systems That Collect Data

Perception and identification technologies are the foundation of IoT. Sensors are devices that can perceive changes in an environment and can include, for example, radio frequency identification (RFID), infrared sensors, cameras, GPS, medical sensors, and smart device sensors. These sensors allow for comprehensive perception through object recognition, location recognition, and geographic recognition and can convert this information to digital signals, which is more convenient for network transmission [ 12 , 13 ]. Sensor technologies allow for treatments to be monitored in real time and facilitate the acquisition of a multitude of physiological parameters about a patient so that diagnoses and high-quality treatment can be fast-tracked. There are many examples of potentially lifesaving IoT sensor devices; however, not all devices are clinically tested or have been proved to be safe or effective. A summary of IoT devices that may support and improve health service delivery is provided in Multimedia Appendix 1 [ 14 - 47 ].

Network Layer: Data Communication and Storage

The network level of IoT technologies includes wired and wireless networks, which communicate and store processed (layer 1) information either locally or at a centralized location. Communication between things can occur over low, medium, and high frequencies, the latter being the predominant focus of IoT. These include short-range communication technologies, such as RFID, wireless sensor networks, Bluetooth, Zigbee, low-power Wi-Fi, and global system for mobile communications [ 12 ]. High-frequency fourth-generation (4G) cellular networks have seen even more communication potential, and evolving 5G networks are becoming more readily available and are expected to be a major driver of the growth of IoT applications for health care, with the potential to provide reliable connection up to thousands of devices at the same time [ 48 ].

Communicated data are stored locally (often decentralized) or sent to a centralized cloud server. Cloud-based computing to support the delivery of health services has many benefits, as it is ubiquitous, flexible, and scalable in terms of data acquisition, storage, and transmission between devices connected to the cloud [ 49 ]. The use of the cloud can be foreseen to support data-intensive electronic medical records (EMRs), patient portals, medical IoT devices (which can include smartphone apps), and the big data analytics driving decision support systems and therapeutic strategies [ 5 ]. However, with more cloud apps entering the health market, it is just as important that an evidence base supports its effectiveness and safety and can deal with the security of health data and the reliability and transparency of that data by third parties. Furthermore, it has been suggested that centralized cloud storage will present issues in the future to users, such as excessive data accumulation and latency because of the distance between IoT devices and data centers.

Decentralized data processing and networking approaches may improve the scalability of IoT in health care. Edge cloud is a newer cloud computing concept that allows IoT sensors and network gateways to process and analyze data themselves (ie, at the edge ) in a decentralized fashion, reducing the amount of data required to be communicated and managed at a centralized location [ 12 , 50 ]. Similarly, blockchain storage uses a decentralized approach to data storage, creating independent blocks containing individual sets of information, which forms a dependent link in a collective block, which in turn creates a network regulated by patients rather than a third party [ 51 ]. There are examples of platforms engineering blockchain for medical practice already [ 51 , 52 ]; however, research on edge cloud and blockchains in health care is still limited and is an important area for future research.

Application Layer

The application layer interprets and applies data and is responsible for delivering application-specific services to the user [ 12 ]. Some of the most promising medical applications that IoT provides are through artificial intelligence (AI). The scientific applications of AI have proliferated, including image analysis, text recognition with natural language processing, drug activity design, and prediction of gene mutation expression [ 53 ]. AI has the capability to read available EMR data, including medical history, physical, laboratory, imaging, and medications, and contextualize these data to generate treatment and/or diagnosis decisions and/or possibilities. For example, IBM Watson uses AI to read both structured and unstructured text in the EMR, read images to highlight primary and incidental findings, and compile relevant medical literature in response to clinical queries [ 54 ].

IoT-based health care and use of deep machine learning can assist health professionals in seeing the unseeable and providing new and enhanced diagnostic capability. Although diagnostic confidence may never reach 100%, combining machines and clinician expertise reliably enhances system performance. For example, compared with the diagnostic evaluation by 54 ophthalmologists and senior residents, applying AI to retinal images improved the detection and grading of diabetic retinopathy and macular edema, achieving high specificities (98%) and sensitivities (90%) [ 55 ]. AI and deep learning can also optimize disease management, can provide big data and analysis generated from mHealth apps and IoT devices, and are starting to see adoption in health care [ 56 ]. Some examples of this include predicting risk, future medical outcomes, and care decisions in diabetes and mental health [ 57 ] and predicting the progression of congestive heart failure [ 58 , 59 ], bone disease [ 60 ], Alzheimer disease [ 61 ], benign and malignant tumor classification [ 62 , 63 ], and cardiac arrhythmias [ 64 ].

Expanding the Functions and Scope of IoT to Provide Smart Health Care

IoT is an infrastructure that enables smart health services to operate. When health data are collected by IoT sensors, communicated, and stored, this enables data analytics and smart health care, which can improve risk factor identification, disease diagnoses, treatment, and remote monitoring and empower people to self-manage.

Smart health care services make use of advancements in information technologies, such as IoT, big data analytics, cloud computing, AI, and deep machine learning, to transform traditional health care delivery to be a more efficient, convenient, and a more personalized system [ 65 ]. Current developments in information computer technologies have allowed the development of health care solutions with more intelligent prediction capabilities both in and out of the hospital. We are seeing the use of virtual models to transfer care provided in hospitals to the home through the use of sensors and devices that allow remote review and monitoring of patients in their homes or treated in hospitals and creates a continuum among these through cloud access [ 7 ]. More recently, the 2020 public health efforts around the world to mitigate the spread of COVID-19 have (at least temporarily) led governments and policy makers to remove implementation and remuneration barriers to enable health care professionals to use virtual models of care for people who need it [ 9 ]. IoT also provides the opportunity to improve the quality and efficiency of the entire ecosystem of service delivery, including hospital management, medical asset management, monitoring of the workflow of staff, and optimization of medical resources based on patient flow [ 66 , 67 ].

How IoT Can Improve Health Service Delivery

Primary health care becoming more accessible.

A focus on disease prevention must become a priority this decade, as the burden of disease attributable to modifiable risk factors is greater than ever before [ 1 , 68 ]. IoT in health care has the potential to improve population health and transition our health care model to a true hybrid model of primary, secondary, and tertiary care, where the health system can use its existing workforce in new and more efficient ways. Transforming health delivery in this way is crucial to improving self-management for people with chronic conditions, as even among high health care users, more than 90% of lifestyle self-management is done by patients themselves, outside of hospitals, and in clinical settings [ 69 , 70 ].

There is a clear public demand for easy-to-access health information. For example, in a 2015 US survey, 58% (931/1604) of smartphone users downloaded a health-related app for their lifestyle self-management [ 71 ]. AI has also driven the availability of point-of-care health information, such as chatbots (or AI doctors), which can deliver lifestyle and medical advice. Examples of these established AI bots are Woebot, Your.Md, Babylon, and HealthTap, where a patient can input their symptoms and advice is generated instantly [ 72 ]. However, more than half of the most highly rated apps make medical claims that are not approved [ 73 ], with no formal process of approving apps or informing consumer choice [ 74 ], and much remains to be done to understand the potential of chatbots to improve health. Therefore, a reliable digital health evidence base is essential [ 75 ]. If health professionals have evidence-based digital resources, devices, and mobile apps readily at their disposal, digital prescriptions could become an enabler of wider adoption of IoT in health care and facilitate a wider population focus on disease prevention.

At the individual level, IoT offers the opportunity to link and potentially learn from nonhealth IoT technologies to monitor daily activities, provide support with information, and promote behavior changes ( Multimedia Appendix 2 ). In addition, IoT and data linkage create great potential of transparent, evidence-based decision making, which may be able to drive the shift of disease patterns and increase the well-being of citizens at scale. The integration of urban infrastructures, IoT technologies, and cloud computing allows the collection and analysis of a vast quantity of different human and non–human-related data. These data could provide valuable information about population-level surveillance in diseases and accidents, risk factors, and environmental conditions [ 76 ], which is difficult to collect through the traditional human-reported disease surveillance system and can be of particular benefit in pandemic responses [ 77 ]. For example, in Taiwan, big data analytics applied to electronic data (GPS, closed-circuit television surveillance, and credit card payments) in the community and personal mobile data have been effectively used to contact trace, communicate, and isolate potential contacts during the global COVID-19 pandemic [ 78 ]. Through IoT and data linkage, decision makers are likely to be able to make evidence-based decisions in promoting healthy social and built environments, safe transportation systems, high-quality public services, and smart health care and emergency response systems [ 76 , 79 , 80 ].

Secondary and Tertiary Health Care That Is Proactive, Continuous, and Coordinated

An IoT-based health care system enables the overall health care systems to move past a traditional model of service delivery, which is often reactive, intermittent, and uncoordinated, to a more proactive, continuous, and coordinated approach [ 81 ]. Such an approach is favorable because it offers the opportunity to provide high-quality care that is less invasive and appealing to patients and health care professionals. This change in the health care system landscape is also highly appealing for policy makers because it can greatly enhance the efficiency (and subsequently reduce resource use) of the health system [ 82 ] and also provide the health system flexibility to shift its models of care and delivery of services as required on an individual or population-wide basis. Multimedia Appendix 3 summarizes 7 examples of how IoT can improve the coordination of health services and likely improve our health system efficiency.

Enablers and Barriers to Address for IoT-Based Health Care

Policy support.

Policy support is one of the most important environmental enablers of IoT. Many countries already have policies in place for eHealth (eg, web-based and software programs to deliver health services) [ 83 , 84 ] and either have or are in the process of developing policies for IoT infrastructure, investment, and/or implementation in health care. For example, China, India, Indonesia, Japan, Malaysia, the Philippines, Singapore, Thailand, the European Union, the United States, and Vietnam currently have relevant policies in place for IoT [ 85 ]. Australia is also in the process of establishing a policy for IoT development and investment [ 86 ].

Technology That Is Accessible and Easy to Use

The ubiquitous nature of technology means that consumers and health care professionals have greater access to digital resources than ever before [ 87 ]. However, it is also important for health systems to be aware of the inequities that may eventuate from the widespread implementation of IoT for health care, including individuals who may not be able to afford or access technology hardware or reliable internet services because of geographic location or financial disadvantage. Similarly, if individuals do not perceive the technology as user friendly , experience poor connections, or do not feel the initiative has been designed in consultation with them (both patients and health professionals), then this often results in frustration and reluctance to use such services [ 88 , 89 ].

Cybersecurity-Focused Guidelines for Robust and Resilient Market Adoption

Cyber risk is a major obstacle to the broad adoption of IoT [ 90 ]. The privacy of patients must be ensured to prevent unauthorized identification and tracking. From this perspective, the higher the level of autonomy and intelligence of the things, the more the challenges for the protection of identities and privacy.

Confidence and Acceptability

There is a gap in public awareness and understanding of data safety in cloud-stored health information. This is of concern, as it is the single biggest threat to the adoption of IoT from a societal perspective. The premise of IoT is clear to society; however, what is not clear to people is the actual value that IoT delivers to them personally from a health care perspective [ 91 , 92 ]. The potential threat of breached confidentiality may never go away; however, the perceived value to consumers needs to outweigh these concerns to confidently engage with IoT-supported health infrastructure [ 90 ]. The confidence and acceptability of IoT by health care professionals are similarly important. There is a diverse range of factors that affect clinicians’ acceptability of technology-supported programs, including the characteristics of the technology (eg, accuracy, compatibility with usual systems, and ease of use), individual’s attitudes and knowledge (eg, familiarity and impact on professional security), external factors (eg, patient and health professional interaction), and organization readiness (eg, training and reimbursement) [ 93 ].

Privacy and Security

IoT might allow opportunities for cyberattacks and for personal data to be collected inappropriately. IoT-based applications are vulnerable to cyberattacks for 2 basic reasons: (1) most of the communications are wireless, which makes eavesdropping very easy; and (2) most of the IoT components are characterized by low energy, and therefore, they can hardly implement complex schemes on their own to ensure security. The National Institute of Standards and Technology has recently released a draft security guide and recommendations for IoT devices, which will see an emphasis on data security in IoT devices [ 94 ]; however, whether such a guideline can or will be enforced across IoT health devices is unclear.

Data Storage, Control, and Ownership

To move forward in IoT-based health care, transparency and enforced codes of practice regarding where centralized cloud data are stored and who owns the data, needs to be considered For example, does the data host have viewing rights to someone’s data and are these data completely controlled by individuals or are they never deleted from the cloud, despite a user’s request? Another important consideration is the sharing of data across states or territories and internationally. Privacy, security, and confidentiality of data control and storage should be federally enforced, but international hosts and suppliers may not be required to follow any such code. Therefore, the use of these platforms requires strategic planning and transparent guidelines to develop and implement robust IoT-based health care policies and models of care.

Interoperability and Standardization Protocols

Issues around the interoperability and standardization of IoT and health care systems are a big threat to the wider adoption of IoT for health care systems. Lack of standardization threatens the development of IoT in the health setting context, as the industry and manufacturers are yet to reach a consensus regarding wireless communication protocols and standards for machine-to-machine communication. Without a unified, standardized, and interoperable system, the adoption of IoT into health care will be greatly hindered and is unlikely to have international reach [ 95 ]. Semantic interoperability in IoT is a necessary condition for big data techniques to support decision-making processes [ 96 ]. It is increasingly common for each new technology startup, device, or system manufacturer to define their own specific architecture, protocols, and data formats, which are unable to communicate with the health care environment unless they are appreciably redeveloped or adapted to interoperate with hospital IoT platforms [ 96 ]. This creates Vertical Silos [ 97 ], which demands the development of new features for granting interoperability between different systems. The future and full potential of IoT-enabled health care relies on addressing interoperability, of which some frameworks do exist [ 98 ]. Achieving interoperability across IoT platforms can provide a safer, more accessible, productive, and satisfying experience for clinicians and patients alike.

Remuneration

Finally, remuneration for technology-assisted health care has historically been challenging [ 99 ] and differs appreciably across different countries. This is likely to be even more complex for IoT-delivered health care, where reimbursement considerations have not been established (and this is unlikely until the abovementioned points are addressed). As international health systems establish robust policies and guidelines on cybersecurity and address the issues surrounding interoperability and standardization protocols, reimbursement and regulatory considerations across single-payer and multipayer systems should become a key priority to ensuring successful, effective, and cost-effective IoT health care models can be implemented in practice.

Conclusions

From this viewpoint, the potential of IoT is summarized as a growing area of research in health care. These developments provide a great opportunity for health care systems to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. As the adoption of technology-supported health services increases to enable health systems to deliver flexible models of care, an increasing number of traditional health service delivery practices will be complemented or replaced through IoT. However, the implementation of IoT in health care will rely on a clear and robust code of practice for the management of data, privacy, confidentiality, and cybersecurity concerning the supply and use of IoT devices in health care. There are still important gaps for future research to address, which relate to the IoT technology itself, the health system, and the users of IoT technology. Specific future research on IoT technology needs to address how IoT devices can be designed with standardized protocols and interoperability with international and cross-state health systems. More research is also needed on the efficiency of blockchain storage compared with centralized cloud-based storage solutions in the context of IoT-supported health care delivery. From a health system perspective, there is a need for clinical guidelines on digital health prescriptions and robust policy regarding remuneration for primary and secondary care services provided through IoT. Finally, more research is needed to determine the acceptability and digital literacy of consumers and clinicians in the context of using IoT to improve the delivery and overall experience of health care. Although this viewpoint is a summary of selected literature only and not based on an exhaustive systematic review of the literature, we believe that addressing these areas for future research will go a long way to enable a wider uptake of IoT, which can ultimately save health care dollars and improve patient-centered care.

Acknowledgments

This research received no specific funding. JK was supported through a Griffith University Postdoctoral Research Fellowship. PS was partially funded through a National Health and Medical Research Council Senior Research Fellowship (#1136923). EG was supported by the Melbourne Graduate Research Scholarship. The authors wish to thank Dr Tilman Dingler for his assistance in the network section of this paper.

Abbreviations

Multimedia appendix 1, multimedia appendix 2, multimedia appendix 3.

Authors' Contributions: Each author contributed to the conception and design of this paper. JK conducted the literature searches and drafted the first draft of the manuscript. EG, KC, and PS revised the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest: None declared.

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Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity

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In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these three topics with the purpose of comprehensively presenting and summarizing relevant literature in these fields.

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IoT Attacks and Malware

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1 Introduction

Since around 2008, when the Internet of Things (IoT) was born [ 1 ], its growth has been booming, and now IoT is a part of daily life and has a place in many homes and businesses. IoT is hard to define as it has been evolving and changing since its conception, but it can be best understood as a network of digital and analog machines and computing devices provided with unique identifiers (UIDs) that have the ability to exchange data without human intervention [ 2 ]. In most cases, this manifests as a human interfacing with a central hub device or application, often a mobile app, that then goes on to send data and instructions to one or multiple fringe IoT devices [ 3 ]. The fringe devices are able to complete functions if required and send data back to the hub device or application, which the human can then view.

The IoT concept has given the world a higher level of accessibility, integrity, availability, scalability, confidentiality, and interoperability in terms of device connectivity [ 4 ]. However, IoTs are vulnerable to cyberattacks due to a combination of their multiple attack surfaces and their newness and thus lack of security standardizations and requirements [ 5 ]. There are a large variety of cyberattacks that attackers can leverage against IoTs, depending on what aspect of the system they are targeting and what they hope to gain from the attack. As such, there is a large volume of research into cybersecurity surrounding IoT. This includes Artificial Intelligence (AI) approaches to protecting IoT systems from attackers, usually in terms of detecting unusual behavior that may indicate an attack is occurring [ 6 ]. However, in the case of IoT, cyber-attackers always have the upper hand as they only need to find one vulnerability while cybersecurity experts must protect multiple targets. This has led to increased use of AI by cyber-attackers as well, in order to thwart the complicated algorithms that detect anomalous activity and pass by unnoticed [ 7 ]. AI has received much attention with the growth of IoT technologies. With this growth, AI technologies, such as decision trees, linear regression, machine learning, support vector machines, and neural networks, have been used in IoT cybersecurity applications to able to identify threats and potential attacks.

Authors in [ 8 ] provide a comprehensive review of the security risks related to IoT application and possible counteractions as well as compare IoT technologies in terms of integrity, anonymity, confidentiality, privacy, access control, authentication, authorization, resilience, and self-organization. The authors propose deep learning models using CICIDS2017 datasets for DDoS attack detection for the cybersecurity in IoT (Internet of Things), which provide high accuracy, i.e., 97.16% [ 9 ]. In [ 10 ], the authors evaluate the Artificial Neural Networks (ANN) in a gateway device to able to detect anomalies in the data sent from the edge devices. The results show that the proposed approach can improve the security of IoT systems. The authors in [ 11 ] propose an AI-based control approach for detection and estimation as well as compensation of cyber attacks in industrial IoT systems. In [ 12 ], The authors provide a robust pervasive detection for IoT Environments and develop a variety of adversarial attacks and defense mechanisms against them as well as validate their approach through datasets including MNIST, CIFAR-10, and SVHN. In [ 13 ], the authors analyze the recent evolution of AI decision-making in cyber physical systems and find that such evolution is virtually autonomous due to the increasing integration of IoT devices in cyber physical systems, and the value of AI decision-making due to its speed and efficiency in handling large loads of data is likely going to make this evolution inevitable. The authors of [ 14 ] discuss new approaches to risk analytics using AI and machine learning, particularly in IoT networks present in industry settings. Finally, [ 15 ] discusses methods of capturing and assessing cybersecurity risks to IoT devices for the purpose of standardizing such practices so that risk in IoT systems may be more efficiently identified and protected against.

This review paper covers a variety of topics regarding cybersecurity, the Internet of Things (IoT), Artificial Intelligence (AI), and how they all relate to each other in three survey-style sections and provides a comprehensive review of cyberattacks against IoT devices as well as provides recommended AI-based methods of protecting against these attacks. The ultimate goal of this paper is to create a resource for others who are researching these prevalent topics by presenting summaries of and making connections between relevant works covering different aspects of these subjects.

2 Methods of attacking IoT devices

Due to the lax security in many IoT devices, cyberattackers have found many ways to attack IoT devices from many different attack surfaces. Attack surfaces can vary from the IoT device itself, both its hardware and software, the network on which the IoT device is connected to, and the application with which the device interfaces; these are the three most commonly used attack surfaces as together they make up the main parts of an IoT system. Figure  1 illustrates a basic breakdown of a common IoT system; most of the attacks discussed in this paper occur at the network gateway and/or cloud data server connections, as these connections are generally where IoT security is most lacking.

figure 1

A high-level breakdown of typical IoT structure

2.1 Initial reconnaissance

Before IoT attackers even attempt cyberattacks on an IoT device, they will often study the device to identify vulnerabilities. This is often done by buying a copy of the IoT device they are targeting from the market. They then reverse engineer the device to create a test attack to see what outputs can be obtained and what avenues exist to attack the device. Examples of this include opening up the device and analyzing the internal hardware—such as the flash memory—in order to learn about the software, and tampering with the microcontroller to identify sensitive information or cause unintended behavior [ 16 ]. In order to counter reverse engineering, it is important for IoT devices to have hardware-based security. The application processor, which consists of sensors, actuators, power supply, and connectivity, should be placed in a tamper-resistant environment [ 16 ]. Device authentication can also be done with hardware-based security, such that the device can prove to the server it is connected to that it is not fake.

2.2 Physical attacks

An often low-tech type category of attacks includes physical attacks, in which the hardware of the target device is used to the benefit of the attacker in some way. There are several different types of physical attacks. These include attacks such as outage attacks, where the network that the devices are connected to are shut off to disrupt their functions; physical damage, where devices or their components are damaged to prevent proper functionality; malicious code injection, an example of which includes an attacker plugging a USB containing a virus into the target device; and object jamming, in which signal jammers are used to block or manipulate the signals put out by the devices [ 17 ]. Permanent denial of service (PDoS) attacks, which are discussed later in this paper, can be carried out as a physical attack; if an IoT device is connected to a high voltage power source, for example, its power system may become overloaded and would then require replacement [ 18 ].

2.3 Man-in-the-Middle

One of the most popular attacks on IoTs is Man-in-the-Middle (MITM) attack. With regards to computers in general, an MITM attack intercepts communication between two nodes and allows the attacker to take the role of a proxy. Attackers can perform MITM attacks between many different connections such as a computer and a router, two cell phones, and, most commonly, a server and a client. Figure  2 shows a basic example of an MITM attack between a client and a server. In regards to IoT, the attacker usually performs MITM attacks between an IoT device and the application with which it interfaces. IoT devices, in particular, tend to be more vulnerable to MITM attacks as they lack the standard implementations to fight the attacks. There are two common modes of MITM attacks: cloud polling and direct connection. In cloud polling, the smart home device is in constant communication with the cloud, usually to look for firmware updates. Attackers can redirect network traffic using Address Resolution Protocol (ARP) poisoning or by altering Domain Name System (DNS) settings or intercept HTTPS traffic by using self-signed certificates or tools such as (Secure Sockets Layer) SSL strip [ 19 ]. Many IoT devices do not verify the authenticity or the trust level of certificates, making the self-signed certificate method particularly effective. In the case of direct connections, devices communicate with a hub or application in the same network. By doing this, mobile apps can locate new devices by probing every IP address on the local network for a specific port. An attacker can do the same thing to discover devices on the network [ 19 ]. An example of an MITM IoT attack is that of a smart refrigerator that could display the user’s Google calendar. It seems like a harmless feature, but attackers found that the system did not validate SSL certificates, which allowed them to perform an MITM attack and steal the user’s Google credentials [ 19 ].

figure 2

A simple representation of a Man-in-the-Middle attack

2.3.1 Bluetooth Man-in-the-Middle

A common form of MITM attack leveraged against IoT devices is via Bluetooth connection. Many IoT devices run Bluetooth Low Energy (BLE), which is designed with IoT devices in mind to be smaller, cheaper, and more power-efficient [ 20 ]. However, BLE is vulnerable to MITM attacks. BLE uses AES-CCM encryption; AES encryption is considered secure, but the way that the encryption keys are exchanged is often insecure. The level of security relies on the pairing method used to exchange temporary keys between the devices. BLE specifically uses three-phase pairing processes: first, the initiating device sends a pairing request, and the devices exchange pairing capabilities over an insecure channel; second, the devices exchange temporary keys and verify that they are using the same temporary key, which is then used to generate a short-term key (some newer devices use a long-term key exchanged using Elliptic Curve Diffie-Hellman public-key cryptography, which is significantly more secure than the standard BLE protocol); third, the created key is exchanged over a secure connection and can be used to encrypt data [ 20 ]. Figure  3 represents this three-phase pairing process.

figure 3

A diagram illustrating the basic BLE pairing process

The temporary key is determined according to the pairing method, which is determined on the OS level of the device. There are three common pairing methods popular with IoT devices. One, called Just Works, always sets the temporary key to 0, which is obviously very insecure. However, it remains one of if not the most popular pairing methods used with BLE devices [ 20 ]. The second, Passkey, uses six-digit number combinations, which the user must manually enter into a device, which is fairly secure, though there are methods of bypassing this [ 20 ]. Finally, the Out-of-Band pairing method exchanges temporary keys using methods such as Near Field Communication. The security level of this method is determined by the security capabilities of the exchange method. If the exchange channel is protected from MITM attacks, the BLE connection can also be considered protected. Unfortunately, the Out-of-Band method is not yet common in IoT devices [ 20 ]. Another important feature of BLE devices is the Generic Attribute Profile (GATT), which is used to communicate between devices using a standardized data schema. The GATT describes devices’ roles, general behaviors, and other metadata. Any BLE-supported app within the range of an IoT device can read its GATT schema, which provides the app with necessary information [ 20 ]. In order for attackers to perform MITM attacks in BLE networks, the attacker must use two connected BLE devices himself: one device acting as the IoT device to connect to the target mobile app, and a fake mobile app to connect to the target IoT device. Some other tools for BLE MITM attacks exist, such as GATTacker, a Node.js package that scans and copies BLE signals and then runs a cloned version of the IoT device, and BtleJuice, which allows MITM attacks on Bluetooth Smart devices which have improved security over BLE [ 20 ].

2.3.2 False data injection attacks

Once an attacker has access to some or all of the devices on an IoT network via an MITM attack, one example of an attack they could carry out next is a False Data Injection (FDI) attack. FDI attacks are when an attacker alters measurements from IoT sensors by a small amount so as to avoid suspicion and then outputs the faulty data [ 21 ]. FDI attacks can be perpetrated in a number of ways, but in practice doing so via MITM attacks is the most practical. FDI attacks are often leveraged against sensors that send data to an algorithm that attempts to make predictions based on the data it has received or otherwise uses data to make conclusions. These algorithms, sometimes referred to as predictive maintenance systems, are commonly used in monitoring the state of a mechanical machine and predicting when it will need to be maintained or tuned [ 21 ]. These predictive maintenance algorithms and similar would also be a staple feature of smart cities, FDI attacks against which could be disastrous. An example of an FDI attack on a predictive maintenance system is sensors on an airplane engine that predict when the engine will need critical maintenance. When attackers are able to access even a small portion of the sensors, they are able to create a small amount of noise that goes undetected by faulty data detection mechanisms but is just enough to skew the algorithm’s predictions [ 21 ]. In testing, it would even be enough to delay critical maintenance to the system, potentially causing catastrophic failure while in use, which could cause a costly unplanned delay or loss of life.

2.4 Botnets

Another kind of common attack on IoT devices is recruiting many devices to create botnets and launch Distributed Denial of Service (DDoS) attacks. A denial of service (DoS) attack is characterized by an orchestrated effort to prevent legitimate use of a service; a DDoS attack uses attacks from multiple entities to achieve this goal. DDoS attacks aim to overwhelm the infrastructure of the target service and disrupt normal data flow. DDoS attacks generally go through a few phases: recruitment, in which the attacker scans for vulnerable machines to be used in the DDoS attack against the target; exploitation and infection, in which the vulnerable machines are exploited, and malicious code is injected; communication, in which the attacker assesses the infected machines, sees which are online and decides when to schedule attacks or upgrade the machines; and attack, in which the attacker commands the infected machines to send malicious packets to the target [ 22 ]. One of the most popular ways to gain infected machines and conduct DDoS attacks is through IoT devices due to their high availability and generally poor security and maintenance. Figure  4 shows a common command structure, in which the attacker’s master computer sends commands to one or more infected command and control centers, who each control a series of zombie devices that can then attack the target.

figure 4

A graphical representation of a common botnet hierarchy

One of the most famous malware, the Mirai worm, has been used to perpetrate some of the largest DDoS attacks ever known and is designed to infect and control IoT devices such as DVRs, CCTV cameras, and home routers. The infected devices become part of a large-scale botnet and can perpetrate several types of DDoS attacks. Mirai was built to handle multiple different CPU architectures that are popular to use in IoT devices, such as x86, ARM, Sparc, PowerPC, Motorola, etc., in order to capture as many devices as possible [ 23 ]. In order to be covert, the virus is quite small and actually does not reside in the device’s hard disk. It stays in memory, which means that once the device is rebooted, the virus is lost. However, devices that have been infected once are susceptible to reinfection due to having already been discovered as being vulnerable, and reinfection can take as little as a few minutes [ 23 ]. Today, many well-known IoT-targeting botnet viruses are derived from Mirai’s source code, including Okiru, Satori, and Reaper [ 23 ].

2.5 Denial of service attacks

IoT devices may often carry out DoS attacks, but they themselves are susceptible to them as well. IoT devices are particularly susceptible to permanent denial of service (PDoS) attacks that render a device or system completely inoperable. This can be done by overloading the battery or power systems or, more popularly, firmware attacks. In a firmware attack, the attacker may use vulnerabilities to replace a device’s basic software (usually its operating system) with a corrupted or defective version of the software, rendering it useless [ 18 ]. This process, when done legitimately, is known as flashing, and its illegitimate counterpart is known as “phlashing”. When a device is phlashed, the owner of the device has no choice but to flash the device with a clean copy of the OS and any content that might’ve been put on the device. In a particularly powerful attack, the corrupted software could overwork the hardware of the device such that recovery is impossible without replacing parts of the device [ 18 ]. The attacks to the device’s power system, though less popular, are possibly even more devastating. One example of this type of attack is a USB device with malware loaded on it that, when plugged into a computer, overuses the device’s power to the point that the hardware of the device is rendered completely ruined and needs to be replaced [ 18 ].

One example of PDoS malware is known as BrickerBot. BrickerBot uses brute force dictionary attacks to gain access to IoT devices and, once logged in to the device, runs a series of commands that result in permanent damage to the device. These commands include misconfiguring the device’s storage and kernel parameters, hindering internet connection, sabotaging device performance, and wiping all files on the device [ 24 ]. This attack is devastating enough that it often requires reinstallation of hardware or complete replacement of the device. If the hardware survives the attack, the software certainly didn’t and would need reflashing, which would lose everything that might have been on it. Interestingly enough, BrickerBot was designed to target the same devices the Mirai botnet targets and would employ as bots, and uses the same or a similar dictionary to make its brute force attacks. As it turns out, BrickerBot was actually intended to render useless those devices that Mirai would have been able to recruit in an effort to fight back against the botnet [ 24 ].

Due to the structure of IoT systems, there are multiple attack surfaces, but the most popular way of attacking IoT systems is through their connections as these tend to be the weakest links. In the future, it is advisable that IoT developers ensure that their products have strong protections against such attacks, and the introduction of IoT security standards would prevent users from unknowingly purchasing products that are insecure. Alternatively, keeping the network that the IoT system resides on secure will help prevent many popular attacks, and keeping the system largely separated from other critical systems or having backup measures will help mitigate the damage done should an attack be carried out.

3 Artificial Intelligence in cybersecurity

In order to dynamically protect systems from cyber threats, many cybersecurity experts are turning to Artificial Intelligence (AI). AI is most commonly used for intrusion detection in cybersecurity by analyzing traffic patterns and looking for an activity that is characteristic of an attack.

3.1 Machine learning

There are two main kinds of machine learning: supervised and unsupervised learning. Supervised learning is when humans manually label training data as malicious or legitimate and then input that data into the algorithm to create a model that has “classes” of data that it compares the traffic it is analyzing. Unsupervised learning forgoes training data and manual labeling, and instead the algorithm groups together similar pieces of data into classes and then classifies them according to the data coherence within one class and the data modularity between classes [ 25 ]. One popular machine learning algorithm for cybersecurity is naïve Bayes, which seeks to classify data based on the Bayesian theorem wherein anomalous activities are all assumed to originate from independent events instead of one attack. Naïve Bayes is a supervised learning algorithm, and once it is trained and has generated its classes will analyze each activity to determine the probability that it is anomalous [ 25 ]. Machine learning algorithms can also be used to create the other models discussed in this section

3.2 Decision trees

A decision tree is a type of AI that creates a set of rules based on its training data samples. It uses iterative division to find a description (often simply “attack” or “normal”) that best categorizes the traffic it is analyzing. An example of this approach in cybersecurity is detecting DoS attacks by analyzing the flow rate, size, and duration of traffic. For example, if the flow rate is low, but the duration of the traffic is long, it is likely to be an attack and will, therefore, be classified as such [ 25 ]. Decision trees can also be used to detect command injection attacks in robotic vehicles by categorizing values from CPU consumption, network flow, and volume of data written [ 25 ] as shown in Fig.  5 . This technique is popular as it is intuitive in that what the AI does and doesn’t consider anomalous traffic is known to the developer. Additionally, once an effective series of rules is found, the AI can analyze traffic in real-time, providing an almost immediate alert if unusual activity is detected.

figure 5

An example of a decision tree for classifying network traffic

Another approach to decision trees is the Rule-Learning technique, which searches for a set of attack characteristics in each iteration while maximizing some score that denotes the quality of the classification (i.e., the number of incorrectly classified data samples) [ 25 ]. The main difference between traditional decision trees and the rule-learning techniques is that traditional decision trees look for characteristics that will lead to a classification, whereas the rule-learning technique finds a complete set of rules that can describe a class. This can be an advantage as it can factor in human advice when generating rules, which creates an optimized set of rules [ 25 ].

3.3 K-nearest neighbors

The k-nearest neighbor (k-NN) technique learns from data samples to create classes by analyzing the Euclidean distance between a new piece of data and already classified pieces of data to decide what class the new piece should be put in, to put it simply [ 25 ]. For example, the new piece of data when k, the number of nearest neighbors, equals three (3) would be classified into class two (2), but when k equals nine (9), the new piece would be classified in class 1 as shown in Fig.  6 . The k-NN technique is attractive for intrusion detection systems as it can quickly learn from new traffic patterns to notice previously unseen, even zero-day attacks. Cybersecurity experts are also researching applications of k-NN for real-time detection of cyberattacks [ 25 ]. The technique has been employed to detect attacks such as false data injection attacks and performs well when data can be represented through a model that allows the measurement of their distance to other data, i.e., through a Gaussian distribution or a vector.

figure 6

How k-NN technique can classify a data point differently given different k values

3.4 Support vector machines

Support vector machines (SVMs) are an extension of linear regression models that locates a plane that separates data into two classes [ 25 ]. This plane can be linear, non-linear, polynomial, Gaussian, sigmoid, etc., depending on the function used in the algorithm. SVMs can also separate data into more than two classes by using more than one plane. In cybersecurity, this technique is used to analyze Internet traffic patterns and separate them into their component classes such as HTTP, FTP, SMTP, and so on [ 25 ]. As SVM is a supervised machine learning technique, it is often used in applications where attacks can be simulated, such as using network traffic generated from penetration testing as training data.

3.5 Artificial neural networks

Artificial neural networks (ANNs) are a technique derived from the way that neurons interact with each other in the brain in order to pass and interpret information. In ANNs, a neuron is a mathematical equation that reads data and outputs a target value, which is then passed along to the next neuron based on its value. The ANN algorithm then iterates until the output value is acceptably close to the target value, which allows the neurons to learn and correct their weights by measuring the error between the expected value and the previous output value. Once this process is finished, the algorithm presents a mathematical equation that outputs a value that can be used to classify the data [ 25 ].

A large benefit of ANNs is that they are able to adjust their mathematical models when presented with new information, whereas other mathematical models may become obsolete as new types of traffic and attacks become common [ 25 ]. This also means that ANNs are adept at catching previously unseen and zero-day attacks as they take new information into heavier consideration than static mathematical models can. Because of this, ANNs make solid intrusion detection systems and have performed well with attacks such as DoS [ 25 ].

At present, using AI in cybersecurity is a small but rapidly growing field. It is also expensive and resource intensive, so using AI to protect a small system may not be feasible. However, businesses that have large networks may benefit from these solutions, especially if they are considering or have already introduced IoT devices into their network. AI cybersecurity would also be beneficial in the massive systems one would find in a smart city, and the AI would be able to give very quick response times that are important in systems like traffic management. In the future, AI cybersecurity could also be integrated into smaller systems such as self-driving cars or smart homes. Additionally, many AI cybersecurity measures detect or thwart attacks in progress rather than preventing attacks in the first place, meaning that other preventative security measured should also be in place.

4 AI to attack IoT

Not all AI is used for the purposes of cybersecurity; cybercriminals have begun using malicious AI to aid attacks, often to thwart the intrusion detection algorithms in the case of IoT, or attacking beneficial AI in such a way that the AI works against its own system.

4.1 Automation of vulnerability detection

Machine learning can be used to discover vulnerabilities in a system. While this can be useful for those trying to secure a system to intelligently search for vulnerabilities that need to be patched, attackers also use this technology to locate and exploit vulnerabilities in their target system. As technology soars in usage, especially technologies with low-security standards such as IoT devices, the number of vulnerabilities that attackers are able to exploit has soared as well, including zero-day vulnerabilities. In order to identify vulnerabilities quickly, attackers often use AI to discover vulnerabilities and exploit them much more quickly than developers can fix them. Developers are able to use these detection tools as well, but it should be noted that developers are at a disadvantage when it comes to securing a system or device; they must find and correct every single vulnerability that could potentially exist, while attackers need only find one, making automatic detection a valuable tool for attackers.

4.1.1 Fuzzing

Fuzzing, at its core, is a testing method that generates random inputs (i.e., numbers, chars, metadata, binary, and especially “known-to-be-dangerous” values such as zero, negative or very large numbers, SQL requests, special characters) that causes the target software to crash [ 26 ]. It can be divided into dumb fuzzing and smart fuzzing. Dumb fuzzing simply generates defects by randomly changing the input variables; this is very fast as changing the input variable is simple, but it is not very good at finding defects as code coverage is narrow [ 26 ]. Smart fuzzing, on the other hand, generates input values suitable for the target software based on the software’s format and error generation. This software analysis is a big advantage for smart fuzzing as it allows the fuzzing algorithm to know where errors can occur; however, developing an efficient smart fuzzing algorithm takes expert knowledge and tuning [ 26 ].

4.1.2 Symbolic execution

Symbolic execution is a technique similar to fuzzing that searches for vulnerabilities by setting input variables to a symbol instead of a real value [ 26 ]. This technique is often split into offline and online symbolic execution. Offline symbolic execution chooses only one path to explore at a time to create new input variables by resolving the path predicate [ 26 ]. This means that each time one wishes to explore a new path, the algorithm must be run from the beginning, which is a disadvantage due to the large amount of overhead due to code re-execution. Online symbolic execution replicates states and generates path predicates at every branch statement [ 26 ]. This method does not incur much overhead, but it does require a large amount of storage to store all the status information and simultaneous processing of all the states it creates, leading to significant resource consumption.

4.2 Input attacks

When an attacker alters the input of an AI system in such a way that causes the AI to malfunction or give an incorrect output, it is known as an input attack. Input attacks are carried out by adding an attack pattern to the input, which can be anything from putting tape on a physical stop sign to confuse self-driving cars to adding small amounts of noise to an image that is imperceptible to the human eye but will confuse an AI [ 27 ]. Notably, the actual algorithm and security of the AI does not need to be compromised in order to carry out an input attack—only the input that the attacker wants to compromise the output of must be altered. In the case of tape on a stop sign, the attacker may not need to use technology at all. However, more sophisticated attacks are completely hidden from the human eye, wherein the attacker may alter a tiny part of the image in a very precise manner that is designed to misdirect the algorithm. That being said, input attacks are often categorized based on where they rest on two axes: perceivability and format.

The perceivability of an input attack is the measure of how noticeable the attack is to the human eye, while the format is the measure of how digital versus physical the attack is [ 27 ]. On one end of the perceivability axis is perceivable attacks. Altering targets, such as by deforming, removing part of, or changing its colors, and adding to the target, such as affixing physical tape or adding digital marks, are types of perceivable attacks [ 27 ]. While perceivable attacks are perceivable by humans, humans may not notice slight changes like tape on a stop sign or consider them important. A human driver still sees a stop sign with tape or scratches as a stop sign, even though a self-driving car may not. This lends itself to the effectiveness of perceivable attacks, allowing them to, in many cases, hide in plain sight. Conversely, imperceivable attacks are invisible to the human eye. This can include things such as “digital dust,” which is a small amount of noise added to the entire image that is not visible to the human eye but significant enough to an AI to change its output or an imperceptible pattern on a 3D printed object that can be picked up by AI [ 27 ]. Imperceivable attacks can also be made through audio, such as playing audio at ranges outside of the human hearing range that would be picked up by a microphone [ 27 ]. Imperceivable attacks are generally more of a security risk, as there is almost no chance that a human would notice the attack before the AI algorithm outputs an incorrect response.

The format of an attack is usually either digital or physical, without many attacks that are a combination of both [ 27 ]. In many cases of physical attacks, the attack pattern must be more obvious rather than imperceivable as physical objects must be digitized to be processed and, in that process, may lose some finer detail [ 27 ]. Some attacks are still difficult to perceive even with the detail loss, however, as with the case of 3D printed objects with a pattern that blends into the structure of the object such that it is imperceptible to humans [ 27 ]. Opposite of physical attacks are digital attacks, which attack digital inputs such as images, videos, audio recordings, and files. As these inputs are already digitized, there is no process wherein detail is lost, and as such attackers can make very exact attacks, allowing them to be more imperceptible to the human eye than physical attacks [ 27 ]. Digital attacks are not necessarily imperceptible. However—photoshopping glasses with a strange pattern over a celebrity, for example, may cause the AI to identify the image as a different person, but still a person nonetheless. An example of input attacks specific to IoT smart cars and, more broadly, smart cities. As mentioned earlier, simply placing pieces of tape in a specific way on a stop sign is enough for an algorithm to not recognize the stop sign or even classify it as a green light—this is harmful for passengers in the car if the car does not heed the stop sign, and at a larger scale could alter traffic pattern detectors in smart cities. Additionally, noise-based input attacks could cause smart assistants to malfunction and carry out unintended commands.

4.3 Data poisoning/false data injection

Data poisoning attacks and input attacks are very similar, but while the goal of input attacks is simply to alter the output of the affected input, the goal of data poisoning is to alter inputs over a long enough period of time that the AI that analyzes data has shifted and is inherently flawed; because of this, data poisoning is usually carried out while the AI is still being trained before it is actually deployed [ 27 ]. In many cases, the AI learns to fail on specific inputs that the attacker chooses; for example, if a military uses AI to detect aircraft, the enemy military may poison the AI so that it does not recognize certain types of aircraft like drones [ 27 ]. Data poisoning can also be used on AIs that are constantly learning and analyzing data in order to make and adjust predictions, such as in predictive maintenance systems. There are three main methods attackers can use to poison an AI.

4.3.1 Dataset poisoning

Poisoning the dataset of an AI is perhaps the most direct method of data poisoning—as AI gain all of their knowledge from the training datasets they are provided, any flaws within those datasets will subsequently flaw the AI’s knowledge. A basic example of this is shown in Fig.  7 : a significant portion of the data is corrupted in the second dataset, leading the resultant machine learning model to be flawed. Dataset poisoning is done by including incorrect or mislabeled information in the target dataset [ 27 ]. As AI learn by recognizing patterns in datasets, poisoned datasets break patterns or may introduce new incorrect patterns, causing the AI to misidentify inputs or identify them incorrectly [ 27 ]. Many datasets are very large, so finding poisoned data within datasets can be difficult. Continuing the example of traffic patterns, an attacker could change dataset labels in such a way that the AI no longer recognizes stop signs or add data and labels that cause the AI to classify a red light as a green light.

figure 7

A visual representation of dataset poisoning

4.3.2 Algorithm poisoning

Algorithm poisoning attacks take advantage of weaknesses that may be in the learning algorithm of the AI. This method of attack is very prominent in federated learning, which is a method of training machine learning while protecting data privacy of an individual. Federated learning, rather than collecting potentially sensitive data from users and combining it into one dataset, trains small models directly on users’ devices and then combines these models to form the final model. The users’ data never leaves their devices, and so is more secure; however, if an attacker is one of the users that the algorithm is using the data of, they are free to manipulate their own data in order to poison the model [ 27 ]. The poisoned algorithm, when combined with the rest of the algorithms, has the potential to poison the final model. They could degrade the model or even install a backdoor in this manner.

One example of federated learning is Google’s Gboard, which used federated learning to learn about text patterns in order to train predictive keyboards [ 28 ]. Although Google has extensive data vetting measures, in a less careful approach, users could potentially type nonsensical messages to confuse the predictive text or, more sinisterly, inject code into the algorithm to give themselves a backdoor. Similarly, some cutting-edge IoT devices are beginning to employ federated learning in order to learn from each other. One example of this is using machine learning to predict air pressure changes as it flows through gradually clogging filters, allowing the IoT sensor to predict when the filter will need to be changed [ 29 ]. This learning process would take a long enough time to make the study infeasible with just a few filters, but with federated learning the process is able to be sped up significantly. However, users could easily manipulate the process with their own filters in order to poison the algorithm. Although this is a relatively innocent example of algorithm poisoning, as federated learning increases in IoT, so will the potentially harmful applications of federated learning.

4.3.3 Model poisoning

Finally, some attackers simply replace a legitimate model with an already poisoned model prepared ahead of time; all the attacker has to do is get into the system which stores the model and replace the file [ 27 ]. Alternatively, the equations and data within the trained model file could be altered. This method is potentially dangerous as even if a model trained model is double-checked and data is verified to be not poisoned, the attacker can still alter the model at various points in its distribution, such as while the model is still in company’s network awaiting placement on an IoT device or on an individual IoT device once it has been distributed [ 27 ].

Many of the attacks as described above can be mitigated or prevented by properly sanitizing inputs and checking for unusual data. However, some attacks are subtle and can bypass the notice of humans and even other AI, especially when the attacks are created by malevolent AI systems. These attacks and how to defend against effectively them are at the forefront of current research as the popularity of these attacks grow, but at present many attacks do not use AI for the same reason that many security systems do not: AI is resource intensive and a good algorithm requires high-level knowledge to build, making it inaccessible and infeasible to many attackers.

5 Summary of attacks and their defenses

The various attacks discussed in this paper are listed in Table  1 , and are paired with one or more ways of protecting an IoT system from the attack. While comprehensively protecting an IoT system can be a challenging task due to the number of attack surfaces present, many of the methods listed will defend against many types of attacks; for example, as many of the attacks listed are carried out by first conducting MITM attacks, protecting the network on which an IoT system resides will protect the system from many common attacks.

6 Conclusion

Due to the nature of IoT systems to have many attack surfaces, there exists a variety of attacks against these systems, and more are being discovered as IoT grows in popularity. It is necessary to protect systems against these attacks as effectively as possible. As the number and speed of attacks grow, experts are turning to AI as a means of protecting these systems intelligently and in real-time. Of course, attackers find ways to thwart these AI and may even use AI to attack systems. This paper explores popular techniques to attempt to disrupt or compromise IoT and explains at a surface level how these attacks are carried out. Where applicable, examples are also provided in order to clarify these explanations. Next, several AI algorithms are introduced, and their applications in cybersecurity are investigated. In many cases, these models are not yet common in commercial applications but rather are still undergoing research and development or are still difficult to implement and thus rare. Nonetheless, the models discussed are promising and may become common attack detection systems within just a couple of years. Methods of attacking AI and using AI to attack are also discussed, with the frame of IoT systems. The growth of IoT systems will see these types of attacks become more and more of a threat, especially as massive networks such as smart cities begin experimentation; both as massive networks are harder to protect with a multitude of attack surfaces, and as daily life and safety revolve around AI which needs to be more or less failure-proof. This is followed by a chart reiterating the threats covered in this paper, paired with common or recommended methods of protecting against each attack. Having covered all these topics, this paper aims to provide a useful tool with which researchers and cybersecurity professionals may study IoT in the context of cybersecurity and AI in order to secure IoT systems. Additionally, it also aims to emphasize the implications of up and coming technology and the impacts that each of these fields will have on the others. It is important to consider all the potential consequences of a technological development both before and after it is made public, as cyberattackers are constantly looking to use new technologies to their benefit, whether this means diverting the technology from its original purpose or using the technology as a tool to perpetuate other attacks. This paper discusses how IoT and AI have been taken advantage of for criminal purposes or have had weaknesses exploited as an example of this, which will help readers understand current risks and help cultivate an understanding such that these weaknesses are accounted for in the future in order to prevent cyberattacks.

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Acknowledgements

This work was supported in part by the Commonwealth Cyber Initiative, an investment in the advancement of cyber R&D, innovation and workforce development in Virginia, USA. For more information about CCI, visit cyberinitiative.org.

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Murat Kuzlu

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MK, and CF conceived and designed the work as well as contributed to the acquisition, analysis, and interpretation of data. All authors discussed the results and wrote the final manuscript. All authors read and approved the final manuscript.

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Kuzlu, M., Fair, C. & Guler, O. Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity. Discov Internet Things 1 , 7 (2021). https://doi.org/10.1007/s43926-020-00001-4

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