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Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions. © 2022 Chongqing University of Posts and Telecommunications

Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions. © 2022 Chongqing University of Posts and Telecommunications

Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions. © 2022 Chongqing University of Posts and Telecommunications

Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions. © 2022 Chongqing University of Posts and Telecommunications

Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions. © 2022 Chongqing University of Posts and Telecommunications

Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions. © 2022 Chongqing University of Posts and Telecommunications