Increasing User Capacity of Wireless Physical-Layer Identification in Internet of Things

Wireless Physical Layer Identification (WPLI) aims at identifying or classifying authorized devices based on the unique Radio Frequency Fingerprints (RFFs) to guarantee physical-layer security between each individual device. WPLI is a promising technique in the security area of Internet of Things (IoT), which avoids the burden of conventional software-level security in IoT on the computation capability, on-chip storage and power consumption of devices. Moreover, WPLI directly extracts the RFFs from radio frequency signals at physical layer, which breaks the software-level security barriers of heterogeneous device networks in IoT. However, in the existing works in WPLI, a general work focusing on theoretical characterization of performance WPLI systems in IoT is still in absence. In this work, we evaluate the performance of WPLI in IoT by thoroughly characterizing user capacity of WPLI system as the metric. We utilize the concept of IoT by combining multiple devices to represent one user's secure identity to increase the user capacity of WPLI system. Based on the information-theoretic tool we proposed, the changes in user capacity of WPLI is characterized over typical real-world scenarios according to different number of devices assigned for one user. Various field experiments on classification performance of a practical multi- user-multi-device WPLI system with are conducted to validate the user capacity characterization for each scenario.