Performance improvement of wireless secret key generation with colored noise for IoT

Abstract

Achieving security in the Internet of things (IoT) networks by generating symmetric keys from the wireless channel parameters like received signal strength (RSS) is a promising approach. Despite the easy acquisition of the RSS signal, RSS-based security is less explored for IoT. In this work, we analyze the performance of RSS-based wireless physical layer key generation with correlated colored noise components and proposed a low complexity filtering approach to improve the performance for the IoT network. We started with providing a survey of various recent researches related to RSS-based key generation and also discussed correlated colored noise components with a few of the recent works considering them. Further, we analyze various colored noise components in the time domain by the Allan variance and Ljung-Box test. Furthermore, we develop a key generation model and proposed a moving window averaging-based filtering followed by Lloyd max quantization to improve the BDR performance, degraded due to the presence of correlated colored noise components. The simulation results show that the proposed preprocessing technique has a considerable improvement in the BDR performance, and the keys generated have sufficient randomness, which is verified by NIST test.

Physical layer secure key generation with nonlinear preprocessing of RSS for power constraint wireless networks

“…Abusara et al 23 employed PCA as the dimensionality reduction technique in WLAN‐based indoor positioning to eliminate redundant information to improve position accuracy and to reduce computational cost. A hybrid preprocessing, AvDR, is used in previous works, 24,25 which consider colored noise along with AWGN for the key generation system.…”