Kalman filtering based preprocessing for secure key generation

Abstract

The global market of IoT devices is increasing rapidly. Examples of IoT like networks include smart cities, industrial enterprises, agriculture, home automation, healthcare etc. IoT offers efficient resource utilization, enhanced data collection, minimum human efforts etc. although it is constrained by many challenges such as security, privacy, limited interoperability, complexity and integration challenges. Among all, security and privacy are paramount and require efficient techniques with low power and minimum computer complexity as IoT is a power-constrained network. Traditional encryption methods fail to meet these limitations, so physical layer key generation (PLKG) using Received Signal Strength Indicator (RSSI) preprocessing, is a promising approach for securing such wireless networks. In this paper, the use of Kalman filtering for RSSI preprocessing in secure key generation at the physical layer is presented and compared its performance with the existing Principal Component Analysis (PCA) based preprocessing technique. The performance of the proposed approach is evaluated on three fading channels namely Rician, Rayleigh, and Nakagami to highlight its effectiveness in different environments. The results show that the Kalman filtering is significantly better than PCA in terms of Bit Disagreement Rate (BDR), Spearmen rank Correlation Coefficient (SCC) and Entropy, thus providing stronger security guarantees and more reliable key generation. This makes Kalman filtering a potential solution for PLKG in IoT environments, focusing on computing performance and high security.