Integrated energy optimization and emulation attack mitigation technique for CRSN under Rayleigh fading channel

Abstract

Abstract Cognitive Radio Sensor Networks (CRSNs) are envisioned to overcome spectrum scarcity by enabling opportunistic spectrum access. However, their performance is severely degraded by Primary User Emulation Attacks (PUEAs), inefficient routing, and unfair channel allocation. In this paper, we present an Energy and PUEA-Aware Algorithm for Rayleigh Fading Channels (EPA-RF) that integrates PUEA mitigation, energy efficiency, and fairness in CRSN operating over Rayleigh flat fading channels. A PUEA mechanism is introduced, in which a malicious user intelligently senses the spectrum environment and transmits signals that imitate genuine primary users to deceive secondary users and optimize spectrum utilization. The Moth Flame Optimization (MFO) based channel allocation scheme is employed to maximize throughput while ensuring fairness among secondary users. Further, to improve network longevity, the algorithm optimally adjusts cluster radius and supports multi-hop forwarding with minimized control overhead. Simulation results verify that the proposed integrated framework significantly enhances throughput, prolongs network lifetime, and mitigates the adverse effects of PUEA compared to existing approaches.