Optimizing Network Longevity and Attack Mitigation through Adaptive Spectrum Sensing in Cognitive Radio Sensor Networks

This paper presents an innovative spectrum sensing method aimed at improving the lifespan of Cognitive Radio Sensor Networks (CRSNs) while addressing the challenge of Primary User Emulation Attacks (PUEA). The Adaptive Spectrum Sensing Multi-hop Grey Wolf Optimization Algorithm (ASSMGA) adjusts dynamically to changing network conditions, effectively differentiating between genuine primary users and emulated attacks. It also enhances energy efficiency. Using metaheuristic clustering for smart data transmission, ASSMGA prolongs network life and minimizes false alarms and missed detections, which are common issues in CRSNs. Simulation results demonstrate that ASSMGA outperforms existing clustering and routing methods by achieving longer network longevity, greater throughput, and better data collection under PUEA interference. This method provides a dependable and efficient solution for CRSNs, ensuring more effective spectrum utilization.