Enhancing Network Longevity and Mitigating Emulation Attack Through Adaptive Metaheuristic Spectrum Sensing in Cognitive Radio Sensor Network

Abstract

Cognitive Radio Sensor Network (CRSN) provides effective utilization of spectrum, using dynamic spectrum allocation that incorporates multi-hop clustering and routing algorithms for energy-efficient data delivery. The existing clustering and routing methods modeled for Cognitive Radio Sensor Network, typically operates with the assumption of flawless spectrum sensing, disregarding incorrect alarms and miss detection techniques. These could lead to transmission failures, Primary User collisions, or restricted spectrum usage. An Adaptive Spectrum Sensing Multi-Hop Grey wolf optimization Algorithm for Primary User Emulation Attack (ASSMGA-EA) has been modeled to mitigate the effect of poor spectrum sensing which affects the sensor network performance when a Primary User Emulation Attack is present and dynamically adapts sensing approaches in response to changing network conditions and attack situations. Nodes are elected as Cluster Heads based on residual energy, high spectrum sensing capability, and accessible channel detection level functions. Accuracy-based, idle channel detection promotes effective intra-cluster and inter-cluster data transmission. Energy consumption due to control overhead is minimized by regulating cluster formation and Cluster Head selection. The malicious users also possess a spectrum sensing capability which makes spectrum access even more difficult. By distinguishing between the Primary User and Primary User Emulation Attack signals, the proposed method improves detecting accuracy while lowering the likelihood of sensing errors, and enhances throughput, and energy efficiency more than cooperative and hybrid sensing techniques. The simulations demonstrate that the proposed algorithm has clear advantages over the current clustering and routing algorithms for CRSN in terms of extending the network’s lifespan, efficient data gathering, higher residual energy, and improving the capacity of the network.

Keywords

• Adaptive sensing
• cognitive radio sensor networks
• primary user emulation attack
• network lifetime
• data gathering