Congestion centric multi-objective reptile search algorithm-based clustering and routing in cognitive radio sensor network

In recent trends, Cognitive Radio Sensor Networks (CRSNs) are investigated in-depth and getting momentum in all types of applications. CRSN can make use of the underutilized frequency spectrum in a suitable manner. Due to the above-mentioned advantage, the scholars have initiated to study of the domain of cognitive radio routing. Network congestion produces transmission delays and packet loss, as well as time and energy wasted on recovery. In order to fulfill the energy efficiency and network lifetime in CRSN, Congestion Centric Multi-Objective Reptile Search Algorithm (CC-MORSA)-based Clustering and Routing are used. The main objective of proposed CC-MORSA is to improve the lifetime by minimizing the distance among the designated Cluster Head nodes which creates the fitness function by multiple objectives like energy, distance, and load. This technique is appropriate for common sensor nodes in coordinated communications infrastructure and large networks. The simulation results are analyzed through MATALB in terms of remaining energy (999.5 J), average delay (0.36 s), Packet Delivery Ratio (99.8%), Energy Consumption (24.1 J), Throughput (0.98 Mbps), routing overhead (0.54), and Packet Loss Rate (0.2%). From the outcomes, it shows that the presented CC-MORSA outperformed conventional Stability-Aware Cluster-based Routing and Drop Factor-Based Energy Efficient Routing technique.