Similarity Preference Weight Based Chimp Optimization Algorithm for Secure Clustering and Routing in IoT

Wireless Sensor Networks (WSN) are designed to monitor sensor nodes systems, with a focus on data processing, collection and transmission. Developments in wireless communication have extended the scope of WSNs to various domains such as underwater communication and Internet of Things (IoT). However, secure clustering and routing remain challenging due to increased vulnerability to attacks in IoT-WSN, which negatively impact communication and data integrity. This research proposes Similarity Preference Weight-Chimp Optimization Algorithm (SPW-ChOA) for secure clustering and routing in WSN-IoT. Trust, node degree, distance between Cluster Head (CH) and Base Station (BS), energy and distance are employed as fitness functions for Secure CH (SCH) selection. The factors considered for routing in SPW-ChOA include energy and distance. The performance of SPW-ChOA is evaluated in terms of residual energy, Packet Delivery Ratio (PDR), throughput and energy consumption, with the number of nodes varied at 500,1000,1500 and 2000. The proposed SPW-ChOA consumes lesser energy at 0.28 which compared to the existing Butterfly Optimization Algorithm (BOA) and Antlion Optimization Algorithm (AOA).