Security and Energy Aware Clustering-Based Routing in Wireless Sensor Network: Hybrid Nature-Inspired Algorithm for Optimal Cluster Head Selection

One of the significant approaches in implementing the routing of WSNs is clustering that leads to scalability and extending of network lifetime. In the clustered WSN, cluster heads (CHs) utilize maximum energy to another node. Moreover, it balanced the load present in the sensor nodes (SNs) between the CHS for enhancing the network lifespan. Moreover, the CH plays an important part in efficient routing, as well as it must be selected in an optimal way. Thus, this work intends to introduce a cluster-based routing approach in WSN, where it selects the CHs by the optimization algorithm. A new hybrid seagull rock swarm with opposition-based learning (HSROBL) is introduced for this purpose, which is the hybridized concept of rock hyraxes swarm optimization (RHSO) and seagull optimization algorithm (SOA). Further, the optimal CH selection is based on various parameters including distance, security, delay, and energy. At the end, the outcomes of the presented approach are analyzed to extant algorithms based on delay, alive nodes, average throughput, and residual energy, respectively. Based on throughput, alive node, residual energy, as well as delay, the overall improvement in performance is about 28.50%.