The Wireless Sensor Network of an Adaptive Hybrid Swarm Optimization Technique for Location Privacy Using an Infrastructure-Centric Method

The WSN has been successfully used in a variety of research topics such as network protocol selection, topology control, node deployment, location technology, and network security, among others. Data accumulation via good network organization enables nodes to be divided into small groups known as clusters. Clustering is the process of grouping sensor nodes into clusters. Cluster chiefs are the leaders of all clusters (CHs). The challenge of clustering networks in order to minimize total distance is NP-hard. For efficient clustering, the current research proposes a hybrid differential evolution with Adaptive Hybrid Bird Swarm Optimization (AHBSO). The CH selection technique is based on transmission cost as well as factors like residual energy and the resource - constrained measure. Simulation results show that the Adaptive Hybrid Bird Swarm Optimization technique outperforms CL-LEACH and Hybrid Bird Swarm Optimized in terms of packet delivery ratio, packet loss ratio and network lifetime. Finally, when compared to Partial Swarm Optimization, Hybrid Bird Swarm Optimization, and its derivatives, AHBSO performance is competitive. For various numbers of nodes, the suggested technique enhances delivery ratio by 25.34–35.50 %.