A Hybrid Approach for Cluster Head Selection in Wireless Sensor Networks Using a Modified Fuzzy-Particle Swarm Optimization Algorithm

Abstract

The wireless sensor network (WSN) is extensively utilized in the network for tracking purposes including identifying animals and monitoring, observing the environment, military monitoring, and surveillance of human activity. The clustering strategy based on transmission has a longer life cycle as an outcome of the elimination of data redundancies. The SN is typically split up into numerous categories, each of which has a CH. The detected data is sent to the corresponding Cluster Head (CH) by each base station (SN) in the clusters. Data from cluster SN was collected by the CHs as well as forwarded to the BS. While clusters are being generated, every node is not guaranteed to join a specific cluster. A few individual nodes, which are residual nodes from cluster creation, may still be present in the area being monitored region. To send data straight to the sink node, these nodes have to use more energy. For the path to be created with the best routing direction, these nodes must transmit several control announcements to determine the next ideal hop. The hybrid efficiency in terms of energy distributed clustering (HEED) lengthens the lifespan of a network's connections. Not many nodes in the sensor network are now not a part of any clusters after grouping using HEED. The process used in each cluster formation is the same. The proposed research uses the modified fuzzy particle swarm optimization (PSO (approach for cluster construction and CH election. To prevent the emergence of such residual nodes, grouping is careful. The throughput, latency, and energy consumption efficiency findings of simulation trials show that the technique is better than existing ones. The resulting findings are presented in the outcome sections.