Soft C-means Multi objective Metaheuristic Dragonfly Optimization for Cluster Head Selection in WSN
Keywords:
Clustering, Cluster head selection, dragonfly optimization, energy consumption, wireless sensor networkAbstract
Wireless communication is a recent area in wireless sensor networks (WSNs) due to the advancement of electronic devices. WSN comprised spatially distributed sensors distributed over area. Clustering groups the sensor nodes for conserving the power. The cluster head (CH) selection balances the load with energy consumption. Many researchers carried out their research on cluster head selection in WSN. Therefore, clustering accuracy was not increased, and processing time was not reduced. In order to resolve the problems, Soft C-means Multiobjective Metaheuristic Dragonfly Optimization (SCMMDO) Method was introduced. The SCMMDO Method's main goal is to identify the ideal cluster head for effective data transmission in WSN. SCMMDO Method performed two processes, namely clustering and optimization in WSN. Initially, the sensor nodes are randomly distributed. The soft C-means method puts sensor nodes into clusters based on three factors. They are received signal strength, residual energy and bandwidth availability. The cluster head is then chosen using multi-objective meta-heuristic dragonfly optimization. The data packet is sent to the destination node by the source node using the cluster head that has been selected. Simulation is performed with the help of the metrics such as energy consumption, clustering accuracy and processing time, throughput and delay. The observed result illustrates that SCMMDO Method effectively increases the clustering accuracy and minimizes the energy consumption as well as processing time. The clustering accuracy of the proposed system is 96%.
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