An optimized clustering using hybrid meta-heuristic approach for wireless sensor networks

Power efficiency is one of the major attributes that has to be concentrated in wireless sensor network (WSN). Efficiency in the consumption of power is achieved by clustering, routing, and balancing the load in the network. The proposed work focuses on clustering to balance the load in the network, which in turn improves the power consumption by the sensor nodes. Clustering is one of the prominent techniques in WSN where research is still going on to improve efficiency. In the proposed work, the sensor nodes are collected together for the formation of multiple groups called as clusters. Cluster heads are selected using efficient satin bower bird optimization algorithm where the weight of the node is taken as a parameter. Among all these multiple cluster heads, the highly powered cluster heads are named as chief cluster head utilizing crow search optimization. In a multihop manner, the sensor nodes sense the collected data to the cluster heads, which in turn send the aggregated data to the chief cluster heads. All the selected chief cluster heads send the collected data to the central server node. Simulation is carried out in MATLAB R2020a and the performance of the proposed heuristic-based clustering is compared with other clustering protocols in terms of energy efficiency, throughput, and delivery ratio, and it is verified that the proposed protocol gives better results.