Hybrid Energy Efficient Fuzzy C-Means with Bear Smell Search Algorithm in Wireless Sensor Networks

Abstract

Energy efficiency is one of the primary deficiencies in wireless sensor networks. The sensor nodes present in the networks contain small battery, which cannot be changed or recharged again. Hence, the use of energy must be carefully monitored and preserved in these battery-operated networks. Energy optimization is recently the popular technique incluster-based routing protocols. The clustering methodology works under the act of group of sensor nodes where each group selects one head member called cluster head. To enhance the lifespan of the network with high energy efficiency, a Fuzzy C-means algorithm is offered in this articlefor clustering the sensor nodes. The cluster head is selected by using particle swarm optimization- Leven berg Marquardt (PSO-LM) algorithm to maximize the energy efficiency and diminish the quantity of dead sensor nodes in the network. Finally, a shortest path is selected via bear smell search algorithm to transmit the data to the sink node. The proposed method is experimentally evaluated and the results are compared in terms of packet delivery ratio and energy efficiency. The experimental outcome revealed that the proposed technique out performs other optimization algorithms and produced better results.