Quantum tunicate swarm algorithm based energy aware clustering scheme for wireless sensor networks

Abstract

Wireless Sensor Network (WSN) is represented by a group of sensor nodes with limited battery capacity deployed in a target region for data collection purposes. Energy is treated as a major issue from the design of WSN as it mainly affects the overall functioning of the network. A commonly employed energy-efficient mechanism is clustering which groups the nodes into several groups of clusters. Earlier works reported that clustering is treated as an NP hard issue which can be addressed by soft computing techniques. In this way, this article focuses on the design of Quantum Tunicate Swarm Algorithm Based Energy Aware Clustering (QTSA-EAC) scheme for WSN. The presented QTSA-EAC technique mainly attempts to effectively manage the available energy among the nodes in WSN. In addition, the QTSA is derived by the incorporation of quantum computing concepts as to the traditional TSA. Moreover, the QTSA-EAC technique derives a fitness function with the minimization of total energy consumption in the networks. The inclusion of multiple parameters in the cluster head (CH) selection process helps to accomplish energy efficiency and maximum lifetime of WSN. The performance assessment of the QTSA-EAC technique is performed and the results reported that the presented model is effective, improves lifetime, reduce energy exploitation, and delay over the other methods.