Energy aware optimal cluster head selection using hybrid algorithm for clustering routing in wireless sensor networks

Abstract

Clustering is one of the most significant methods for maximizing the lifetime of the network in wireless sensor networks (WSNs). The technique involves the first grouping of sensor nodes to clusters and second selecting appropriate cluster heads (CHs) for each cluster. In fact, CHs gather data from corresponding nodes and transmit those aggregated data to Base Station (BS). However, the major shortcoming drawn in this technique is to choose optimal CHs. More research works have been already under the investigation on selecting the appropriate CH with respect to different angles. In this scenario, this paper aims to propose a new energy-aware CH selection by hierarchical routing in WSN via proposing a new hybrid optimization technique. More importantly, the selection of CH is carried out under the consideration of stabilization of energy, minimization of the distance between nodes and minimization of delay during data transmission as well. With this, a non-linear objective function is framed, which intent to optimize the CH in such a way that the network lifetime has to be prolonged. Here, for attaining optimal CHs, a new hybrid algorithm is introduced that integrates the concept of Lion Algorithm (LA) and Grey Wolf Optimizer (GWO), which is named as Lion Updated GWO (LU-GWO). Finally, the performance of the proposed model is evaluated by comparing other conventional models with respect to certain performance measures. The simulation analysis shows that the proposed scheme for 50 nodes and 1000x1000 distance was 2.7%, 2.7%, 5.56%, 5.56%, and 5.56% better than GWO, LA, GA, PSO and ABC algorithms with respect to the alive node analysis and the overall analysis proves the superiority of proposed work.