Multi-Objective Decision Making Approach based Load Balanced Clustering in WSNs to Increase The Lifetime of Network

A network of wireless sensors (WSN) is an outstanding technology that can aid in the various applications. Batteries run the sensor nodes those are used in WSN. The battery is impossible to charge or repair, so the most valuable resource for wireless sensor networks is power. Over the years, several strategies have been invented and used to preserve this precious WSN resource. One of the most successful approach for this purpose has turned out to be clustering. The aim of this paper is to suggest an effective technique for choosing cluster heads in WSNs to increase the lifetime of the network. To accomplish this task, Grey Wolf Optimizer (GWO) technique has been used. The general GWO was updated in this paper to meet the particular purpose of cluster head selection in WSNs. In this article, we have considered eleven attributes in the fitness function for the proposed algorithm. The simulation is carried out under different conditions. The results obtained show that the proposed protocol is superior in terms of energy consumption and network lifetime by evaluating the proposed protocol (i.e. CH-GWO protocol) with some well-existing cluster protocols. The suggested protocol forms energy-efficient and scalable clusters.

A Modified GA-Based Load Balanced Clustering Algorithm for WSN

The prevalent applications of WSN have fascinated a plethora of research efforts. Sensor nodes have serious limitations such as battery lifetime, memory constraints, and computational capabilities. Clustering is an important method for maximizing the network lifetime. In clustering, a network is divided into virtual groups, and CHs send their data to the BS either directly or using multi-hop routing. CHs are some special nodes having more energy than normal nodes. In fact, these special nodes are also battery operated and consequently power constrained; thus, they play a vital role in network lifetime. Cluster formation is very important and improper design may cause overload. This paper presents a modified GA-based load balanced clustering (MGALBC) algorithm for WSN. It is better than GA-based load balanced clustering (GALBC) algorithm because it balances the load by considering the residual energy. The result shows that the proposed method is better than GALBC in terms of energy consumption, number of active sensor nodes and network life.