A study of optimization Algorithm based clustering in Wireless sensor networks

Abstract

Recently, Wireless Sensor Network (WSN) attains global attention, as it is a promising technology for numerous critical applications. WSNs are responsible for collecting, processing, and distributing the processed data through a wireless medium to the intended Base Station (BS) or data storage. However, the energy consumption and the network lifetime concerns the implementation of WSNs in critical application sectors. Thus, prolonging network lifetime with optimized energy consumption and improved delivery ratio is a greater challenge in WSN. Clustering is an optimistic approach, which organizes the sensor nodes in an efficient manner for minimizing the energy depletion. Sensor nodes possessing common features are grouped together to form clusters. Each clustering mechanism may yield variable number of Cluster Heads (CHs) and cluster members based on the application scenario. The CHs are able to develop a new level of hierarchy for processing the data accumulated or can just act as an intermediator node to transmit the data in between the sensor nodes and the BS. Though clustering aids in extending the network lifetime, it holds some challenges such as CH selection overhead and assigning cluster members to form optimal clusters. The challenges in clustering may lead the network in higher energy consumption. Thus, selecting the best CH, forming optimal clusters, and effective protocol still play a vital role in determining the WSN lifetime. The traditional clustering approaches may not solve all the clustering challenges. In order to yield globally optimized clustering solutions, meta-heuristic algorithms are required. However, selecting an efficient meta-heuristic algorithm is another challenge. Since, most of the existing algorithms falls for the local optimum solutions and does not explore well for the global optimum solution newline