Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Abstract

SummarySensors plays an important role in day‐to‐day life as they sense and transfer data on the cloud and servers. These sensors have limited battery power due to which their optimized use is preferred with efficient energy consumption. As sensors were deployed almost everywhere under‐earth, underwater, electronic devices, and mobiles thus their enhanced performance is really important. In WSN, clustering is considered to be an essential approach that gives various advantages such as efficient energy, stability period, network lifetime, less delay, and scalability but has an issue of hot‐spot or energy‐hole problems. For this Un‐Equal Clustering is proposed where the size of clusters varies directly to the distance of the B.S. (Base Station). The main objective of Un‐Equal clustering is energy consumption, hot‐spot problem, and load balance among the cluster heads. In this paper, we have proposed an improved fuzzy‐based multi‐attributes un‐equal clustering to overcome the problem of hot‐spot problem. Earlier, researchers considered two attributes for selecting cluster heads (CHs), but later some of the MADM approaches were used that considered multiple attributes for optimal cluster head selection. In this paper, we have applied the fuzzy‐based TOPSIS method for optimized Un‐Equal clustering where the optimal node deployments with load balance among sensor nodes having optimal coverage and connectivity is considered. The results of the proposed method validate that it is one of the effective ways for selecting optimal cluster heads using fuzzy‐based multi‐attributes. Fuzzy‐based TOPSIS for cluster heads selection method shows 37% enhanced network performance with other compared existing algorithms. The performance of the proposed method is evaluated in terms of network stability, packet delivery, energy consumption, and network lifetime and F‐MAUC (Fuzzy based multi‐attributes un‐equal clustering) outperforms all other compared algorithms.