EeFFA/DE - a fuzzy-based clustering algorithm using hybrid technique for wireless sensor networks. (17th February 2022)

• Record Type: Journal Article
• Authors: Sharma, Richa; Vashisht, Vasudha; Singh, Umang
• Is Part Of: International journal of advanced intelligence paradigms. Volume 21:Number 1/2(2022)
• Page Start: 129
• Page End: 157
• Publication Date: 2022-02-17
• Subjects: clustering -- firefly algorithm -- network lifetime -- network throughput -- residual energy; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

Abstract

Designing an energy-aware clustering algorithm for wireless sensor networks (WSNs) has become an issue of great concern among the scientific community in these days. This is due to the non-rechargeable nature of the battery operated sensor devices, which are considered as the main building blocks of these wireless networks. Clustering the sensor nodes into disjoint groups has proven to be a best energy-saving approach. This paper suggested a fuzzy-based clustering algorithm named eeFFA/DE to achieve energy efficiency for WSNs. Proposed algorithm eeFFA/DE comprises of two phases. First phase focuses on the clustering of the nodes by using a distributed approach named 'balanced clustering algorithm with distributed self organisation' (DSBCA). The second phase critically analyses and select cluster heads by using two metaheuristic approaches, firefly algorithm and differential evolution technique. In this attempt, each individual node fitness value is evaluated. Proposed algorithm also emphasises on fault tolerance for selecting sub-cluster head selection. Experimental results validate the efficiency of the eeFFA/DE algorithm by using metrics like dead nodes per round, network throughput and residual energy of the nodes per round.