Hybrid Red Deer and Improved Fireworks Optimization Algorithm–based Clustering Protocol for improving network longevity with energy stability in WSNs

Abstract

Clustering of nodes in wireless sensor networks (WSNs) plays a dominant role in gathering environmental data from the specific area of monitoring over which they are deployed for achieving a reactive decision-making process. The design and development of an energy-efficient clustering strategy with a potential cluster head (CH) selection process is a herculean task. This development of the CH selection scheme is referred as a non-deterministic polynomial (NP) hard problem as it needs to optimize different parameters that influence the selection of potential sensor nodes as CH. It needs to concentrate on the process of enhancing network lifespan with energy efficiency by selecting optimal routing path during data dissemination activity. In this paper, a Hybrid Red Deer and Fireworks Optimization Algorithm (HIRDIFOA)–based energy efficient clustering technique is proposed for extending network lifespan with maximized stability in the network energy. This proposed HRDFOA integrated the exploration capability of Improved Red Deer Optimization (IRDOA) with the maximized exploitation tendency of the Modified Firework Optimization Algorithm (MFWOA) during the CH selection process. It facilitated the CH selection by evaluating the fitness functions that integrate the factors of residual energy (RE), distance between sensor and CH, distance between CH and sink, and radius of communication. It significantly adopted MWFOA for achieving sink node mobility such that data can be reliably routed from CH to sink. The outcomes of HRDFOA confirm better throughput of 19.21% with reduced energy consumption of 17.42% and reduced end-to-end delay of 18.52% in contrast to the competitive CH selection schemes used for investigation.