Energy Efficient Unequal Clustering Routing Algorithm Based on Neuro-Fuzzy Logic for Wireless Sensor Network

Abstract

Nowadays, Wireless Sensor Network (WSN) is used in large number of sectors like military surveillance, agriculture, health care and so on, because they are inexpensive, flexible and easy to use. Despite there are several benefits of WSNs, the major challenge faced by experts is their limited battery capacity which result in reduced lifespan. Over the years, numerous methods have been proposed by various researchers but these methods faced issues in uniform node deployment, complexity, clustering and cluster head (CH) selection which degraded their performance. In order to solve these issues, an enhanced model based on Neuro-Fuzzy system and unequal clustering is proposed in this paper. The process of clustering and CH selection is improved in the proposed Neuro-Fuzzy Energy Efficient Unequal Clustering (NFEEUC) model by utilizing the Neuro-Fuzzy system which takes node density and distance from the sink node as two input parameters so that the chances of a node to become a CH is determined. In addition to this, the proposed model is effective in detecting and eliminating the redundant data by comparing the sensed data with the previously sensed information. The performance of the suggested NFEEUC model is analyzed in the MATLAB software. The results from simulation are evaluated and compared with standard EEFUC model in terms dead nodes, node energy, first node death (FND), half node death (HND) and last node death (LND). The results obtained proved that the proposed NFEEUC model outperforms the traditional model in all parameters and has increased lifespan.