EFTVG: An Energy Efficient Fuzzy–Timer Clustering Approach in an Adaptive Virtual Grid Cluster Based WSN

Abstract

Abstract Clustering is an effective way that improves WSN lifetime. In this paper, unlike the other approaches, an adaptive virtual grid is conducted to form cluster boundaries during network lifetime. Using the adaptive virtual grid not only shrinks the size of clusters but also determines network configuration based on the location of the base station. In EFTVG, compared to the previous clustering methods which used a specific cluster head selection procedure for the entire network lifetime, we propose a consecutive clustering (fuzzy-timer) approach. Authors divide the network lifetime into two parts. Firstly, a fuzzy clustering is applied to address uncertainty in a semi-distributed manner. Then a timer-based clustering is used in the second part to tackle sharp energy consumption in a fully-distributed manner. A local threshold is applied in each cluster to specify whether cluster head selection is required or not. Using this novelty, the network may encounter new cluster heads in some clusters, while we trust the ex-cluster head in others. Applying this policy results in energy saving. Finally, we present a novel hybrid routing to reduce energy consumption in WSN. EFTVG is compared with MOFCA, EAFCA, EAUCF, FLECH, DECUC, FUCA, DUCF, FSVGA and FMCR-CT approaches in four different scenarios.