Maximizing the lifetime of wireless sensor networks through intelligent clustering and data reduction techniques

Abstract

Wireless sensor networks are generally deployed in remote areas where no infrastructure is available. This imposes the use of battery operated devices which seriously limits the lifetime of the network. In this paper we present a cluster-based routing algorithm which is based on Fuzzy-ART neural networks to maximize the life span of such networks. Results show that the energy saving obtained improves the network lifetime by 79.6%, 17.1% and 22.4% (in terms of First Node Dies) when compared to LEACH, a centralised version of LEACH and a self-organizing map (SOM) neural network-based clustering algorithm respectively. Furthermore, this paper explores the use of a base station centric predictive filtering algorithm to reduce the amount of transmitted data leading to a further increase in network lifetime.