Paper Info
Title
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.
Year
DOI
Venue
2009
10.1109/WCNC.2009.4917803
WCNC
Keywords
Field
DocType
base station centric,centralised version,neural network-based clustering algorithm,cluster-based routing algorithm,first node,data reduction technique,intelligent clustering,wireless sensor network,life span,fuzzy-art neural network,network lifetime,remote area,intelligent sensors,neural networks,neural network,lead,intelligent networks,base stations,artificial neural networks,data reduction,routing,cost function,wireless sensor networks,base station,clustering algorithms
Base station,Intelligent sensor,Computer science,Filter (signal processing),Computer network,Real-time computing,Battery (electricity),Artificial neural network,Cluster analysis,Wireless sensor network,Data reduction
Conference
Citations 
PageRank 
References 
7
0.55
12
Authors
2
Name
Order
Citations
PageRank
Mario Cordina1112.41
Carl J. Debono2193.71