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  • Research Article
  • Open Access

Extending the Lifetime of Sensor Networks through Adaptive Reclustering

EURASIP Journal on Wireless Communications and Networking20072007:031809

  • Received: 14 October 2006
  • Accepted: 30 March 2007
  • Published:


We analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality-of-service (QoS) constraint, given by the maximum tolerable probability of decision error at the access point (AP). In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal) for the lifetime of a single sensor. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. We also derive an analytical framework for the computation of the network lifetime and the penalty, in terms of time delay and energy consumption, brought by adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. Moreover, the observation of the phenomenon should be frequent in order to limit the penalties associated with the reclustering procedure. We also apply the developed framework to analyze the energy consumption associated with the proposed reclustering protocol, obtaining results in good agreement with the performance of realistic wireless sensor networks. Finally, we present simulation results on the lifetime of IEEE 802.15.4 wireless sensor networks, which enrich the proposed analytical framework and show that typical networking performance metrics (such as throughput and delay) are influenced by the sensor network lifetime.


  • Sensor Network
  • Wireless Sensor Network
  • Network Lifetime
  • Network Behavior
  • Present Simulation Result


Authors’ Affiliations

Wireless Ad-Hoc and Sensor Networks (WASN) Laboratory, Department of Information Engineering, University of Parma, Parma, 43100, Italy


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© G. Ferrari and M. Martalò. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.