A Distributed Algorithm to Approximate Node-Weighted Minimum α-Connected (θ,k)-Coverage in Dense Sensor Networks

  • Yongan Wu
  • Min Li
  • Zhiping Cai
  • En Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5059)

Abstract

The fundamental issue in sensor networks is providing a certain degree of coverage and maintaining connectivity under the energy constraint. In this paper, the connected k-coverage problem is investigated under the probabilistic sensing and communication models, which are more realistic than deterministic models. Furthermore, different weights for nodes are added in order to estimate the real power consumption. Because the problem is NP-hard, a distributedprobabilisticcoverageandconnectivitymaintenancealgorithm (DPCCM) for dense sensor networks is proposed. DPCCM converts task requirement into two parameters by using the consequence of Chebyshev’s inequality, then activate sensors based on the properties of weighted ε-net. It is proved that the sensors chosen by DPCCM have (θ,k)-coverage and α-connectivity. And the time and communication complexities are theoretically analyzed. Simulation results show that compared with the distributed randomized k-coverage algorithm, DPCCM significantly maintain coverage in probabilistic model and prolong the network lifetime in some sense.

Keywords

probabilistic model (θandk)-coverage α-connectivity dense sensor networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yongan Wu
    • 1
  • Min Li
    • 1
  • Zhiping Cai
    • 1
  • En Zhu
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangshaP.R. China

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