Chapter

Frontiers in Algorithmics

Volume 5059 of the series Lecture Notes in Computer Science pp 221-232

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

  • Yongan WuAffiliated withSchool of Computer, National University of Defense Technology
  • , Min LiAffiliated withSchool of Computer, National University of Defense Technology
  • , Zhiping CaiAffiliated withSchool of Computer, National University of Defense Technology
  • , En ZhuAffiliated withSchool of Computer, National University of Defense Technology

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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 distributed probabilistic coverage and connectivity maintenance algorithm (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 (θand k)-coverage α-connectivity dense sensor networks