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

Authors:
Yongan Wu;Min Li;Zhiping Cai;En Zhu
Affiliations:
School of Computer, National University of Defense Technology, Changsha, P.R. China 410073;School of Computer, National University of Defense Technology, Changsha, P.R. China 410073;School of Computer, National University of Defense Technology, Changsha, P.R. China 410073;School of Computer, National University of Defense Technology, Changsha, P.R. China 410073
Venue:
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Year:
2008

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Abstract

The fundamental issue in sensor networks is providing a certaindegree of coverage and maintaining connectivity under the energyconstraint. In this paper, the connected k-coverageproblem is investigated under the probabilistic sensing andcommunication models, which are more realistic than deterministicmodels. Furthermore, different weights for nodes are added in orderto estimate the real power consumption. Because the problem isNP-hard, a distributedprobabilisticcoverageandconnectivitymaintenancealgorithm(DPCCM) for dense sensornetworks is proposed. DPCCM converts task requirement into twoparameters by using the consequence of Chebyshev's inequality, thenactivate sensors based on the properties of weightedε-net. It is proved that the sensors chosen byDPCCM have (θ,k)-coverage andα-connectivity. And the time and communicationcomplexities are theoretically analyzed. Simulation results showthat compared with the distributed randomized k-coverage algorithm,DPCCM significantly maintain coverage in probabilistic model andprolong the network lifetime in some sense.