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)


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Estrin, D., Govindan, R., Heidemann, J., Kumar, S.: Next century challenges: Scalable coordination in sensor networks. In: 5th ACM International Conference on Mobile Computing and Networking (MOBICOM 1999), pp. 263–270. ACM Press, Seattle (1999)CrossRefGoogle Scholar
  2. 2.
    Cerpa, A., Elson, J., Hamilton, M., Zhao, J., Estrin, D., Girod, L.: Habitat monitoring: Application driver for wireless communications technology. In: Proc. of ACM SIGCOMM Workshop on Data Communications in Latin America and the Caribbean, pp. 3–5. ACM Press, Costa Rica (2001)Google Scholar
  3. 3.
    Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: A survey. Computer Networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
  4. 4.
    Yang, S., Dai, F., Cardei, M., Wu, J., Patterson, F.: On connected multiple point coverage in wireless sensor networks. Journal of Wireless Information Networks 13(4), 289–301 (2006)CrossRefGoogle Scholar
  5. 5.
    Hekmat, R., Van Mieghem, P.: Connectivity in wireless ad-hoc networks with a log-normal radio model. Mobile Networks and Applications 11(3), 351–360 (2006)CrossRefGoogle Scholar
  6. 6.
    Hefeeda, M., Ahmadi, H.: A probabilistic coverage protocol for wireless sensor networks. In: 15th IEEE International Conference on Network Protocols (ICNP 2007), pp. 41–50. IEEE Press, Beijing (2007)CrossRefGoogle Scholar
  7. 7.
    Hefeeda, M., Bagheri, M.: Randomized k-coverage algorithms for dense sensor networks. In: 26th IEEE International Conference on Computer Communications (INFOCOM 2007), pp. 2376–2380. IEEE Press, Anchorage (2007)CrossRefGoogle Scholar
  8. 8.
    Chakrabarty, K., Iyengar, S., Qi, H., Cho, E.: Grid coverage for surveillance and target location in distributed sensor networks. IEEE Transactions on Computers 51(12), 1448–1453 (2002)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Huang, C., Tseng, Y.: The coverage problem in a wireless sensor network. In: 2nd ACM International Conference on Wireless Sensor Networks and Applications (WSNA 2003), pp. 115–121. ACM Press, San Diego (2003)CrossRefGoogle Scholar
  10. 10.
    Zhou, Z., Das, S., Gupta, H.: Connected k-coverage problem in sensor networks. In: 13th International Conference on Computer Communications and Networks (ICCCN 2004), pp. 373–378. IEEE Press, Chicago (2004)Google Scholar
  11. 11.
    Wu, J., Dai, F.: On constructing k-connected k-dominating set in wireless networks. In: 19th International Parallel and Distributed Processing Symposium (IPDPS 2005), pp. 81a–81a. IEEE Press, Denver (2005)Google Scholar
  12. 12.
    Lu, J., Bao, L., Suda, T.: Coverage-Aware Sensor Engagement in Dense Sensor Networks. In: Yang, L.T., Amamiya, M., Liu, Z., Guo, M., Rammig, F.J. (eds.) EUC 2005. LNCS, vol. 3824, pp. 639–650. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Hefeeda, M., Ahmadi, H.: Network connectivity under probabilistic communication models in wireless sensor networks. In: 4th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2007), pp. 1–9. IEEE Press, Pisa (2007)Google Scholar
  14. 14.
    He, T., Huang, C., Lum, B., Stankovic, J., Adelzaher, T.: Range-free localization schemes for large scale sensor networks. In: 9th ACM International Conference on Mobile Computing and Networking (MOBICOM 2003), pp. 81–95. ACM Press, San Diego (2003)CrossRefGoogle Scholar
  15. 15.
    Ganeriwal, S., Kumar, R., Srivastava, M.B.: Network-Wide Time Synchronization in Sensor Networks. NESL Technical Report (2003)Google Scholar
  16. 16.
    Gupta, H., Zhou, Z., Das, S.R., Gu, Q.: Connected sensor cover: Self-Organization of sensor networks for efficient query execution. In: 4th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2003), pp. 189–200. ACM Press, New York (2003)CrossRefGoogle Scholar
  17. 17.
    Brönnimann, H., Goodrich, M.: Almost optimal set covers in finite VC-dimension. Discrete&Computational Geometry 14(4), 463–479 (1995)CrossRefMathSciNetMATHGoogle Scholar
  18. 18.
    Haussler, D., Welzl, E.: Epsilon-nets and simplex range queries. Discrete and Computational Geometry 2(1), 127–151 (1987)CrossRefMathSciNetMATHGoogle Scholar
  19. 19.

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

Personalised recommendations