Minimization of the Redundant Sensor Nodes in Dense Wireless Sensor Networks

  • Dingxing Zhang
  • Ming Xu
  • Wei Xiao
  • Junwen Gao
  • Wenshen Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4684)


Most sensor networks are deployed with high density and then node duty cycle is dynamically managed in order to prolong the network lifetime. In this paper, we address the issue of maintaining sensing coverage of surveillance target in large density wireless sensor networks and present an efficient technique for the selection of active sensor nodes. First, the At Most k-Coverage Problem (AM k-Coverage) is modeled as a nonlinear integer programming. Then Genetic Algorithm is designed to solve the multi-objective nonlinear integer programming, which is a quasi-parallel method. And later by using Genetic Algorithm, a central algorithm is designed to organize a sensor network into coverage sets. Considering that the central base station consumes a great deal of energy when it collects the coverage information from every node, we also propose a localized manner on the basis of the proposed central algorithm. Finally, Experimental results show that the proposed algorithm can construct the coverage sets reliably and reduce the number of active sensor nodes which is helpful to reduce system energy consumption and prolong the network lifespan.


coverage sets multi-objective optimization genetic algorithm Pareto-optimal local central node maximal remaining energy 


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  1. 1.
    Steuer, R.E.: Multiple criteria optimization: Theory, computation, and application. Wiley, New York (1986)zbMATHGoogle Scholar
  2. 2.
    Chakrabarty, K., Iyengar, S.S., Qi, H., Cho, E.: Grid coverage for surveillance and target location in distributed sensor networks. IEEE Trans. on Comput. 51(12), 1448–1453 (2002)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Cardei, M., Du, D.Z.: Improving wireless sensor network lifetime through power aware organization. ACM Wireless Networks 11(3) (2005)Google Scholar
  4. 4.
    Cardei, M., Thai, M.T., Li, Y., Wu, W.: Energy-efficient target coverage in wireless sensor networks. In: INFOCOM 2005. Proceedings of 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1976–1984 (2005)Google Scholar
  5. 5.
    Cardei, M., Wu, J., Lu, M., Pervaiz, M.O.: Maximum network lifetime in wireless sensor networks with adjustable sensing ranges. In: WiMob 2005. Proc. of IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (2005)Google Scholar
  6. 6.
    Koubaa, H., Fleury, E.: On the performance of double domination in ad hoc networks. In: Proc. of IFIP Medhoc (2000)Google Scholar
  7. 7.
    Dai, F., Wu, J.: On constructing k-connected k-dominating set in wireless networks. In: Proc. of IPDPS (2005)Google Scholar
  8. 8.
    Deb, K.: An Efficient Constraint Handling Method for GAs. Computer Methods in Applied Mechanics and Engineering 186(2/4), 311–338 (2000)zbMATHCrossRefGoogle Scholar
  9. 9.
    Srinivas, N., Deb, K.: Multi-Objective function optimization using non-dominated sorting genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)CrossRefGoogle Scholar
  10. 10.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization:NSGA-II. In: PPSN VI. Proceedings of Parallel Problem Solving from Nature, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Deband, K., Agarwal, R.B.: Simulated Binary Crossover for Continuous Search Space. Complex Systems (9), 115–148 (1995)Google Scholar
  12. 12.
    Abrams, Z., Goel, A., Plotkin, S.: Set k-cover algorithms for energy efficient monitoring in wireless sensor networks. In: Ramchandran, K., Sztipanovits, J. (eds.) Proc. of the 3rd Int’l Conf. on Information Processing in Sensor Networks, pp. 424–432. ACM Press, Berkeley (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dingxing Zhang
    • 1
    • 2
  • Ming Xu
    • 1
  • Wei Xiao
    • 3
  • Junwen Gao
    • 4
  • Wenshen Tang
    • 1
  1. 1.School of Computer, National University of Defense Technology, ChangshaChina
  2. 2.Guangdong Tech. College of Water Resources & Electric, GuangzhouChina
  3. 3.College of Mathematics and Computer Science, Hunan Normal University, Changsha 
  4. 4.School of Mechanical Engineering, South China University of tech. GuangzhouChina

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