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Scheduling Sensors Activity in Wireless Sensor Networks

  • Antonina Tretyakova
  • Franciszek Seredynski
  • Frederic Guinand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

In this paper we consider Maximal Lifetime Coverage Problem in Wireless Sensor Networks which is formulated as a scheduling problem related to activity of sensors equipped at battery units and monitoring a two-dimensional space in time. The problem is known as an NP-hard and to solve it we propose two heuristics which use specific knowledge about the problem. The first one is proposed by us stochastic greedy algorithm and the second one is metaheuristic known as Simulated Annealing. The performance of both algorithms is verified by a number of numerical experiments. Comparison of the results show that while both algorithms provide results of similar quality, but greedy algorithm is slightly better in the sense of computational time complexity.

Keywords

Maximum lifetime coverage problem Metaheuristics Energy-efficient coverage preserving protocol 

References

  1. 1.
    Nesamony, S., Vairamuthu, M.K., Orlowska, M.E., Sadiq, S.W.: On sensor network segmentation for urban water distribution monitoring. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 974–985. Springer, Heidelberg (2006). doi: 10.1007/11610113_104CrossRefGoogle Scholar
  2. 2.
    Pierce, F.J., Elliott, T.V.: Regional and on-farm wireless sensor networks for agricultural systems in Eastern Washington. Comput. Electron. Agric. 61(1), 32–43 (2008)CrossRefGoogle Scholar
  3. 3.
    Cardei, M., Wu, J.: Energy-efficient coverage problems in wireless ad-hoc sensor networks. J. Comput. Commun. Arch. 29, 413–420 (2006)CrossRefGoogle Scholar
  4. 4.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co., New York (1979)zbMATHGoogle Scholar
  5. 5.
    Sahoo, B., Ravu, V., Patel, P.: Observation on using genetic algorithm for extending the lifetime of wireless sensor networks. In: IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network, pp. 9–13 (2011)Google Scholar
  6. 6.
    Fayyazi, H., Sabokrou, M., Hosseini, M., Sabokrou, A.: Solving heterogeneous coverage problem in Wireless Multimedia Sensor Networks in a dynamic environment using Evolutionary Strategies. In: ICCKE2011, Mashhad, Iran, 13–14 October 2011Google Scholar
  7. 7.
    Abbasi, M., Abd Latiff, M.S., Modirkhazeni, A., Anisi, M.H.: Optimization of wireless sensor network coverage based on evolutionary algorithm. IJCCN 1(1), 104 (2011)Google Scholar
  8. 8.
    Gil, J.M., Han, Y.H.: A target coverage scheduling scheme based on genetic algorithms in directional sensor networks. Sensors 11(2), 1888–1906 (2011)CrossRefGoogle Scholar
  9. 9.
    Tretyakova, A., Seredynski, F.: Application of evolutionary algorithms to maximum lifetime coverage problem in wireless sensor networks. In: 27-th IEEE IPDPS, Boston, USA (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Antonina Tretyakova
    • 1
  • Franciszek Seredynski
    • 1
  • Frederic Guinand
    • 1
    • 2
  1. 1.Department of Mathematics and Natural SciencesCardinal Stefan Wyszynski University in WarsawWarsawPoland
  2. 2.LITIS LaboratoryUniversity of Le HavreLe HavreFrance

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