Encyclopedia of Algorithms

Living Edition
| Editors: Ming-Yang Kao

Maximum Lifetime Coverage

  • Weili Wu
  • Ling Ding
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27848-8_617-1

Years and Authors of Summarized Original Work

2012; Ding, Ling; Wu, Weili; Willson, James; Wu, Lidong; Lu, Zaixin; Lee, Wonjun2005; Cardei, Mihaela; Thai, My T.; Li, Yingshu; Wu, Weili

Problem Definition

Energy resources are very limited in wireless sensor networks since the wireless devices are small and battery powered. There are two ways to deploy wireless sensor networks. One is deterministic [ 1] deployment, and the other one is stochastic or random deployment [ 2]. In deterministic deployment, the goal is to minimize the number of sensors. In the latter one, the goal is to improve coverage ratio. Normally, in random or stochastic deployment, one target is covered by several sensors. It is unnecessary and a waste of energy to activate all sensors around the target to monitor it. We can prolong the coverage duration through making sleep/activate schedules in wireless sensor networks when we don’t have abundant energy resources. In Fig.  1, t 1, t 2, and t 3 are three targets. s 1, s 2,...

Keywords

Coverage Wireless sensor network Approximation Algorithm Constant Lifetime 
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Recommended Reading

  1. 1.
    Bai X, Xuan D, Yun Z, Lai TH, Jia W (2008) Complete optimal deployment patterns for full-coverage and k-connectivity (k<=6) wireless sensor networks. In: Proceedings of ACM MobiHoc, Hong KongGoogle Scholar
  2. 2.
    Balister P, Bollobas B, Sarkar A, Kumar S (2007) Reliable density estimates for coverage and connectivity in thin strips of finite length. In: Proceedings of ACM MobiCom, MontréalGoogle Scholar
  3. 3.
    Ding L, Wu W, Willson J, Wu L, Lu Z, Lee W (2012) Constant-approximation for target coverage problem in wireless sensor networks. In: INFOCOM, Orlando, pp 1584–1592Google Scholar
  4. 4.
    Cardei M, Thai MT, Li Y, Wu W (2005) Energy-efficient target coverage in wireless sensor networks. In: Proceedings of IEEE INFOCOM, MiamiGoogle Scholar
  5. 5.
    Huang Y, Gao X, Zhang Z, Wu W (2009) A better constant-factor approximation for weighted dominating set in unit disk graph. J Comb Optim 18(2):179–194CrossRefMathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Computer Science, University of Texas at DallasDallasUSA
  2. 2.Institute of Technology, University of Washington TacomaTacomaUSA