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Minimum energy target tracking with coverage guarantee in wireless sensor networks

Author

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  • Lersteau, Charly
  • Rossi, André
  • Sevaux, Marc

Abstract

Wireless Sensor Networks (WSN) are composed of low-cost sensors designed to monitor targets inside their sensing range. The sensors are randomly dispatched in a region and have a limited battery capacity. The targets are moving and their trajectory are subject to uncertainty. A way to save energy of the WSN is to activate subsets of sensors covering all the targets. The challenge of this paper is to preserve and balance the residual capacities of the sensors in order to perform further target tracking missions using the same WSN. A two-step exact method is proposed. First, the input data is processed in order to design a mathematical formulation. Second, a column generation algorithm, combined with a GRASP metaheuristic, assigns activation time to sensors.

Suggested Citation

  • Lersteau, Charly & Rossi, André & Sevaux, Marc, 2018. "Minimum energy target tracking with coverage guarantee in wireless sensor networks," European Journal of Operational Research, Elsevier, vol. 265(3), pages 882-894.
    • Handle: RePEc:eee:ejores:v:265:y:2018:i:3:p:882-894
    DOI: 10.1016/j.ejor.2017.08.045
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    References listed on IDEAS

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    1. Lersteau, Charly & Rossi, André & Sevaux, Marc, 2016. "Robust scheduling of wireless sensor networks for target tracking under uncertainty," European Journal of Operational Research, Elsevier, vol. 252(2), pages 407-417.
    2. Castaño, Fabian & Bourreau, Eric & Velasco, Nubia & Rossi, André & Sevaux, Marc, 2015. "Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks," European Journal of Operational Research, Elsevier, vol. 241(1), pages 28-38.
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    Citations

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    Cited by:

    1. Melo, Rafael A. & Queiroz, Michell F. & Ribeiro, Celso C., 2021. "Compact formulations and an iterated local search-based matheuristic for the minimum weighted feedback vertex set problem," European Journal of Operational Research, Elsevier, vol. 289(1), pages 75-92.
    2. Meihua Wang & Wei-Chang Yeh & Ta-Chung Chu & Xianyong Zhang & Chia-Ling Huang & Jun Yang, 2018. "Solving Multi-Objective Fuzzy Optimization in Wireless Smart Sensor Networks under Uncertainty Using a Hybrid of IFR and SSO Algorithm," Energies, MDPI, vol. 11(9), pages 1-23, September.
    3. Ridhi Kapoor & Sandeep Sharma, 2023. "Glowworm Swarm Optimization (GSO) based energy efficient clustered target coverage routing in Wireless Sensor Networks (WSNs)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 622-634, May.
    4. Calvete, Herminia I. & del-Pozo, Lourdes & Iranzo, José A., 2018. "Dealing with residual energy when transmitting data in energy-constrained capacitated networks," European Journal of Operational Research, Elsevier, vol. 269(2), pages 602-620.
    5. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).

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