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Energy Efficient Sweep Coverage with Mobile and Static Sensors

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Algorithms and Discrete Applied Mathematics (CALDAM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8959))

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Abstract

Sweep coverage provides solution for the applications in wireless sensor networks, where periodic monitoring is sufficient instead of continuous monitoring. For a given set of points in the plane, the objective of the sweep coverage problem is to minimize number of sensors required in order to guarantee sweep coverage for a given set of points of interest. Instead of using only mobile sensors for sweep coverage, use of both static and mobile sensors can be more effective in terms of energy utilization. In this paper we introduce the EEGSweep coverage problem, where objective is to minimize energy consumption by a set of sensors (mobile and/or static) with guaranteed sweep coverage for a given set of points. We prove that the EEGSweep coverage problem is NP-hard and cannot be approximated within a factor of 2. We propose an 8-approximation algorithm to solve the problem. A 2-approximation algorithm is also proposed for a special case of this problem.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology, Guwahati, India

    Barun Gorain & Partha Sarathi Mandal

Authors
  1. Barun Gorain
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  2. Partha Sarathi Mandal
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, I.I.T. Kanpur, 208016, India

    Sumit Ganguly

  2. School of Computing Science, Simon Fraser University, V5A 1S6, Burnaby, BC, Canada

    Ramesh Krishnamurti

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Gorain, B., Mandal, P.S. (2015). Energy Efficient Sweep Coverage with Mobile and Static Sensors. In: Ganguly, S., Krishnamurti, R. (eds) Algorithms and Discrete Applied Mathematics. CALDAM 2015. Lecture Notes in Computer Science, vol 8959. Springer, Cham. https://doi.org/10.1007/978-3-319-14974-5_26

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  • DOI: https://doi.org/10.1007/978-3-319-14974-5_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14973-8

  • Online ISBN: 978-3-319-14974-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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