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A Multiobjective Evolutionary Algorithm for Surveillance Sensor Placement

Author

Listed:
  • Kamyoung Kim
  • Alan T Murray
  • Ningchuan Xiao

    (Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210, USA)

Abstract

Automated or semiautomated surveillance monitoring involves movement tracking and sensor handoff. In order to track moving objects over a large area, sensor coverage needs to overlap significantly. Overlapping coverage can be modeled using the concept of backup coverage, a location modeling approach that seeks to maximize primary and backup coverage simultaneously. This kind of sensor placement problem belongs to the class of NP-hard combinatorial optimization problems, so computational difficulty is expected when solving large problem instances, not to mention the need for dealing with multiple objectives. Beyond this, backup coverage for supporting sensor placement actually brings about confounding problem instances for branch-and-bound approaches because of the trade-off between primary and backup coverage. To address these difficulties, this paper develops a multiobjective evolutionary algorithm for the backup coverage problem to support sensor placement. The solutions of this algorithm are evaluated in terms of computational requirements and solution quality.

Suggested Citation

  • Kamyoung Kim & Alan T Murray & Ningchuan Xiao, 2008. "A Multiobjective Evolutionary Algorithm for Surveillance Sensor Placement," Environment and Planning B, , vol. 35(5), pages 935-948, October.
    • Handle: RePEc:sae:envirb:v:35:y:2008:i:5:p:935-948
    DOI: 10.1068/b33139
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    References listed on IDEAS

    as
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    3. Ningchuan Xiao & David A Bennett & Marc P Armstrong, 2002. "Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems," Environment and Planning A, , vol. 34(4), pages 639-656, April.
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