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Optimal Online and Offline Algorithms for Robot-Assisted Restoration of Barrier Coverage

  • J. Czyzowicz
  • E. Kranakis
  • D. Krizanc
  • L. Narayanan
  • J. Opatrny
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8952)

Abstract

Cooperation between mobile robots and wireless sensor networks is a line of research that is currently attracting a lot of attention. In this context, we study the following problem of barrier coverage by stationary wireless sensors that are assisted by a mobile robot with the capacity to move sensors. Assume that \(n\) sensors are initially arbitrarily distributed on a line segment barrier. Each sensor is said to cover the portion of the barrier that intersects with its sensing area. Owing to incorrect initial position, or the death of some of the sensors, the barrier is not completely covered by the sensors. We employ a mobile robot to move the sensors to final positions on the barrier such that barrier coverage is guaranteed. We seek algorithms that minimize the length of the robot’s trajectory, since this allows the restoration of barrier coverage as soon as possible. We give an optimal linear-time offline algorithm that gives a minimum-length trajectory for a robot that starts at one end of the barrier and achieves the restoration of barrier coverage. We also study two different online models: one in which the online robot does not know the length of the barrier in advance, and the other in which the online robot knows it in advance. For the case when the online robot does not know the length of the barrier, we prove a tight bound of \(3/2\) on the competitive ratio, and we give a tight lower bound of \(5/4\) on the competitive ratio in the other case. Thus for each case we give an optimal online algorithm.

Keywords

Line Segment Wireless Sensor Network Mobile Robot Optimal Trajectory Competitive Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • J. Czyzowicz
    • 1
  • E. Kranakis
    • 2
  • D. Krizanc
    • 3
  • L. Narayanan
    • 4
  • J. Opatrny
    • 4
  1. 1.Département d’informatiqueUniversité du Québec en OutaouaisGatineauCanada
  2. 2.School of Computer ScienceCarleton UniversityOttawaCanada
  3. 3.Department of Mathematics and Computer ScienceWesleyan UniversityMiddletownUSA
  4. 4.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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