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Area of Science:

  • Robotics
  • Operations Research
  • Wireless Sensor Networks

Background:

  • The carrier-based coverage repair problem involves a mobile robot replacing damaged sensors in wireless sensor networks.
  • This problem is a variant of the traveling salesman problem (TSP), specifically the one-commodity traveling salesman problem with selective pickup and delivery (1-TSP-SELPD).
  • Existing methods face challenges in efficiently solving this problem for large-scale sensor distributions.

Purpose of the Study:

  • To propose an efficient two-stage approach for solving the carrier-based coverage repair problem.
  • To reduce computation time and improve solution quality for the 1-TSP-SELPD, especially in larger sensor network scenarios.
  • To contribute to the development of effective algorithms for the one-commodity pickup-and-delivery traveling salesman problem (1-PDTSP).

Main Methods:

  • A two-stage algorithm is proposed: Stage 1 uses the Lin-Kernighan-Helsgaun (LKH) algorithm to create a Hamiltonian cycle for delivery nodes.
  • Stage 2 involves setting elliptical regions for pickup node selection and constructing a candidate edge list (CEL).
  • The CEL is iteratively updated using expansion, extension, and constriction operations until all delivery nodes are visited.

Main Results:

  • The proposed method significantly reduces computing time compared to existing approaches.
  • It achieves better results, particularly in higher-dimensional problems.
  • The method demonstrates effectiveness in handling complex sensor network configurations.

Conclusions:

  • The developed two-stage algorithm provides an efficient solution for the carrier-based coverage repair problem.
  • This approach enhances the scalability and performance for large-scale wireless sensor and robot networks.
  • The findings support the development of more efficient algorithms for the 1-PDTSP.