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On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.

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  • 1Department of Computing and Information Systems, Universidade Federal de Ouro Preto, João Monlevade, 359301-026, Brasil flavio@decea.ufop.br.

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This study introduces novel geometric operators for combinatorial optimization problems. A genetic algorithm (GA) using these operators outperforms integer linear programming (ILP) for wireless sensor network dynamic coverage and connectivity problems (WSN-DCCP), enhancing network lifetime and reducing computation time.

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Wireless sensor networksdynamic optimizationgenetic algorithmsgeometric operators

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Area of Science:

  • * Computational intelligence and optimization algorithms.
  • * Network engineering and wireless sensor networks.

Background:

  • * Geometric approaches in combinatorial optimization can describe fitness landscapes and guide evolutionary operators.
  • * Existing methods for wireless sensor network dynamic coverage and connectivity problems (WSN-DCCP) often use proxy objectives and greedy strategies.

Purpose of the Study:

  • * To introduce new geometric operators for combinatorial search spaces.
  • * To apply these operators within a genetic algorithm (GA) for the WSN-DCCP.
  • * To compare the GA's performance against an integer linear programming (ILP) formulation.

Main Methods:

  • * Development of novel geometric operators based on descent directions and subspaces.
  • * Implementation of a genetic algorithm (GA) incorporating these geometric operators for WSN-DCCP.
  • * Formulation of an ILP model with a proxy objective (energy consumption) and greedy dynamics for comparison.

Main Results:

  • * The proposed GA successfully outperformed the ILP formulation in maximizing network lifetime for WSN-DCCP.
  • * The GA demonstrated significantly reduced computational times, especially for large problem instances.
  • * This represents the first known algorithm to surpass ILP-based lifetime synthesis for this problem.

Conclusions:

  • * Geometric operators offer a powerful new paradigm for combinatorial optimization.
  • * The developed GA provides a more effective and efficient solution for WSN-DCCP compared to traditional ILP methods.
  • * This work highlights the potential of geometric approaches in advancing network optimization and longevity.