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Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular

Franciszek Seredynski1, Miroslaw Szaban1, Jaroslaw Skaruz1

  • 1University of Siedlce, Institute of Computer Science, 08-110 Siedlce, Poland.

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Summary
This summary is machine-generated.

This study introduces self-organizing algorithms for Wireless Sensor Networks (WSNs) using game theory. (ϵ,h)-learning automata agents outperform cellular automata agents in solving the coverage problem efficiently.

Keywords:
adaptive cellular automatacollective behaviorlearning automatanetwork coverage problemself-organizationsensor networksspatial prisoner’s dilemma

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face challenges in distributed coverage optimization.
  • Existing solutions often require central coordination, which is not ideal for WSNs.

Purpose of the Study:

  • To develop self-organizing algorithms for distributed coverage in WSNs.
  • To apply a game-theoretical framework using agent-based reinforcement learning.

Main Methods:

  • A game-theoretical framework based on the Spatial Prisoner's Dilemma game was adapted.
  • Multi-agent systems were modeled using graph theory, with nodes as agents.
  • Two agent types were used: Learning Automata (LA) and Cellular Automata (CA).
  • A novel (ϵ,h)-learning automata agent model was developed and compared to adaptive CA agents.

Main Results:

  • Agents reaching Nash equilibria in iterated games led to global optimization of coverage and sensor usage.
  • The (ϵ,h)-learning automata model significantly outperformed the cellular automata model.
  • Distributed coverage was achieved without agents knowing the global criterion or needing a central coordinator.

Conclusions:

  • Game-theoretical multi-agent systems offer an effective approach for distributed WSN coverage.
  • The proposed (ϵ,h)-learning automata model provides a superior solution compared to CA for this problem.