An MPS-BNS mixed strategy based on game theory for wireless mesh networks

Area of Science:

• Computer Science
• Network Engineering
• Game Theory

Background:

• Selfish nodes pose a significant challenge to the efficiency and reliability of wireless mesh networks.
• Packet forwarding games are often used to model and analyze node behavior in these networks.
• Existing strategies may not adequately address varying levels of selfishness.

Purpose of the Study:

• To propose and evaluate a novel strategy, the mixed Maximum Payoff Strategy (MPS) and Best Neighbor Strategy (BNS), to mitigate selfish behavior in wireless mesh networks.
• To enhance packet forwarding efficiency and network performance in the presence of non-cooperative nodes.
• To analyze the strategy's effectiveness compared to existing methods, particularly under conditions of low initial cooperation.

Main Methods:

• The study introduces a mixed strategy combining Maximum Payoff Strategy (MPS) and Best Neighbor Strategy (BNS).
• Nodes engage in packet forwarding games with neighbors, recording payoffs.
• Strategies are updated probabilistically based on game outcomes, with MPS maximizing individual payoff and BNS adopting the highest-earning neighbor's strategy.

Main Results:

• The mixed MPS-BNS strategy demonstrates a faster evolution towards maximum average payoff compared to the pure BNS strategy.
• The strategy proves effective in improving performance even when starting with low cooperation levels.
• Simulation analysis confirms the strategy's ability to handle diverse levels of node selfishness.

Conclusions:

• The mixed MPS-BNS strategy is a robust and effective approach for addressing selfish behavior in wireless mesh networks.
• This strategy offers a significant performance improvement in packet forwarding, especially in scenarios with initial low cooperation.
• The proposed method provides a valuable tool for enhancing the overall performance and reliability of wireless mesh networks.