A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Area of Science:

• Computer Science
• Network Engineering
• Game Theory

Background:

• Clustering is vital for wireless sensor network (WSN) scalability and lifetime.
• Efficient cluster head (CH) selection is crucial for energy distribution in clustered WSNs.
• Game theory offers a framework for modeling rational, selfish sensor node behavior in clustering.

Purpose of the Study:

• To develop a game theoretic approach for balancing energy consumption in clustered WSNs.
• To address the challenge of finding equilibrium strategies that maximize sensor node payoffs.
• To enhance network lifetime through optimized energy distribution.

Main Methods:

• A novel payoff function was designed to model realistic sensor behaviors and energy heterogeneity.
• A penalty mechanism was incorporated to encourage higher-energy nodes to compete for CH roles.
• Convex optimization was used to derive the Nash equilibrium (NE) strategy for the clustering game.

Main Results:

• The proposed approach effectively balances energy consumption among sensor nodes.
• Sensor nodes achieve maximal individual payoffs by adhering to the derived NE strategy.
• Simulations demonstrate a significant enhancement in overall network lifetime.

Conclusions:

• The game theoretic clustering approach provides an effective solution for energy balancing in WSNs.
• Considering energy heterogeneity through a penalty mechanism improves CH selection fairness.
• The method offers a practical strategy for prolonging WSN operational duration.