Game-Theoretic and Reinforcement Learning-Based Cluster Head Selection for Energy-Efficient Wireless Sensor Network

2025-08-18

AI Summary

In wireless sensor networks (WSNs), suboptimal cluster head (CH) selection leads to uneven energy consumption, premature node failure, and unpredictable network lifetime. To address these issues, this paper proposes a dynamic CH selection mechanism integrating game theory and multi-agent reinforcement learning (MARL). The approach constructs an energy-aware utility function and a distributed game-theoretic model, coupled with a multi-stage adaptive clustering strategy, to enable dynamic CH election and balanced energy distribution. Its key innovation lies in the synergistic combination of non-cooperative game modeling and MARL-based global optimization, reconciling rational local decision-making with holistic energy-efficiency objectives. Experimental results demonstrate that the proposed mechanism extends network lifetime by approximately 32%, reduces energy consumption variance by 41%, enhances topological stability, and enables predictable network degradation—thereby significantly lowering operational and maintenance costs.

Technology Category

• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Energy in Wireless Sensor Networks (WSNs) is critical to network lifetime and data delivery. However, the primary impediment to the durability and dependability of these sensor nodes is their short battery life. Currently, power-saving algorithms such as clustering and routing algorithms have improved energy efficiency in standard protocols. This paper proposes a clustering-based routing approach for creating an adaptive, energy-efficient mechanism. Our system employs a multi-step clustering strategy to select dynamic cluster heads (CH) with optimal energy distribution. We use Game Theory (GT) and Reinforcement Learning (RL) to optimize resource utilization. Modeling the network as a multi-agent RL problem using GT principles allows for self-clustering while optimizing sensor lifetime and energy balance. The proposed AI-powered CH-Finding algorithm improves network efficiency by preventing premature energy depletion in specific nodes while also ensuring uniform energy usage across the network. Our solution enables controlled power consumption, resulting in a deterministic network lifetime. This predictability lowers maintenance costs by reducing the need for node replacement. Furthermore, our proposed method prevents sensor nodes from disconnecting from the network by designating the sensor with the highest charge as an intermediary and using single-hop routing. This approach improves the energy efficiency and stability of Wireless Sensor Network (WSN) deployments.

Problem

• Optimize energy distribution in WSNs using GT and RL
• Prevent premature energy depletion in sensor nodes
• Ensure uniform energy usage and network stability

Innovation

• Game Theory optimizes dynamic cluster head selection
• Reinforcement Learning balances energy usage efficiently
• AI-powered algorithm prevents premature energy depletion