Energy-Efficient Routing Algorithm for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach

2025-08-20

AI Summary

To address uneven energy consumption, cluster-head overload, and short network lifetime in wireless sensor networks (WSNs), this paper proposes an energy-efficient routing algorithm integrating multi-agent Q-learning with graph optimization. The method embeds Minimum Energy Routing (MERA) and Minimum Spanning Tree (MST) heuristics into a multi-agent reinforcement learning framework and introduces an energy-aware State-of-Charge (SoC) reward function, supporting both distributed and cloud-assisted training. It dynamically coordinates cluster-head election and multi-hop path selection to jointly achieve load balancing, hotspot avoidance, and extended network lifetime. Experimental results demonstrate significant improvements in node survival rate, reduced SoC variance, and enhanced robustness. Moreover, the algorithm exhibits superior scalability and adaptability in large-scale, dynamic WSNs, outperforming conventional approaches in energy efficiency and operational longevity.

Technology Category

• Multiagent Systems: Multiagent Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Learning & Optimization for ROB

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
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Efficient energy management is essential in Wireless Sensor Networks (WSNs) to extend network lifetime and ensure reliable data transmission. This paper presents a novel method using reinforcement learning-based cluster-head selection and a hybrid multi-hop routing algorithm, which leverages Q-learning within a multi-agent system to dynamically adapt transmission paths based on the energy distribution across sensor nodes. Each sensor node is modeled as an autonomous agent that observes local state parameters, such as residual energy, distance to sink, hop count, and hotspot proximity, and selects routing actions that maximize long-term energy efficiency. After computing the optimal paths, each sensor aggregates sensed data and forwards it through intermediate nodes to a selected transmitter node, chosen based on the highest remaining State of Charge (SoC), thereby avoiding premature node depletion. To promote efficient learning, a carefully designed reward function incentivizes balanced load distribution, hotspot avoidance, and energy-aware forwarding while maintaining signal quality. The learning process occurs either in a decentralized manner or via a cloud-based controller that offloads computation in large-scale deployments. Moreover, the RL-driven routing decisions are fused with classical graph-based methods, Minimum Energy Routing Algorithm (MERA) and Minimum Spanning Tree (MST), to optimize energy consumption and load balancing. Simulations confirm that the proposed approach significantly improves node survival rate, reduces SoC variance, and enhances network resilience, making it a scalable and adaptive solution for energy-constrained WSNs in dynamic sensor deployments and IoT applications.

Problem

• Optimizing energy-efficient routing in wireless sensor networks
• Extending network lifetime through multi-agent reinforcement learning
• Balancing energy consumption and reliable data transmission

Innovation

• Multi-agent Q-learning optimizes energy-aware routing paths
• Hybrid RL-graph fusion balances load and energy consumption
• Decentralized or cloud-based adaptive learning for scalability