Multi-agent Deep Reinforcement Learning for Energy-Efficient Cluster Head Selection in IoT-Based Wireless Sensor Networks

Abstract

To monitor large physical spaces, modern industrial and urban Internet of Things (IoT) architectures depend on data collected by Wireless Sensor Networks (WSNs). Crucially, battery life remains the absolute limiting factor. Because these tiny sensor nodes run on limited, non-rechargeable power, keeping the network alive for long periods is incredibly difficult. To stretch these limited power reserves, current systems group nodes into clusters. These protocols group the nodes together so that a single coordinator—the Cluster Head (CH)—collects all local data and routes it to the main base station. However, static heuristics and centralized systems are simply too rigid. They fail completely if data traffic surges or if the physical network layout changes unexpectedly. To bridge this gap, we designed a decentralized Multi-Agent Deep Reinforcement Learning (MADRL) framework specifically for selecting CHs efficiently. Instead of relying on a central controller, we treat each sensor node as an independent agent. These agents work together to cut down overall energy loss while keeping network coverage wide. We built a Deep Q-Network (DQN) directly into each agent, allowing them to learn smart clustering habits on the fly. The nodes make these choices by tracking their own remaining power, how close they are to neighbors, and their distance to the base station. Our simulations show excellent results. This decentralized MADRL setup drastically lowers energy use, keeps individual nodes alive longer, and extends network lifespan far better than traditional LEACH or genetic algorithms.