Synergic Impact of Reinforcement Learning and Swarm Intelligence in Wireless Sensor Network Services

Wireless sensor networks (WSNs) consist of distributed sensor nodes deployed for real-time monitoring and data collection. Optimizing sensor energy consumption is critical for extending the overall network lifespan. In large-scale WSNs, clustering techniques are required to reduce energy consumption. Many effective clustering methods have been proposed, but finding the optimal number of clusters in an energy-efficient manner remains challenging. Swarm intelligence (SI) algorithms help solve this problem, but testing all possible cluster configurations is computationally expensive. Neural networks excel in identifying hidden patterns in data, making them a promising tool for this task. However, training an AI agent to accurately predict both the number of cluster heads (CHs) and their locations is difficult. In this study, we developed a synergic method by employing a reinforcement learning (RL) model to predict the number of CHs while utilizing an SI algorithm to identify the most appropriate nodes to become CHs. This approach minimizes transmission energy and prolongs the lifespan of WSNs and their services.