Effective Node Deployment in Wireless Sensor Networks Using Reinforcement Learning

• Published in: Proceedings (International Conference on Communication Systems and Network Technologies Online) pp. 1113 - 1118
• Main Authors: Priyadarshi, Rahul, Teja, Panduranga Ravi, Vishwakarma, Anish Kumar, Ranjan, Rakesh
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.03.2025
• Subjects: Adaptation models; Coverage; Energy consumption; Energy efficiency; Environmental monitoring; Network Lifetime; Node Deployment; Optimization; Reinforcement learning; Smart cities; Wireless sensor networks; Wireless Sensor Networks (WSNs)
• ISSN: 2473-5655
• DOI: 10.1109/CSNT64827.2025.10968953

Wireless Sensor Networks (WSNs) are vital for applications like environmental monitoring and smart cities, but effective node deployment remains challenging. Traditional methods often fail in dynamic scenarios. This paper proposes a Reinforcement Learning (RL)-based framework to optimize WSN node deployment, maximizing coverage while minimizing energy use and extending network lifetime. The RL model adapts to environmental changes and varying network conditions, ensuring robust performance in static, dynamic, and high-density environments. Experimental results show the RL approach outperforms conventional methods, achieving better coverage, lower energy consumption, and improved network longevity. The study demonstrates RL's adaptability and effectiveness in addressing WSN deployment challenges, enabling intelligent, self-optimizing networks for dynamic real-world applications.