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Title	Effective Node Deployment In Wireless Sensor Networks Using Reinforcement Learning
ID_Doc	22075
Authors	Priyadarshi R.; Teja P.R.; Vishwakarma A.K.; Ranjan R.
Year	2025
Published	2025 IEEE 14th International Conference on Communication Systems and Network Technologies, CSNT 2025
DOI	http://dx.doi.org/10.1109/CSNT64827.2025.10968953
Abstract	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. © 2025 IEEE.
Author Keywords	Coverage; Energy Efficiency; Network Lifetime; Node Deployment; Optimization; Reinforcement Learning; Wireless Sensor Networks (WSNs)

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