Abstract

Wireless Sensor Networks (WSNs) play a critical role in environmental monitoring, healthcare, disaster management, and smart infrastructure. However, the limited energy resources of sensor nodes remain a pressing challenge, particularly in data aggregation and transmission processes, where redundancy and inefficient routing can significantly shorten network lifetime. To address this problem, we propose a Hybrid Deep Reinforcement Learning (HDRL) framework that optimizes data aggregation while balancing energy consumption and communication overhead. The method integrates the decision making capability of reinforcement learning with the representational power of deep neural networks, enabling adaptive node selection and dynamic routing based on real-time energy and network states. The proposed HDRL model employs a dual-agent mechanism: the first agent focuses on cluster head selection for balanced energy distribution, while the second agent optimizes multi-hop routing paths to minimize redundant transmissions. A reward function is designed to jointly consider residual energy, data latency, and transmission reliability. Simulation results show that the HDRL-based approach outperforms traditional clustering and reinforcement learning methods in terms of network lifetime extension, reduced packet loss, and improved throughput. Notably, the proposed method achieves up to 30% improvement in energy efficiency and 25% reduction in end-to end delay, making it highly suitable for large-scale, real-time WSN applications.

Authors

Ramdas D. Gore¹, R. Rajavignesh²
National Forensic Sciences University, India¹, K.S.K College of Engineering and Technology, India ²

Keywords

Wireless Sensor Networks, Data Aggregation, Deep Reinforcement Learning, Energy Efficiency, Adaptive Routing