aspg-20-4362-1791307782
20261006172942
American Scientific Publishing Group
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American Scientific Publishing Group
International Journal of Wireless and Ad Hoc Communication
IJWAC
2692-4056
2026
10
2
A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks
Suhasini
Monga
Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
Damandeep
Kaur
Department of CSE, Chandigarh University, Mohali, India
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Battery-powered sensor nodes expire when their energy reserves are depleted, terminating data collection regardless of the physical integrity of the hardware. Solar harvesting offers a viable path to perpetual operation, but only when the routing layer can continuously track the time-varying energy state of every node and steer traffic away from nodes likely to be power-starved in the near future. Classical clustering and chain-based protocols select forwarding paths without regard to harvested energy, leading to premature node death even when sufficient solar income would have been available to sustain operation. This paper presents a deep reinforcement learning framework in which each sensor node operates an independent Deep Q-Network agent that adapts its next-hop forwarding decision based on local battery state, short-horizon solar energy forecasts, link quality estimates, and the residual energy levels of candidate neighbours. A lightweight LSTM sub-model provides the solar prediction horizon that the agent uses as part of its state representation, enabling it to distinguish nodes that are temporarily depleted but will recover from those whose batteries are trending toward permanent failure. Extensive simulation across a 100-node deployment over 3,000 operational rounds confirms that the proposed approach substantially extends network lifetime, improves packet delivery, and reduces wasted harvested energy compared with five competitive baselines. Reward function ablation, scalability experiments, and an energy neutrality verification further validate the design choices and confirm stability across a wide range of deployment conditions.
2026
27
35
4362
https://creativecommons.org/licenses/by/4.0/
10.54216/IJWAC.100204
https://americaspg.com/journal/20/article/4362
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https://americaspg.com/storage/31779217931.pdf