A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks

Suhasini; Damandeep

doi:https://doi.org/10.54216/IJWAC.100204

Full Length Article

Volume 10 • Issue 2 • PP: 27–35 • 2026

A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks

Suhasini Monga ^1*

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,

Damandeep Kaur ²

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¹Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

²Department of CSE, Chandigarh University, Mohali, India

* Corresponding Author.

DOI https://doi.org/10.54216/IJWAC.100204

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Received: January 31, 2026 Revised: March 08, 2026 Accepted: May 07, 2026

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Abstract

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.

Keywords

Wireless sensor networks Energy harvesting Deep Q-Network Adaptive routing Network lifetime Solar power LSTM forecasting Reinforcement learning IoT sustainability

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Monga, Suhasini, Kaur, Damandeep. "A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks." International Journal of Wireless and Ad Hoc Communication, vol. Volume 10, no. Issue 2, 2026, pp. 27–35. DOI: https://doi.org/10.54216/IJWAC.100204

Monga, S., Kaur, D. (2026). A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks. International Journal of Wireless and Ad Hoc Communication, Volume 10(Issue 2), 27–35. DOI: https://doi.org/10.54216/IJWAC.100204

Monga, Suhasini, Kaur, Damandeep. "A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks." International Journal of Wireless and Ad Hoc Communication Volume 10, no. Issue 2 (2026): 27–35. DOI: https://doi.org/10.54216/IJWAC.100204

Monga, S., Kaur, D. (2026) 'A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks', International Journal of Wireless and Ad Hoc Communication, Volume 10(Issue 2), pp. 27–35. DOI: https://doi.org/10.54216/IJWAC.100204

Monga S, Kaur D. A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks. International Journal of Wireless and Ad Hoc Communication. 2026;Volume 10(Issue 2):27–35. DOI: https://doi.org/10.54216/IJWAC.100204

S. Monga, D. Kaur, "A Deep Reinforcement Learning Framework with Solar Energy Forecasting for Adaptive Routing and Lifetime Extension in Energy-Harvesting Wireless Sensor Networks," International Journal of Wireless and Ad Hoc Communication, vol. Volume 10, no. Issue 2, pp. 27–35, 2026. DOI: https://doi.org/10.54216/IJWAC.100204

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