A Deep Reinforcement Learning Framework with Solar Energy
Forecasting for Adaptive Routing and Lifetime Extension in
Energy-Harvesting Wireless Sensor Networks
Suhasini Monga1,* Damandeep Kaur2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
2Department of CSE, Chandigarh University, Mohali, India
Emails: suhasini.monga@gmail.com · daman03.cu@gmail.com
Received: January 31, 2026 Revised: March 08, 2026 Accepted: May 07, 2026 ⋆ Corresponding author
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