Deep Reinforcement Learning for Optimized Energy Harvesting in Wireless Sensor Networks

The authors present an innovative energy harvesting management solution for Wireless Sensor Networks using Deep Reinforcement Learning algorithms. Limited energy resources in Wireless Sensor Networks demand critical energy-efficient strategies to extend their operational lifespan while maintaining reliable operation. By using DRL-based technology the system allows dynamic changes in energy levels which optimize power harvesting activities together with storage management to reduce power consumption. Application of the process control model confirmed superiority through extensive testing against traditional Q-learning and greedy-based and randomized approaches. The DRL-based system showed 30% improved efficiency for energy use while offering extended operation times of 10-15 hours and reduced energy consumption levels dramatically. The DRL-based system delivers consistent energy efficiency during tests that involve multiple node density conditions. The energy storage management component of the DRL system showed outstanding performance through its enhanced energy recovery plus discharge efficiency which supported sustained network stability. DRL demonstrates great potential for WSN sustainability and robustness in energy-limited applications and presents itself as a powerful solution for critically constrained network environments. Research in the future will concentrate on improving the DRL model design alongside the implementation of advanced energy storage solutions to facilitate real-world system implementation.

Hali tarjima qilinmagan