2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 349-360, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
P. Keerthana 1 * , A. Vijayalakshmi 2
Doi: https://doi.org/10.54216/JISIoT.180127
Wireless sensor networks used in disaster-struck areas experience the problem of energy constraints, which may negatively affect the data communication process. A novel energy-aware UAV relaying scheme is presented that incorporates SWIPT (Simultaneous Wireless Information and Power Transfer) to power the UAVs and their ground sensor devices. Dynamic power and flight path allocation according to the environmental conditions is achieved with dynamic reinforcement learning and, in particular, with a Proximal Policy Optimization (PPO) method. The system maximizes energy gathering at the sensor nodes and lengthens UAV flight life, and preserves high-quality signal transmission. The findings indicate a 23.5 dB increase in the SINR, 83.2 percent efficiency of energy harvesting, and an average of 43.2 minutes of endurance for the UAV. The success rate on the relay was 94.6 per cent, and a convergence of 12.3 seconds. The model also took the lead over other past ways in terms of mission coverage and energy efficiency in various simulation cases. This system enhances the resilience of disaster communication by effectively utilizing energy resources. Finally, it makes adaptation in real time and continued work in high-danger situations possible.
Wireless Sensor Device , Simultaneous Wireless Information and Power Transfer , Proximal Policy Optimization , Signal-to-Interference-plus-Noise Ratio , Base Station , Robot Operating System 2
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