Energy-Aware UAV Relaying with SWIPT and Real-Time Reinforcement Learning for Disaster Response

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.