GAIA-DRL: A Geoenvironmental Agent for Energy Optimization in Batteryless IoT Networks with Ambient Backscatter

This work addresses sustainability-driven network design for future wireless systems by integrating batteryless dense IoT networks-based on ambient backscatter communication-with geospatial intelligence and deep reinforcement learning. We propose GAIA-DRL (Geoenvironmentally-Aware Intelligent Agent with Deep Reinforcement Learning), an approach that jointly optimizes throughput, latency, energy efficiency, and interference, while incorporating land-use information such as NDVI and pasture coverage. A geospatially-informed agent controls communication policies using real territorial data. Geographic Information Systems (GIS) and remote sensing techniques enable context-aware decisions, supporting low-cost, low-carbon applications in 6G-ready IoT networks for greenhouse gas monitoring, smart agriculture, and sustainable environmental management.