AMBIENT IOT AND AI-RAN FOR SUSTAINABLE SMART AGRICULTURE: ENERGY-NEUTRAL WIRELESS INTELLIGENCE IN FUTURE 6G RURAL NETWORKS

Abstract

The emergence of sixth-generation (6G) wireless networks is expected to revolutionize smart agriculture through intelligent, energy-efficient, and sustainable communication technologies. Conventional Internet of Things (IoT) systems rely on battery-powered sensors, leading to frequent maintenance, limited operational lifetime, and increased deployment costs in large agricultural fields. This paper proposes an Ambient IoT and AIRAN-enabled framework for sustainable smart agriculture that supports energy-neutral wireless intelligence in future 6G rural networks. The framework integrates battery-free Ambient IoT sensors with radio frequency (RF) energy harvesting and ambient backscatter communication to enable continuous environmental monitoring without conventional power sources. Artificial Intelligence-based Radio Access Networks (AI-RAN) utilize Deep Reinforcement Learning (DRL) to optimize spectrum allocation, communication scheduling, and resource management under dynamic field conditions. Edge intelligence processes real-time data collected from soil moisture, temperature, humidity, and crop health sensors to support precision farming. Additionally, a Digital Twin creates a virtual representation of the agricultural environment for continuous monitoring, while Federated Learning enables collaborative model training without sharing sensitive farm data. Experimental analysis demonstrates improvements in energy efficiency, network lifetime, communication reliability, and resource utilization compared with conventional IoT-based agricultural systems. The proposed framework provides a scalable, intelligent, and sustainable solution for next-generation precision agriculture powered by AI-native 6G rural networks.