Dynamic resource allocation in smart agricultural IoT using reinforcement learning and 6g edge computing

Abstract Smart agricultural IoT systems face dynamic resource allocation challenges from device heterogeneity and environmental variability. In this paper, we propose a novel framework that combines 6G edge computing with reinforcement learning to optimise resource management. The framework exploits the ultra-low latency and high bandwidth of 6G for real-time communication and efficient task offloading, while reinforcement learning adapts the resource allocation strategy based on changing environment states and task requirements. A reward function is designed to balance latency reduction, resource utilisation and energy efficiency. Experimental results demonstrate 35% latency reduction and 25% resource utilization improvement over traditional approaches, with scalability validated under increasing device density and task complexity. This work provides a scalable and adaptive solution for smart agriculture, which can be used in areas such as precision irrigation, autonomous agriculture, and drone surveillance.