An Offline Learning Framework for Edge Resource Allocation and Task Offloading in Agricultural IoT With Limited Trajectory Data

In Agricultural Internet of Things (Ag-IoT), a large number of terminal sensor nodes with limited computational power and energy are widely deployed across various production sites to collect real-time data that support precision agriculture decision-making, which makes it challenging to perform complex computing tasks in real time. This article proposes an offline decision-making framework based on a compositional diffusion model that addresses the limitations of traditional offline deep reinforcement learning (DRL), which often struggles to produce high-quality policies in the presence of malicious-node interference and attacks and with insufficient decision trajectories in agricultural settings. Specifically, the proposed scheme leverages the diffusion model’s reverse generative process to synthesize numerous high-quality augmented samples from a small set of existing decision trajectories, which are then used to train robust policies, mitigating performance degradation in offline DRL due to limited trajectories and adversarial state perturbations introduced by malicious nodes. Experimental results demonstrate that the proposed method improves the quality and generalization of task offloading and resource allocation decisions, exhibiting superior stability and performance even under data sparsity and adversarial attacks.