Fairness-Aware Task Offloading Based on Location Prediction in Collaborative Edge Networks

With the widespread deployment of assembly robots in smart manufacturing, efficiently offloading tasks and allocating resources in highly dynamic industrial environments has become a critical challenge for Mobile Edge Computing (MEC). To address this challenge, this paper constructs a cloud-edge-end collaborative MEC system that enables assembly robots to offload complex workflow tasks via multiple paths (horizontal, vertical, and hybrid collaboration). To mitigate uncertainties arising from mobility, the location prediction module is employed. This enables proactive channel-quality estimation, providing forward-looking insights for offloading decisions. Furthermore, we propose a fairness-aware joint optimization framework. Utilizing an improved Multi-Agent Deep Reinforcement Learning (MADRL) algorithm whose reward function incorporates total system cost, positional reliability, and timeout penalties, the framework aims to balance resource distribution among assembly robots while maximizing system utility. Simulation results demonstrate that the proposed framework outperforms traditional offloading strategies. By integrating predictive mobility management with fairness-aware optimization, the framework offers a robust solution for dynamic industrial MEC environments.