Open Access

ARTICLE

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

Xiaocong Wang¹, Jiajian Li¹, Peng Zhao¹, Hui Lian², Yanjun Shi^1,*

1 School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China
2 TBEA Xinjiang Cable Research Institute, TBEA Xinjiang Cable Co., Ltd., Xinjiang, 831100, China

* Corresponding Author: Yanjun Shi. Email:

Computers, Materials & Continua 2026, 87(2), 53 https://doi.org/10.32604/cmc.2026.075202

Received 27 October 2025; Accepted 31 December 2025; Issue published 12 March 2026

Abstract

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.

---

Graphic Abstract

---

Keywords

---