Mobility-Aware Cooperative Optimization for Task Offloading and Resource Allocation in Multi-Edge Computing

The rapid proliferation of mobile Internet of Things (IoT) devices has introduced significant resource scheduling challenges in multi-edge computing networks, where device mobility leads to dynamic network connectivity and load imbalance, complicating task offloading and resource management. To address these issues, this paper presents a mobility-driven hierarchical optimization framework for task offloading and computation resource allocation in multi-region edge computing environments, a functionally coupled hierarchical framework that integrates mobility-aware heuristic offloading with multi-agent deep deterministic policy gradient (MADDPG)-based resource allocation. Devices are first clustered according to their mobility patterns, and offloading decisions are dynamically made based on trajectory and dwell-time characteristics. Each edge server is modeled as an autonomous agent, and an MADDPG framework is adopted to collaboratively optimize resource allocation, with the joint objective of minimizing task processing delay and system energy consumption. Experimental evaluations under diverse mobility and workload conditions show that the proposed approach achieves a 19.0% reduction in task delay compared to the Multi-Objective Gray Wolf Optimization (MOGWO) method at the largest device scale (60 devices) and maintains comparable energy efficiency. Furthermore, it exhibits stronger adaptability and scheduling performance across varying mobility group distributions. These results confirm the effectiveness of the proposed method in enhancing system performance within dynamic mobile edge computing scenarios.