Multi-UAV-Assisted MEC Offloading-Optimization Method on Deep Reinforcement Learning

• Published in: International journal on semantic web and information systems Vol. 21; no. 1; pp. 1 - 31
• Main Authors: Li, Zhihua, Sun, Chao
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2025
• Subjects: Algorithms; Collaboration; Communication; Computation offloading; Continuity (mathematics); Decision making; Deep learning; Edge computing; Energy consumption; Energy efficiency; Heuristic; Information systems; Mobile computing; Optimization algorithms; Optimization models; Pareto optimum; Resource allocation; Scheduling; Semantic web; Semantics; Unmanned aerial vehicles; Utility functions
• ISSN: 1552-6283; 1552-6291
• DOI: 10.4018/IJSWIS.368839

In multi-UAV-assisted mobile edge computing (MEC), insufficient consideration of collaborative computation in inter-UAV communication can significantly reduce computational service capabilities. For this problem, we present a multi-UAV-assisted MEC offloading optimization model that jointly optimizes task offloading decision, UAV resource allocation, UAV trajectories and establish collaborative computation through inter-UAV communication. First, to solve the multi-UAV-assisted MEC offloading optimization issue, we define a weighted utility function that balances delay and energy consumption. Then, to tackle the continuous nature of the computation-offloading problem and the coexistence of discrete and continuous variables, the PPO algorithm is enhanced by integrating an average reward objective function and a hybrid action generation offloading mechanism. Finally, we propose a multi-UAV-assisted MEC computing offloading optimization method to improve the utility function. Experiments show that the proposed method significantly enhances system utility.