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

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Publicado en:International Journal on Semantic Web and Information Systems vol. 21, no. 1 (2025), p. 1-32
Autor principal: Li, Zhihua
Otros Autores: Sun, Chao
Publicado:
IGI Global
Materias:
Collaboration
Deep learning
Communication
Resource allocation
Edge computing
Mobile computing
Unmanned aerial vehicles
Semantic web
Heuristic
Pareto optimum
Energy consumption
Optimization models
Scheduling
Continuity (mathematics)
Decision making
Computation offloading
Algorithms
Information systems
Energy efficiency
Utility functions
Optimization algorithms
Semantics
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1552-6283
1552-6291
DOI:10.4018/IJSWIS.368839
Fuente:Engineering Database