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Research on Computation Offloading and Resource Allocation Strategy Based on MADDPG for Integrated Space–Air–Marine Network

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Publicado en:Entropy vol. 27, no. 8 (2025), p. 803-824
Autor principal: Gao Haixiang
Publicado:
MDPI AG
Materias:
Deep learning
Intelligent agents
Internet of Things
Satellite communications
Combinatorial analysis
Power control
Markov processes
Optimization
Edge computing
Resource allocation
Mobile computing
Adaptation
Decomposition
Distance learning
Energy consumption
Low earth orbits
Scheduling
Energy costs
Convexity
Unmanned aerial vehicles
Cloud computing
Decision making
Network latency
Computation offloading
Convex analysis
Algorithms
Energy efficiency
Satellites
Multiagent systems
Cost control
Resource management
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Resumen:This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically.
ISSN:1099-4300
DOI:10.3390/e27080803
Fuente:Engineering Database