Research on Computation Offloading and Resource Allocation Strategy Based on MADDPG for Integrated Space–Air–Marine Network
Wedi'i Gadw mewn:
| Cyhoeddwyd yn: | Entropy vol. 27, no. 8 (2025), p. 803-824 |
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| Prif Awdur: | |
| Cyhoeddwyd: |
MDPI AG
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| Pynciau: | |
| Mynediad Ar-lein: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tagiau: |
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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MARC
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| 022 | |a 1099-4300 | ||
| 024 | 7 | |a 10.3390/e27080803 |2 doi | |
| 035 | |a 3244012956 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231460 |2 nlm | ||
| 100 | 1 | |a Gao Haixiang | |
| 245 | 1 | |a Research on Computation Offloading and Resource Allocation Strategy Based on MADDPG for Integrated Space–Air–Marine Network | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Deep learning | ||
| 653 | |a Intelligent agents | ||
| 653 | |a Internet of Things | ||
| 653 | |a Satellite communications | ||
| 653 | |a Combinatorial analysis | ||
| 653 | |a Power control | ||
| 653 | |a Markov processes | ||
| 653 | |a Optimization | ||
| 653 | |a Edge computing | ||
| 653 | |a Resource allocation | ||
| 653 | |a Mobile computing | ||
| 653 | |a Adaptation | ||
| 653 | |a Decomposition | ||
| 653 | |a Distance learning | ||
| 653 | |a Energy consumption | ||
| 653 | |a Low earth orbits | ||
| 653 | |a Scheduling | ||
| 653 | |a Energy costs | ||
| 653 | |a Convexity | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Cloud computing | ||
| 653 | |a Decision making | ||
| 653 | |a Network latency | ||
| 653 | |a Computation offloading | ||
| 653 | |a Convex analysis | ||
| 653 | |a Algorithms | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Satellites | ||
| 653 | |a Multiagent systems | ||
| 653 | |a Cost control | ||
| 653 | |a Resource management | ||
| 773 | 0 | |t Entropy |g vol. 27, no. 8 (2025), p. 803-824 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244012956/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244012956/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244012956/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |