Research on Task Offloading Optimization Scheme Based on MADRL for Maritime IoT

Zhang Zonghui; Zhu Sifeng

doi:10.11959/j.issn.2096-3750.WLW25086

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Research on Task Offloading Optimization Scheme Based on MADRL for Maritime IoT

更新时间：2026-06-16

- Research on Task Offloading Optimization Scheme Based on MADRL for Maritime IoT
- Chinese Journal on Internet of Things (2026)
- 作者机构：
  
  天津城建大学，天津 300384
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62172457);Key projects of Tianjin Natural Science Foundation(22JCZDJC00600);Henan Science and Technology Innovation Talent Project(23HASTIT029);Key Science and Technology Program of Henan Province(242102210027)
- DOI：10.11959/j.issn.2096-3750.WLW25086
  CLC： TP393.1;TN929.5
- Received：09 May 2025，
  
  Revised：2025-06-07，
  
  Accepted：16 June 2025，
- 稿件说明：
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Zhang Zonghui, Zhu Sifeng. Research on Task Offloading Optimization Scheme Based on MADRL for Maritime IoT[J/OL]. Chinese Journal on Internet of Things, 2026.
DOI：

Zhang Zonghui, Zhu Sifeng. Research on Task Offloading Optimization Scheme Based on MADRL for Maritime IoT[J/OL]. Chinese Journal on Internet of Things, 2026. DOI： 10.11959/j.issn.2096-3750.WLW25086.

摘要

为解决海上网络覆盖范围小、计算能力弱的问题，引入了空天地海一体化网络场景下的任务卸载决策。考虑计算任务的卸载成功率、能耗约束、近海场景与远海场景的环境差异以及空天地海一体化场景的动态性，构建了一种适用于空天地海一体化网络的任务卸载架构，提出了一种基于深度强化学习的多智能体协同任务卸载方案。实验结果表明，相较于基于MADQN算法的卸载方案、基于DDPG算法的卸载方案和随机策略的卸载方案，所提方案在卸载成功率方面分别提高5.08%、21.71%和60.48%，在时延方面分别降低11.65%、18.64%和64.60%，在能耗方面分别降低11.57%、9.66%和10.38%。

Abstract

To address the issues of limited network coverage and weak computing capabilities in maritime environments

a task offloading scheme in the space-air-ground-sea integrated network scenario was introduced. Considering factors such as the offloading success rate of computational tasks

energy consumption constraints

environmental differences between near-shore and offshore scenarios

and the dynamic nature of the integrated space-air-ground-sea environment

a task offloading framework suitable for space-air-ground-sea integrated networks was constructed. A multi-agent collaborative task offloading scheme based on deep reinforcement learning was proposed. Experimental results demonstrate that

compared to offloading schemes based on the MADQN algorithm

the DDPG algorithm

and random policy

the proposed scheme improves the offloading success rate by 3.09%

18.42%

and 66.42%

respectively

reduces delay by 19.07%

21.53%

and 65.02%

respectively

and lowers energy consumption by 10.59%

8.20%

and 8.75%

respectively.

关键词

Keywords

references

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