User Mobility-aware Joint Optimization of Dependent Task Offloading and 3D Trajectory Planning for AAN-assisted Maritime Mobile Edge Computing

CHEN Nuo; ZHANG Xinyan; HAN Lei; LU Xiaochun; SU Xin; GONG Zi Yang

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User Mobility-aware Joint Optimization of Dependent Task Offloading and 3D Trajectory Planning for AAN-assisted Maritime Mobile Edge Computing

更新时间：2026-03-03

- User Mobility-aware Joint Optimization of Dependent Task Offloading and 3D Trajectory Planning for AAN-assisted Maritime Mobile Edge Computing
- Chinese Journal on Internet of Things (2026)
- 作者机构：
  
  1.河海大学信息科学与工程学院，江苏常州 213022
  2.河海大学人工智能与自动化学院，江苏常州 213022
  3.嘉泉大学IT融合工程系，京畿道城南市 13202
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN929.52
- Received：17 June 2025，
  
  Revised：2025-07-13，
  
  Accepted：07 August 2025，
- 稿件说明：
移动端阅览
CHEN Nuo, ZHANG Xinyan, HAN Lei, et al. User Mobility-aware Joint Optimization of Dependent Task Offloading and 3D Trajectory Planning for AAN-assisted Maritime Mobile Edge Computing[J/OL]. Chinese Journal on Internet of Things, 2026.
DOI：

CHEN Nuo, ZHANG Xinyan, HAN Lei, et al. User Mobility-aware Joint Optimization of Dependent Task Offloading and 3D Trajectory Planning for AAN-assisted Maritime Mobile Edge Computing[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

针对海上移动边缘网络中用户移动性强、任务依赖关系复杂及空中辅助节点（AAN）轨迹规划受限等问题，本文提出一种用户移动感知的空海协同任务卸载方法。通过构建由用户层、AAN层和边缘层组成的空海协同计算架构，建立以最小化系统平均成本（时延与能耗）为目标的优化模型，综合考虑任务间依赖关系、资源分配约束及AAN三维空间位移安全限制。创新性地提出基于K-means的动态用户集群划分机制，周期性地根据移动设备（MD）位置更新集群归属关系，确保AAN高效跟踪动态用户；设计异构多智能体深度强化学习框架（TD3-HAO算法），实现依赖性子任务卸载决策、计算资源分配与AAN三维飞行轨迹的联合优化。仿真结果表明，相较于LOCAL、DDPG等基准算法，所提方案在MD数量增至25时仍能维持平均系统成本较最优解偏差小于3%，时延降低16.94%-38.34%，有效解决传统方法中因忽略任务依赖性和节点同质性导致的资源利用率低下问题，为空海协同边缘计算提供理论支撑。

Abstract

To address the challenges of user mobility

task dependency

and restricted aerial trajectory planning in maritime mobile edge computing

this study proposes a user mobility-aware air-sea collaborative task offloading framework. We establish a three-tier computing architecture comprising user equipment

aerial auxiliary nodes (AANs)

and edge servers

formulating a multi-objective optimization model that minimizes system costs (latency and energy consumption) while considering task dependencies

resource allocation constraints

and AAN 3D spatial safety requirements. The technical contributions include: 1) A dynamic K-means-based user clustering mechanism that periodically updates MD-AAN associations according to real-time mobility patterns; 2) A Twin-delayed Deep Deterministic Policy Gradient based Heterogeneous Agent Offloading (TD3-HAO) algorithm enabling joint optimization of dependent sub-task offloading

resource allocation

and 3D AAN trajectory planning through heterogeneous multi-agent coordination. Simulation results demonstrate superior performance over baseline methods

maintaining less than 3% deviation from optimal solutions when scaling to 25 MDs

with 16.94%-38.34% latency reduction. The proposed solution effectively resolves the resource underutilization caused by ignoring task dependencies and agent homogeneity in existing approaches

providing theoretical guidance for air-sea integrated edge computing systems.

关键词

Keywords

references

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