Sea-Air-Ground Collaborative Edge Computing Architecture and Topology Control for Marine Monitoring

JIN Zhigang; YIN Huan; HONG Ye; SU Yishan

doi:10.7652/xjtuxb202306006

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Sea-Air-Ground Collaborative Edge Computing Architecture and Topology Control for Marine Monitoring

更新时间：2025-05-08

- Sea-Air-Ground Collaborative Edge Computing Architecture and Topology Control for Marine Monitoring
- Vol. 57, Issue 6, Pages: 47-55(2023)
- 作者机构：
  
  1. 天津大学电气自动化与信息工程学院,天津,300072
  2. 天津大学国际工程师学院,天津,300072
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202306006
  CLC： TP393
- Online First：10 June 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
JIN Zhigang, YIN Huan, HONG Ye, et al. Sea-Air-Ground Collaborative Edge Computing Architecture and Topology Control for Marine Monitoring[J]. 2023, 57(6): 47-55.
DOI：

JIN Zhigang, YIN Huan, HONG Ye, et al. Sea-Air-Ground Collaborative Edge Computing Architecture and Topology Control for Marine Monitoring[J]. 2023, 57(6): 47-55. DOI： 10.7652/xjtuxb202306006.

摘要

针对现有海洋监测网受限于光缆技术可达深度及传统集中式处理的网络架构存在中心节点压力大的问题

提出了一种面向海洋监测的海空地协同边缘计算架构。在该架构中

水声传感器网络是海洋数据监测平台

协同边缘计算架构将复杂处理任务从远程云中心分散至边缘端

将水下异常数据传达到边缘端进行预处理后

再上传到云数据中心

必要时发出预警。针对水下传感器容易出现能量不足、链路中断的问题

提出了水下滑翔机辅助拓扑控制算法。该算法中终端节点和边缘节点呈3:1比例部署

通过节点的链路失效模型判断失效概率

当超出失效概率阈值时

便通知水下滑翔机利用改进的粒子群优化算法执行拓扑修复

并以覆盖率和连通性最大为优化目标求解最优修复位置。仿真结果表明:所提新架构端到端时延约为传统架构的1/3

能量效率得到明显提升; 与传统深度调节方法对比

所提拓扑控制算法覆盖率与连通度提升至87.9%、9.3。

Abstract

Aiming at the problem that the existing marine monitoring network is limited by the reachable depth of optical cable technology

and the heavy pressure of central node caused by traditional centralized processing network architecture

a sea-air-ground collaborative edge computing architecture for marine monitoring was proposed. In this architecture

the underwater acoustic sensor network is a platform for marine data monitoring

and the collaborative edge computing architecture disperses complex processing tasks from the remote cloud center to the edge

and transmits the underwater abnormal data to the edge for preprocessing before uploading it to the cloud data center

giving an early warning if necessary. In addition

in view of the problems that underwater sensors are prone to energy shortage and link interruption

an autonomous underwater glider-assisted topology control algorithm was proposed. In this algorithm

terminal nodes and edge nodes are deployed at a ratio of 3:1

and the failure probability is judged through the node's link failure model. When the failure probability threshold is exceeded

the underwater glider is notified to use the improved particle swarm optimization algorithm to perform topology repair

and the optimal repair location is solved with the optimization goal of maximum coverage and connectivity. The simulation results showed that the end-to-end delay of the proposed new architecture is about 1/3 of the traditional architecture

and the energy efficiency is significantly improved; compared with the traditional deep adjustment method

the coverage and connectivity of the proposed topology control algorithm are increased to 87.9% and 9.3.

关键词

Keywords

references

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