Research on computing offloading method for maritime observation monitoring sensor network

Xin SU; Haoyang XUE; Yiqing ZHOU; Jinxiu ZHU

doi:10.11959/j.issn.1000-436x.2021067

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Research on computing offloading method for maritime observation monitoring sensor network

Papers | 更新时间：2024-06-06

- Research on computing offloading method for maritime observation monitoring sensor network
- Journal on Communications Vol. 42, Issue 5, Pages: 149-163(2021)
- 作者机构：
  
  1. 河海大学物联网工程学院，江苏常州 231022
  2. 中国科学院计算技术研究所，北京100190
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2021YFE0105500);The National Natural Science Foundation of China(61801166)
- DOI：10.11959/j.issn.1000-436x.2021067
  CLC： TN929.52
- Online First：2021-05，
  
  Published：25 May 2021
- 稿件说明：
移动端阅览
Xin SU, Haoyang XUE, Yiqing ZHOU, et al. Research on computing offloading method for maritime observation monitoring sensor network[J]. Journal on Communications, 2021, 42(5): 149-163.
DOI：

Xin SU, Haoyang XUE, Yiqing ZHOU, et al. Research on computing offloading method for maritime observation monitoring sensor network[J]. Journal on Communications, 2021, 42(5): 149-163. DOI： 10.11959/j.issn.1000-436x.2021067.

摘要

针对海洋网络节点间计算能力与通信资源的差异性，提出了一种基于海洋网络连通概率的边缘计算节点选取方法。根据海洋近岸与远岸的网络节点密度不同，分别建立2种卸载模型。在近岸场景下，提出多节点协同的卸载方法，利用基于海洋多节点协同卸载遗传算法求解；在远岸场景下，提出可容错的卸载方法，利用基于分组交叉学习粒子群算法求解。仿真结果表明，所提方法较传统方法可降低30%以上的网络延时并可节约20%以上的网络成本，极大提高了各类海事应用的用户体验。

Abstract

Considering the differences in computing capacity and communication resources of the maritime network nodes

a maritime network connectivity probability based method was proposed for selecting edge computing service nodes.Because of the different node densities in the near-shore and far-shore scenarios

two offloading models were established accordingly.In the near-shore scenario

a multi-node cooperative offloading method was proposed by using the genetic algorithm based on maritime multi-node cooperative offloading.In the far-shore scenario

a fault-tolerant offloading method was proposed based on the particle swarm algorithm with grouping cross learning.Simulation results show that compared with conventional methods

the proposed methods save over 30% network delay and reduces about 20% network costs

which can greatly enhance the maritime user experiences.

关键词

Keywords

references

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