面向海洋观监测传感网的计算卸载方法研究

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

通信学报 ›› 2021, Vol. 42 ›› Issue (5): 149-163.doi: 10.11959/j.issn.1000-436x.2021067

面向海洋观监测传感网的计算卸载方法研究

苏新¹, 薛淏阳¹, 周一青², 朱金秀¹

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

修回日期:2021-01-05 出版日期:2021-05-25 发布日期:2021-05-01
作者简介:苏新（1986- ），男，河北霸州人，博士，河海大学教授、硕士生导师，主要研究方向为通信与计算融合、移动通信技术、边缘/雾计算、智慧海洋等
薛淏阳（1996- ），男，山西临汾人，河海大学硕士生，主要研究方向为海洋网络、边缘/雾计算、计算卸载等
周一青（1975- ），女，浙江杭州人，中国科学院计算技术研究所研究员、博士生导师，主要研究方向为通信与计算融合、移动边缘计算、存储通信、干扰管控等
朱金秀（1972- ），女，江苏常州人，博士，河海大学副教授、硕士生导师，主要研究方向为神经网络、边缘/雾计算、数字通信等
基金资助:
国家重点研发计划基金资助项目(2021YFE0105500);国家自然科学基金资助项目(61801166)

Research on computing offloading method for maritime observation monitoring sensor network

Xin SU¹, Haoyang XUE¹, Yiqing ZHOU², Jinxiu ZHU¹

¹ College of IoT Engineering, Hohai University, Changzhou 213022, China
² Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Revised:2021-01-05 Online:2021-05-25 Published:2021-05-01
Supported by:
The National Key Research and Development Program of China(2021YFE0105500);The National Natural Science Foundation of China(61801166)

摘要/Abstract

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。

关键词: 海洋网络, 多接入边缘计算, 计算卸载, 遗传算法, 粒子群优化

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.

Key words: maritime network, multi-access edge computing, computing offloading, genetic algorithm, particle swarm optimization

中图分类号:

TN929.52

苏新, 薛淏阳, 周一青, 朱金秀. 面向海洋观监测传感网的计算卸载方法研究[J]. 通信学报, 2021, 42(5): 149-163.

Xin SU, Haoyang XUE, Yiqing ZHOU, Jinxiu ZHU. Research on computing offloading method for maritime observation monitoring sensor network[J]. Journal on Communications, 2021, 42(5): 149-163.

图/表 18

图1

图2

图3

图4

图5

图6

表1

图7

表2

图8

图9

图10

表3

远岸实验仿真参数"

参数描述	参数值
OUE计算能力 f_k /GHz	[1, 1.5]
中继节点计算能力 $f_{m_{k}} / GHz$	[2, 3.5]
海上基站计算能力 f_OBS /GHz	12
OUE计算功率 $P_{k}^{L} / W$	[0.8, 1]
OUE传输功率 $P_{k}^{tran} / W$	[0.3, 0.5]
OUE任务复杂度c_k /(cycle·bit^-1)	[800, 1 000]
船舶通信半径r /m	[100, 300]
基站通信半径R/m	1 200
船舶速度v /Knot	[20, 30]

表3

图11

图12

图13

图14

图15

参考文献 24

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GCL-PSO算法	远海可容错优化问题
粒子位置	卸载策略
适应度值	系统总成本
种群	不同卸载策略集合

参数描述	参数值
任务数量N	8
传输带宽W /MHz	1
OUE计算能力 f_oue/GHz	1
移动边缘节点计算能力/GHz	[1, 3]
固定边缘节点计算能力/GHz	[6, 7]
OUE计算功率P_loc /W	1
OUE传输功率P_tran/W	0.4
任务复杂度c_n/(cycle·bit^-1)	800
信噪比SNR /dB	[30, 50]
船舶通信半径r /m	[100, 300]
基站通信半径R/m	1 000
船舶速度v /Knot	[15, 20]

面向海洋观监测传感网的计算卸载方法研究

Research on computing offloading method for maritime observation monitoring sensor network

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