基于水下云边协同架构的珊瑚礁监测新机制

doi:10.12305/j.issn.1001-506X.2022.12.28

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (12): 3829-3836.doi: 10.12305/j.issn.1001-506X.2022.12.28

基于水下云边协同架构的珊瑚礁监测新机制

金志刚¹, 段晨旭¹, 羊秋玲², 苏毅珊^1,*

1. 天津大学电气自动化与信息工程学院, 天津 300072
2. 海南大学计算机科学与网络空间安全学院, 海南海口 570228

收稿日期:2021-09-07 出版日期:2022-11-14 发布日期:2022-11-24
通讯作者: 苏毅珊
作者简介:金志刚(1972—), 男, 教授, 博士, 主要研究方向为水下网络、传感器网络、网络安全、社交网络、大数据|段晨旭(1997—), 女, 硕士研究生, 主要研究方向为水下传感器网络、水下路由协议|羊秋玲(1981—), 女, 教授, 博士, 主要研究方向为水下传感器网络、网络空间安全、网络信息安全|苏毅珊(1985—), 男, 副教授, 博士, 主要研究方向为水下传感器网络、网络信息安全
基金资助:
国家自然科学基金(52171337);国家自然科学基金(61862020);海南省重点研发项目(ZDYF2020199)

A new mechanism for reef coral monitoring based on underwater cloud-edge collaborative architecture

Zhigang JIN¹, Chenxu DUAN¹, Qiuling YANG², Yishan SU^1,*

1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
2. School of Computer Science and Cyberspace Security, Hainan University, Haikou 570228, China

Received:2021-09-07 Online:2022-11-14 Published:2022-11-24
Contact: Yishan SU

摘要/Abstract

摘要：

珊瑚礁是极具研究价值的海洋生态系统, 水声传感器网络(underwater acoustic sensor networks, UASNs)是监测与保护珊瑚礁系统的有效手段。然而, 随着水下传感设备的大规模应用, 感知数据的类型及数量大幅增加, 传统UASNs架构将原始数据直接上传至水面数据中心的集中处理方式给网络能耗和通信效率带来严峻挑战。本文构建了一种基于边缘计算的水下端边云系统架构, 并提出一种适用于该架构的两级协同珊瑚礁系统监测机制。该架构将复杂处理任务从远程云中心分散至边缘端, 减轻了云端处理负荷。该机制由两级监测环节组成, 同时包含了端侧图像处理和端边协同数据检测策略, 实现了机器学习任务的边缘侧执行和数据的原位处理。实验结果表明, 本文研究能够明显减少网络数据流量, 有效降低网络能耗及传输时延, 显著延长网络生命周期。

关键词: 水声传感器网络, 边缘计算架构, 云边协同, 珊瑚礁监测

Abstract:

Coral reefs are marine ecosystems of great research value, and underwater acoustic sensor networks (UASNs) are effective means to monitor and protect this system. However, with the extensive application of underwater sensing equipment, the types and quantity of sensing data have increased greatly. The traditional UASNs architecture uploads the raw data directly to the surface data center, which brings severe challenges to the network energy and communication efficiency. This paper constructs an underwater end-edge-cloud system architecture based on edge computing, and proposes a two-level collaborative coral reef system monitoring mechanism. This architecture sinks complex processing tasks from the remote cloud center to the edge, and reduces the cloud processing load. The mechanism includes end-side image processing and end-edge collaborative data detection strategies, which realizes the edge-side execution of machine learning tasks and the in-situ processing of data. Experimental results show that this study can significantly reduce network data traffic, effectively decrease energy consumption and transmission delay, and extend network life cycle.

Key words: underwater acoustic sensor networks (UASNs), edge computing architecture, cloud-edge collaborative, coral reef monitoring

中图分类号:

TP393

金志刚, 段晨旭, 羊秋玲, 苏毅珊. 基于水下云边协同架构的珊瑚礁监测新机制[J]. 系统工程与电子技术, 2022, 44(12): 3829-3836.

Zhigang JIN, Chenxu DUAN, Qiuling YANG, Yishan SU. A new mechanism for reef coral monitoring based on underwater cloud-edge collaborative architecture[J]. Systems Engineering and Electronics, 2022, 44(12): 3829-3836.

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参考文献 22

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分类	警报标准	警报等级	白化影响
一级大范围持续监测(HotSpot＜1)	HotSpot≤0	无风险	无影响
一级大范围持续监测(HotSpot＜1)	0＜HotSpot＜1	持续监测	无影响
二级小范围本地监测(HotSpot≥1)	HotSpot≥1且0＜DHW＜4	白化预警	可能白化
	HotSpot≥1且4≤DHW＜8	预警级别Ⅰ	极可能白化
	HotSpot≥1且8≤DHW	预警级别Ⅱ	严重白化可能甚至死亡

参数	取值
终端感知节点数量/个	45
边缘处理节点数量/个	15
中继传输节点数量/个	60
中继节点感知半径/m	1 000
初始能量/J	10 000
发送功率/W	10
接收功率/W	1
数据包大小/byte	100
异常数据率(α=异常数据量/总数据量)	0~1
数据包生成速率λ/(packets/s)	0.01~0.1
实验运行次数/次	30

处理方式	平均处理时间/s
云端集中处理	5.058
端边协同处理	1.324

[1]	金志刚, 冀智华, 苏毅珊. 基于深度可调节节点的水声网络部署优化算法[J]. 系统工程与电子技术, 2019, 41(1): 203-207.
[2]	赵旦峰，梁明珅，段晋珏. 水声网络中喷泉码的应用研究现状与发展前景[J]. 系统工程与电子技术, 2014, 36(9): 1838-1843.

基于水下云边协同架构的珊瑚礁监测新机制

A new mechanism for reef coral monitoring based on underwater cloud-edge collaborative architecture

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