软件定义云边协同架构下的水下监测新机制

doi:10.12305/j.issn.1001-506X.2024.03.37

Abstract

Abstract:

Traditional underwater acoustic sensor networks (UASNs) monitoring architecture based hardware lacks flexibility and controllability to meet the needs of diverse underwater monitoring tasks. An underwater software-defined monitoring network architecture based on edge computing and a multi-level collaborative underwater monitoring mechanism are proposed. The architecture deploys centralized cloud processing tasks to the edge through a master-slave controller, and provides hierarchical control of the network. The mechanism firstly conducts large-scale ocean monitoring through low-orbit remote sensing satellites, followed by small-scale monitoring, with data uploaded to the surface for analysis after in-situ processing and edge processing, and finally joint underwater monitoring. Simulation results show that the proposed monitoring architecture and monitoring mechanism can reduce the abnormal data processing time cost and network overhead, perform well in terms of end-to-end delay, network energy consumption and packet delivery rate in different scenarios, and improve the flexibility of the monitoring architecture.

Key words: underwater acoustic sensor networks (UASNs), monitoring architecture, software defined, underwater monitoring mechanism

CLC Number:

TP393

Zhigang JIN, Ye HONG, Yishan SU, Qiuling YANG. New mechanism for underwater monitoring in a software-defined cloud-edge collaborative architecture[J]. Systems Engineering and Electronics, 2024, 46(3): 1101-1108.

Figures/Tables 13

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References 30

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名称	组成	功能
应用层	地面数据中心、云数据中心及低轨遥感卫星	深度处理、存储、组织和共享从较低层聚合数据的最终任务, 定制各种应用程序、制定管理策略并向边缘控制层下发任务
边缘控制层	sink控制节点(主要控制器)、边缘控制节点(次要控制器)	sink控制节点负责控制与维护UASNs的全局视图, 并将执行策略同步至边缘控制节点。边缘控制节点对其控制域内的节点进行流量控制、拓扑感知、通过南向接口收集各自控制域内节点的状态信息并搭建起局部控制网络, 并将执行策略以流表的形式部署在底层的转发节点上, 且可让感知数据按照流表执行相应的动作
智能感知层	智能水下传感器、移动AUV	支持Open Flow流表, 可由网络控制器通过南向接口配置。配备了先进的传感设备(如盐度和温度监测传感器、高清摄像头), 可进行信息感知、部署区域数据的收集及原位处理。其中智能水下传感器被放置在指定的水下区域, 一旦部署了节点, 便自动形成集群并划分为不同的控制域

参数	取值
节点/个	300
水下节点感知半径/m	1 500
水声传播速度/(m/s)	1 500
初始能量/J	10 000
发送功率/W	10
接收功率/W	1
数据包大小/bytes	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

处理方式	平均处理时间
传统数据集中处理	5.576
新型数据分散处理	2.375

数据包生成速率/(packet/s)	协议	架构	端到端时延/s	包投递率	总能耗/J
0.02	HHVBF	传统方法	88	0.712 4	5 930
	HHVBF	本文方法	70	0.802 5	4 457
	GEDAR	传统方法	66	0.867 2	4 290
	GEDAR	本文方法	58	0.956 9	3 456
	QELAR	传统方法	63	0.843 0	3 729
	QELAR	本文方法	48	0.916 7	2 836
0.06	HHVBF	传统方法	101	0.653 7	5 238
	HHVBF	本文方法	83	0.763 7	4 028
	GEDAR	传统方法	81	0.833 5	3 990
	GEDAR	本文方法	60	0.926 7	2 907
	QELAR	传统方法	68	0.812 1	3 316
	QELAR	本文方法	53	0.893 7	2 674
0.1	HHVBF	传统方法	116	0.596 2	4 535
	HHVBF	本文方法	95	0.732 3	3 675
	GEDAR	传统方法	86	0.809 6	3 685
	GEDAR	本文方法	71	0.893 6	2 590
	QELAR	传统方法	76	0.793 6	2 993
	QELAR	本文方法	58	0.865 4	2 323

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New mechanism for underwater monitoring in a software-defined cloud-edge collaborative architecture

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