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

摘要/Abstract

摘要：

传统基于硬件的水声传感器网络(underwater acoustic sensor networks, UASNs)架构灵活性及可控性较差, 难以满足水下多样化监测任务的需求。针对此问题, 提出基于边缘计算的水下软件定义监测网络架构, 并提出一种多级协同水下监测机制。该架构通过主从控制器将集中式云处理任务部署至边缘端, 并对网络进行分级控制。该机制首先通过低轨遥感卫星进行大范围海洋监测, 其次进行小范围监测, 数据经原位处理及边缘处理后上传至水面进行分析, 最后进行水下联合监测。仿真结果表明, 所提出的监测架构和监测机制可降低异常数据处理时间成本和网络开销, 在不同场景下的端到端延迟、网络能耗和包投递率方面都有良好的表现, 提高了监测架构的灵活性。

关键词: 水声传感器网络, 监测架构, 软件定义, 水下监测机制

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

中图分类号:

TP393

金志刚, 洪叶, 苏毅珊, 羊秋玲. 软件定义云边协同架构下的水下监测新机制[J]. 系统工程与电子技术, 2024, 46(3): 1101-1108.

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.

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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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