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一种基于边缘编排的分布式嵌入式系统的服务发现解决方案。

A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems.

机构信息

Institute of Information and Communication Technologies, ITACA, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

School of Informatics, ETSINF, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

出版信息

Sensors (Basel). 2021 Jan 19;21(2):672. doi: 10.3390/s21020672.

DOI:10.3390/s21020672
PMID:33478175
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7835934/
Abstract

This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The solution adds to the choreography middleware three messages passing models to be coherent and compatible with current IoT messaging protocols. It is aimed to support blind hot plugging of new machines and help with service load balance. The discovery mechanism is implemented as a broker service and supports regular expressions (Regex) in message scope to discern both publishing patterns offered by data providers and client services necessities. Results compare Control Process Unit (CPU) usage in a request-response and datacentric configuration and analyze both regex interpreter latency times compared with a traditional message structure as well as its impact on CPU and memory consumption.

摘要

本文提出了一种支持基于边缘编排的分布式嵌入式系统服务发现的解决方案。物联网（IoT）边缘架构层由 Raspberry Pi 机器组成。每台机器都根据编排协作范例托管不同的服务。该解决方案为编排中间件添加了三种消息传递模型，以与当前的 IoT 消息传递协议保持一致和兼容。其目的是支持新机器的盲插，并帮助实现服务负载均衡。发现机制实现为代理服务，并在消息范围内支持正则表达式（Regex），以区分数据提供方提供的发布模式和客户端服务需求。结果比较了请求-响应和以数据为中心的配置中的控制处理单元（CPU）使用情况，并分析了正则表达式解释器的延迟时间与传统消息结构的比较，以及对 CPU 和内存消耗的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/2e886b6e7ccb/sensors-21-00672-g011a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/2628e5102d93/sensors-21-00672-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/c7cd78e37f69/sensors-21-00672-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/7736530bc172/sensors-21-00672-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/4d104c2b7722/sensors-21-00672-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/87b096f3e3c1/sensors-21-00672-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/f3171d32b957/sensors-21-00672-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/ee030e8d0f45/sensors-21-00672-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/f9e391d8075f/sensors-21-00672-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/622366211b4c/sensors-21-00672-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/8f94c7f9391d/sensors-21-00672-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/2e886b6e7ccb/sensors-21-00672-g011a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/2628e5102d93/sensors-21-00672-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/c7cd78e37f69/sensors-21-00672-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/7736530bc172/sensors-21-00672-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/4d104c2b7722/sensors-21-00672-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/87b096f3e3c1/sensors-21-00672-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/f3171d32b957/sensors-21-00672-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/ee030e8d0f45/sensors-21-00672-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/f9e391d8075f/sensors-21-00672-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/622366211b4c/sensors-21-00672-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/8f94c7f9391d/sensors-21-00672-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bade/7835934/2e886b6e7ccb/sensors-21-00672-g011a.jpg

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本文引用的文献

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IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture.基于物联网的智能灌溉系统：精准农业中传感器和物联网系统在灌溉方面的最新趋势综述。
Sensors (Basel). 2020 Feb 14;20(4):1042. doi: 10.3390/s20041042.
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Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care.
可穿戴传感器与物联网集成，推动电子医疗保健发展。
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Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes.基于流程编排的分布式服务和混合模型的集成，用于预测和检测 2 型糖尿病。
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A Study of LoRa: Long Range & Low Power Networks for the Internet of Things.LoRa研究：用于物联网的远距离低功耗网络
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