面向工业应用的基于 SDN 的可扩展实时 MQTT 框架

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of the Industrial Electronics Society Pub Date : 2024-03-05 DOI:10.1109/OJIES.2024.3373232
E. Shahri;P. Pedreiras;L. Almeida
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引用次数: 0

摘要

在工业 4.0 背景下,智能生产和工业物联网(IIoT)等概念日益突出,为工业系统工程提出了一系列新要求,包括支持动态环境、保证及时性、支持异构性、互操作性和可靠性。由于涉及的设备数量和种类显著增加,这些要求在网络层面进一步加剧。要在瞬息万变的工业环境中保持竞争力,同时提高生产率,就必须满足这些要求,并将既定协议与新兴技术相结合。软件定义网络(SDN)是最前沿的流量管理模式,可为复杂的工业网络提供灵活性,实现高效的资源分配和动态重新配置。消息队列遥测传输(MQTT)是应用层的低开销协议,在物联网和 IIoT 领域越来越受欢迎。然而,其服务质量(QoS)策略并不支持及时性要求。本文提出了一种无缝集成 SDN 和 MQTT 的框架,在提高网络管理灵活性的同时满足工业环境中的实时性要求。它利用 MQTTv5 的用户属性来指定实时性要求。网络管理器会拦截 MQTT 流量,提取实时信息,并指示 SDN 控制器部署相应的网络预留。多个边缘网络的 MQTT 流量由选定的经纪人使用组播进行传播。广泛的实验验证了所提出的方法,证明它在减少延迟方面优于 MQTT 和直接组播-MQTT(DM-MQTT)DM-MQTT。通过实验验证的响应时间分析强调了该方法在各项指标上的稳健性能。
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A Scalable Real-Time SDN-Based MQTT Framework for Industrial Applications
The increasing prominence of concepts such as Smart Production and Industrial Internet of Things (IIoT) within the context of Industry 4.0 has introduced a new set of requirements for the engineering of industrial systems, including support for dynamic environments, timeliness guarantees, support for heterogeneity, interoperability and reliability. These requirements are further exacerbated at the network level by the notable rise in the number and variety of devices involved. To stay competitive in this ever-changing industrial landscape while boosting productivity, it is vital to meet those requirements, combining established protocols with emerging technologies. Software-Defined Networking (SDN) is the forefront traffic management paradigm that offers flexibility for complex industrial networks, enabling efficient resource allocation and dynamic reconfiguration. Message Queuing Telemetry Transport (MQTT) is a low-overhead protocol of the application layer that is gaining popularity in the scope of the IoT and IIoT. However, its Quality-of-Service (QoS) policies do not support timeliness requirements. This article presents a framework that seamlessly integrates SDN and MQTT, enhancing network management flexibility while satisfying real-time requirements found in industrial environments. It leverages the User Properties of MQTTv5 to allow specifying real-time requirements. MQTT traffic is intercepted by a Network Manager that extracts real-time information and instructs an SDN controller to deploy corresponding network reservations. MQTT traffic across multiple edge networks is propagated by selected brokers using multicasting. Extensive experiments validate the proposed approach, demonstrating its superiority over MQTT and Direct Multicast-MQTT (DM-MQTT) DM-MQTT in latency reduction. A response time analysis, validated experimentally, emphasizes robust performance across metrics.
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来源期刊
IEEE Open Journal of the Industrial Electronics Society
IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
10.80
自引率
2.40%
发文量
33
审稿时长
12 weeks
期刊介绍: The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.
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