Industrial Communication based on Modbus and Node-RED

Tabaa, Mohamed; Chouri, Brahim; Saadaoui, Safa; Alami, Karim

doi:10.1016/j.procs.2018.04.107

Cited by 47 publications

(22 citation statements)

References 7 publications

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“…Dynamic dashboard applications allow stakeholders, e.g., managers, system experts, customers, to build their own dashboards that visualize KPIs about the systems of their choice by selecting sensors and visualizations at will. Many off-the-shelf dynamic dashboards, such as Microsoft PowerBI [22], Tableau [23] and Google Data Studio [24], are either well integrated with classic data warehouses but do not integrate well with the vast amount of streaming sensor data and the streaming platform technology they require; or they require a lot of programming effort or manual configuration when combining the output of multiple sensors, e.g., Node-RED [25]. This manual configuration is required, on the one hand, at design time to instruct how to fetch data and bind it correctly to the available data processing and visualization components, and at runtime, on the other hand, there is still the burden for the user to select from a plethora of available sensors, data processing components and visualizations.…”

Section: Related Workmentioning

confidence: 99%

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

Moens

Bracke

Soete

et al. 2020

Sensors

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The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.

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Section: Related Workmentioning

confidence: 99%

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

Moens

Bracke

Soete

et al. 2020

Sensors

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“…IIoT applies IoT to industry by presenting great opportunities for new applications having the capacity to improve productivity in industry [21]. An important feature of IoT technology which drives IIoT is the collaboration among heterogeneous IoT devices.…”

Section: Related Workmentioning

confidence: 99%

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Oyewobi

Hancke

Abu‐Mahfouz

et al. 2019

Sensors

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The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

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“…Due to its simple learning curve, it is also recommended by some researchers as an education tool for learning basic IoT technology [19]. However, its vast options for different types of communication protocols and mediums also attract researchers to consider Node-RED to be part of the possible replacement for industrial control systems [21] [22], even though it is also excellent to be part of wireless sensor networks when installed at compatible devices such as Raspberry Pi [20] [23]. However, due to its built-in nodes that allows MQTT protocol connectivity [21], allows us to connect Node-RED with ESP8266, which is the setup that will be done on this project.…”

Section: Literature Reviewmentioning

confidence: 99%

Automotive Real-Time Data Acquisition Using Wi-Fi Connected Embedded System

Rahman¹,

Selamat

Alimin

et al. 2019

Elektrika

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The advancement in embedded systems, which includes the mass deployment of internet-connected electronics, allows the concept of Internet of Things (IoT), to become a reality. This paper discusses one example of how an internet-connected embedded system is utilized in an automotive system. An Electronic Control Unit (ECU), which functions as a control unit in a fuel injection system, are equipped with Wi-Fi capability and installed on 110cc motorcycle. The ECU is connected to multiple sensors that is used by the ECU as part of control system, as well as giving raw data in real time to the server by using Wi-Fi as the communication medium. The server will accumulate data transmitted from ECU by using MQTT protocol, chosen due to its minimal data profile. The data can be visualized through web portal, or opened by any other web-enabled devices. The data collected may also be used later for any other purposes, such as On-Board Diagnostics (OBD) system, etc.

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Industrial Communication based on Modbus and Node-RED

Cited by 47 publications

References 7 publications

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things

Automotive Real-Time Data Acquisition Using Wi-Fi Connected Embedded System

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