Industrial Internet of Things (IIoT) Platforms - An Evaluation Model

Moura, Ralf Luis de; Ceotto, Luciana de Landa Farias; Gonzalez, Alexandre; Ricardo, Toledo

doi:10.1109/csci46756.2018.00194

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…While IIoT will initially improve existing processes and augmented current infrastructure, the ultimate goal will be to realize entirely new and dramatically improved products and services. Successful companies will be those that understand how and where IoT technologies and solutions will drive opportunities for operational improvements, new and enhanced products and services, as well as completely new business models [10].…”

Section: Resultsmentioning

confidence: 99%

“…For example, a vacuum oven might generate a vacuum data point every second while a manufacturing execution system (MES) might generate a route completion data point every five minutes. While the content and form of the two data points differ, they're both streams [10].…”

Section: Discussion On Used Modelmentioning

confidence: 99%

“…This is exclusively beneficial for OT-based industries that are using tools or software to collect process and analyze data generated by the industrial machines that are managed and operated in an IIoT ecosystem. These types of industrial setups face critical issues of software-legacy, which, in turn, greatly hinders the interoperability factor [10].By integrating AI algorithms in an IIoT infrastructure, the entire mechanical apparatus can be trained and automated to manage and run itself smartly and intelligently. The influx of data from an IIoT ecosystem of devices into AI-powered analytical models can significantly enhance the entire industrial procedure, not just the manufacturing department as is mostly spoken of.…”

Section: Background Informationmentioning

confidence: 99%

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The Emergence of Artificial Intelligence for Industrial Internet of Thing Engagement

2021

IJATCSE

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New IT technologies will help enterprises across all industries to master future challenges. As AI emerges from science fiction to become the frontier of world changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Today, the term “industrial automation” is generally referred to in the context of Industry 4.0 and the Industrial Internet of Things the two most recent technological revolutions in the industrial sector. The core principles of Industry 4.0 focus on increasing productivity, cost efficiency, quality, and safety by utilizing innovative technologies enabled by the IIoT, such as cyberphysical systems, cloud computing, big data, artificial intelligence and machine learning. When implementing this technology by using data centric digital business model. Internet of Things & Artificial Intelligence are poised to transform industrial operations.

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Section: Resultsmentioning

confidence: 99%

Section: Discussion On Used Modelmentioning

confidence: 99%

Section: Background Informationmentioning

confidence: 99%

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The Emergence of Artificial Intelligence for Industrial Internet of Thing Engagement

2021

IJATCSE

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“…On the other hand, it creates issues for researchers that seek to understand the interplay of IIoT platforms' architecture and business models, which are strongly interwoven in the context of digital technology. Research has already put effort into investigating IIoT platforms, focusing on their business model [7,8], framework [9], or design criteria [10]. However, we still miss a unified classification of IIoT platforms' fundamental building blocks, which we subsume as architectural design options, to enable a transparent evaluation and comparison of existing IIoT platforms.…”

Section: Introductionmentioning

confidence: 99%

A Taxonomy of Industrial IoT Platforms’ Architectural Features

Arnold

Jöhnk

Vogt

et al. 2021

Lecture Notes in Information Systems and Organisation

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In the industrial Internet of Things (IIoT), the concept of digital platforms has received significant attention. Although IIoT platforms revolve around similar business objectives, they address a variety of use cases and, thus, differ considerably in their architectural setup. While research has already investigated IIoT platforms from a business or design perspective, little is known about their underlying technology stack and its implications. To unveil different IIoT platform configurations and better understand their architectural design, we systematically develop and validate a taxonomy of IIoT platforms' architectural features based on related literature, real-world cases, and expert interviews. On this foundation, we identify and discuss four IIoT platform archetypes. Our findings contribute to the descriptive knowledge in this ambiguous research field, while also elucidating the interplay of IIoT platforms' architectural setup and their purpose. From a managerial viewpoint, our results may guide practitioners in comparing and selecting a suitable IIoT platform.

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“…The industrial internet of things (IIoT) is a physical network of things, objects or devices (that contain embedded technology) for sensing and remote control, in an industrial context, that allows greater integration between physical and cyber worlds [ 13 ]. In the fifth-generation (5G) era, efficient, reliable and high-performance applications’ provision has to be combined with exploitation of capabilities offered by 5G networks.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Trakadas

Simoens

Gkonis

et al. 2020

Sensors

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The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

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Industrial Internet of Things (IIoT) Platforms - An Evaluation Model

Cited by 19 publications

References 10 publications

The Emergence of Artificial Intelligence for Industrial Internet of Thing Engagement

The Emergence of Artificial Intelligence for Industrial Internet of Thing Engagement

A Taxonomy of Industrial IoT Platforms’ Architectural Features

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

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