Classifications, Concerns, and Concepts: Reference Architectures and the Industrial Internet of Things

The expected disruptive developments collectively referred to as the Internet of Things (IoT) have drawn significant attention in many industries, disciplines, and organizations. While the concrete benefits and requirements are still not sufficiently clear, the general agreement on its relevance and impact is undeniable. As a result, a large number of initiatives and consortia from industry and research have been formed to all set the de facto standards and best practices. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications, and the concerns they target.

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Editor’s Note: According to the authors, this article on the structuring of reference architectures for the Industrial Internet of Things (IIoT) contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications, and the concerns they target. The article supplies alignment of shared concepts, identifies gaps, and gives a structured mapping of concerns at each part of respective reference architectures. The article also links relevant industry standards and technologies to the architectures, allowing for a more effective search for specifications and guidelines and supporting direct technology adoption. Understanding the classifications, concerns, and concepts presented in this article may be beneficial for legal, business, and information technology professionals in the eDiscovery ecosystem as they consider the challenges and opportunities for data discovery and legal discovery in the age of the IIoT.

An extract from the article by Sebastian Bader, Maria Maleshkova, and Steffen Lohmann

Structuring Reference Architectures for the Industrial Internet of Things*

Abstract

The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption.

Introduction

The expected disruptive developments collectively referred to as the Internet of Things (IoT) have drawn significant attention in many industries, disciplines and organizations. While the concrete benefits and requirements are still not sufficiently clear, the general agreement on its relevance and impact is undeniable. As a result, a large number of initiatives and consortia from industry and research have been formed to all set the de facto standards and best practices.

Especially the manufacturing industry is actively involved in numerous activities related to this topic. Organizing this area and enabling effective discussions and design decisions are the targets of several standardization efforts. Many of them provide reference frameworks and architecture models. Reference frameworks in this context provide the necessary structure to transform the combined experiences and best practices, the opportunities of available technologies and the expected implications into understandable guidance for the involved stakeholders.

As a common understanding has not yet been reached, the current situation is characterized by the variety of proposed models and frameworks, created by groups of experts from different countries and domains. Whereas the goal of each approach is to overcome the current confusion, the huge amount of published models is again becoming a source of heterogeneity and misunderstanding. Newcomers and non-experts are overwhelmed by the amount of published recommendations and suggestions, contradicting terminology, inconsistent structuring and proposed best practices. The uncountable efforts intended to structure the domain have by now created another dimension of complexity. The thereby created barriers aggravate the adaption of crucial developments and decelerate further progress. Moreover, the rising difficulty to find and classify relevant information undermines the further propagation of the core principles.

Therefore, a consistent alignment of the different frameworks and a structured organization of the main concepts are a pressing need, in order to create a sufficiently complete picture of the current state of the specification processes. Following the assumption that a single model can not cover all requirements, we annotated and interlinked the frameworks and models of the most influential initiatives, which cope with the digitization of the manufacturing domain. An openly available knowledge graph with self-defined and both human and machine-readable concepts, serves as the representation of the derived facts. Based on this grounded mapping of discovered relations, variances and commonalities we illustrate the different scopes and strengths.

This paper contributes to the mentioned challenges by:

  • Providing a methodology to structure, align and compare the various reference frameworks;
  • Presenting a collection of relevant concerns, their hierarchical structure and relationships;
  • Providing configurable visual views of the characteristics and relations between the concerns and the reference frameworks (http://i40.semantic-interoperability.org/sto-visualization/);
  • Offering an analysis of the thereby gained insights, for example, frequently covered areas or inconsistencies, which need further attention from the community.

The remainder of this paper is structured as follows: Section 2 gives an overview of other approaches to structure the observed frameworks and of surveys of the IIoT domain. The used methodology and data model is introduced in Section 3, followed by a description how the IIoT reference frameworks have been selected Section 4 and an introduction of the most relevant ones in Section 6, followed by an outline of the findings and outline research gaps in Section 7 and conclude with our lessons learned and future activities (Section 8).

Read the complete article at Structuring Reference Architectures for the Industrial Internet of Things

* Bader, S.R.; Maleshkova, M.; Lohmann, S. Structuring Reference Architectures for the Industrial Internet of Things. Future Internet 201911, 151.


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Original Source: Future Internet 201911(7), 151; https://doi.org/10.3390/fi11070151 (Republished Under the Creative Commons Attribution License)


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