Special Issue "Network Architectures and Protocols for Industrial IoT"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editor

Dr. Rute C. Sofia
Website
Guest Editor
fortiss GmbH, Research Institute of the Free State of Bavaria for Software-Intensive Systems, 80805 Munich, Germany
Interests: network architectures and protocols; IoT; mobility management; edge computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Internet services and networking architectures have evolved steadily to accommodate technological changes and the development of novel services on the Internet. With the expansion of the Internet of Things (IoT), there is now a complete entanglement of the Internet into different realms of our daily lives. Things are becoming increasingly mobile and heterogeneous in terms of computational and storage capabilities, as well as in terms of power. Furthermore, new IoT technologies are expanding the reach of services over large distances, and into new realms, such as space.

These new possibilities open up a myriad of new business possibilities, and in the context of Industrial IoT (IIoT), they are the basis for a new Industrial Revolution. IIoT data will be generated from a gazillion smart devices over very large distances, sustaining a global and wide digitalization, greater efficiency, and improved productivity, among other benefits.

This Special Issue is aimed at archiving the most recent developments on networking architectures and protocols for Industrial IoT. Potential topics include but are not limited to:

-   Networking protocol performance;

-   Protocol interoperability aspects;

-   Automated IoT onboarding mechanisms;

-   Determinism in IIoT wireless environments;

-   Semi-automated brownfield devices integration;

-   Delay Tolerant Networking in IIoT;

-   Information-centric networking in IIoT;

-   Serverless cloud-edge computing mechanisms;

-   AI operationalization in IIoT;

-   Decentralized security mechanisms for IIoT.

Dr. Rute C. Sofia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Open AccessArticle
CMS: A Continuous Machine-Learning and Serving Platform for Industrial Big Data
by and
Future Internet 2020, 12(6), 102; https://doi.org/10.3390/fi12060102 - 10 Jun 2020
Cited by 2
Abstract
The life-long monitoring and analysis for complex industrial equipment demands a continuously evolvable machine-learning platform. The machine-learning model must be quickly regenerated and updated. This demands the careful orchestration of trainers for model generation and modelets for model serving without the interruption of [...] Read more.
The life-long monitoring and analysis for complex industrial equipment demands a continuously evolvable machine-learning platform. The machine-learning model must be quickly regenerated and updated. This demands the careful orchestration of trainers for model generation and modelets for model serving without the interruption of normal operations. This paper proposes a container-based Continuous Machine-Learning and Serving (CMS) platform. By designing out-of-the-box common architecture for trainers and modelets, it simplifies the model training and deployment process with minimal human interference. An orchestrator is proposed to manage the trainer’s execution and enables the model updating without interrupting the online operation of model serving. CMS has been deployed in a 1000 MW thermal power plant for about five months. The system running results show that the accuracy of eight models remains at a good level even when they experience major renovations. Moreover, CMS proved to be a resource-efficient, effective resource isolation and seamless model switching with little overhead. Full article
(This article belongs to the Special Issue Network Architectures and Protocols for Industrial IoT)
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