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Collaborative Data-Access Enablers in the Industrial Internet of Things
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Collaborative Data-Access Enablers in the Industrial Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2551

Special Issue Editors

Dr. Danfeng Sun

E-Mail Website
Guest Editor
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: IIoT; programmable logic controllers (PLC); machine learning
Prof. Dr. Huifeng Wu

E-Mail Website
Guest Editor
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: intelligent software; edge computing; intelligent control; embedded system; programmable technology
Dr. Jin Fan

E-Mail Website
Guest Editor
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: industrial big data analysis; anomaly detection; ubiquitous computing

Special Issue Information

Dear Colleagues,

The Industrial Internet of Things (IIoT) is being increasingly implemented in various fields such as smart manufacturing, industrial automation, logistics, and warehousing to promote industrial modernization and intelligence. However, due to the differences in devices, data formats, and protocols involved in the IIoT, interoperability and standardization of data face challenges. Therefore, it is necessary to balance the diversity of devices and the consistency of data while achieving automation and intelligence on a large scale. Thus, standardization, data acquisition, data fusion, and scalable architecture play critical roles in overcoming the challenges of data interoperability and standardization in the IIoT. These technologies and solutions provide robust technological and theoretical support for achieving industrial intelligence.

In the IIoT domain, data access is a paramount research area. Nevertheless, numerous challenges must be confronted. Firstly, the sensors' colossal data collection demands an aptitude to accumulate and handle immense loads of data. This necessity encompasses efficacious access, transmission, storage, and management. Secondly, the multiformity of devices and technologies employed cultivates distinguishing data formats and divergent semantics, affording impediments to data integration and processing. Additionally, the data's profuse application venues underscore the indispensability of efficient processing, application and data security. Given the multitude of incompatible data sources, the challenge arises as to how to acquire these heterogeneous data flexibly, and how to uniformly format them to be suitable for a range of diverse applications. Only through surmounting these challenges can one manifest the efficiency, scalability, and customizability of IIoT systems.

In this special issue, we aim to provide a forum for colleagues to publish recent research results related to the frontiers of sensing data access, as well as comprehensive surveys of state-of-the-art industry intelligence in relevant specific areas. Both original contributions with theoretical novelty and practical solutions for addressing particular problems are solicited. Prospective authors are invited to submit original manuscripts reporting novel theoretical and experimental contributions on topics including but not limited to:

  • Standardization of data access in IIoT;
  • Trust and identity management in data access;
  • Blockchain technologies in data access;
  • Scalable architecture of data access in IIoT;
  • Data acquisition in of data access in IIoT;
  • Data fusion of data access in IIoT;
  • Access control for shared data in IIoT devices;
  • Communication protocols in data access;
  • Machine learning or deep learning-based data access solutions in IIoT;
  • Hardware and software co-design for data access;
  • Cloud computing integration and big data analysis in data access.

Dr. Danfeng Sun
Prof. Dr. Huifeng Wu
Dr. Jin Fan
Guest Editors

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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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.

Keywords

  • data access
  • IIoT
  • machine learning

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Published Papers (2 papers)

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23 pages, 1146 KiB  
Article
Norm Emergence through Conflict-Blocking Interactions in Industrial Internet of Things Environments
by Yuchen Wang, Yanqin Miao, Gang Fu, Peng Lu, Yikun Yang, Wen Gu, Zijie Fang and Lei Niu
Sensors 2024, 24(18), 6047; https://doi.org/10.3390/s24186047 - 19 Sep 2024
Viewed by 672
Abstract
Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms [...] Read more.
Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms using decentralized interaction-based methods has attracted much attention. However, the current methods overlook Industrial Internet of Things (IIoT) environments. In IIoT, there is a prevalent feature called “conflict-blocking”, where agents’ conflicting action strategies can block an interaction from being completed or even cause danger. To facilitate norm emergence in IIoT, we propose a framework to support agent decisions in conflict-blocking interactions. The framework aids in achieving system scalability by integrating the fusion of agent beliefs about norms. We prove that the proposed framework guarantees norm emergence. We also theoretically and experimentally analyze the time required for norm emergence under the influence of various factors, such as the number of agents. A vehicle movement simulator is also developed to vividly illustrate the process of norm emergence. Full article
(This article belongs to the Special Issue Collaborative Data-Access Enablers in the Industrial Internet of Things)
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14 pages, 1123 KiB  
Article
Machine Learning Model Application and Comparison in Actuated Traffic Signal Forecasting
by Feng Xie, Sebastian Naumann, Olaf Czogalla and Hartmut Zadek
Sensors 2023, 23(15), 6912; https://doi.org/10.3390/s23156912 - 3 Aug 2023
Viewed by 1326
Abstract
Traffic signal forecasting plays a significant role in intelligent traffic systems since it can predict upcoming traffic signal without using traditional radio-based direct communication with infrastructures, which causes high risk in the communication security. Previously, mathematical and statistical approach has been adopted to [...] Read more.
Traffic signal forecasting plays a significant role in intelligent traffic systems since it can predict upcoming traffic signal without using traditional radio-based direct communication with infrastructures, which causes high risk in the communication security. Previously, mathematical and statistical approach has been adopted to predict fixed time traffic signals, but it is no longer suitable for modern traffic-actuated control systems, where signals are dependent on the dynamic requests from traffic flows. And as a large amount of data is available, machine learning methods attract more and more attention. This paper views signal forecasting as a time-series problem. Firstly, a large amount of real data is collected by detectors implemented at an intersection in Hanover via IoT communication among infrastructures. Then, Baseline Model, Dense Model, Linear Model, Convolutional Neural Network, and Long Short-Term Memory (LSTM) machine learning models are trained by one-day data and the results are compared. At last, LSTM is selected for a further training with one-month data producing a test accuracy over 95%, and the median of deviation is only 2 s. Moreover, LSTM is further evaluated as a binary classifier, generating a classification accuracy over 92% and AUC close to 1. Full article
(This article belongs to the Special Issue Collaborative Data-Access Enablers in the Industrial Internet of Things)
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