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Special Issue "Towards an Industrial Internet of Things (IIoT)"

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

Deadline for manuscript submissions: closed (31 May 2019).

Special Issue Editors

Prof. Dr. Boudewijn R. Haverkort
Website
Guest Editor
University of Twente, Faculty of Electrical Engineering, Mathematics & Computer Science, Zilverling (building no. 11), Hallenweg 19, 7522NH Enschede, The Netherlands
Interests: cyber-physical systems; energy systems; critical infrastructures; performance & dependability evaluation; model checking
Prof. Dr. Paul Havinga
Website
Guest Editor
Pervasive Systems Group, Department of Computer Science, University of Twente, 7500 AE, Enschede, The Netherlands
Interests: sensor data analytics; cyber physical systems; wireless sensor networks
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleague,

Over the last few years, we have seen an enormous increase of the number of (wireless) sensors, which together make up the internet of things (IoT). Whereas, initially, these sensors were mostly used for recreational purposes, currently, they are more and more often employed in professional and industrial contexts. The application of the IoT to industry is often referred to as the Industrial Internet of Things (IIoT). Its development is closely related to what is also known as “Industry 4.0” or “Smart Industry”. The IIoT will revolutionize manufacturing by enabling the acquisition and accessibility of larger amounts of data, at much larger speeds and far more efficiently than before. This will in turn enable manufacturers to produce better products more efficiently and in a more sustainable fashion (by being more careful with resources). Furthermore, the resulting products, embedding IoT-nodes, will also become part of the IIoT; this will allow a more efficient use of the products, improving satisfaction and the products themselves, for example, by enabling smart predictive maintenance based on the employed products. Finally, the IIoT will also change the way people work in industries: this aspect will also be considered in this Special Issue.

This Special Issue invites all sorts of contributions related to the IIoT, including studies on smart (wireless) sensor networks, cloud-based data collection techniques, machine learning in the IIoT context, improved manufacturing through the IIoT, predictive maintenance, work conditions changes for the human workforce, etc.

Prof. Dr. Boudewijn R. Haverkort
Prof. Dr. Paul J.M. Havinga
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 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. 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 2000 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

  • wireless sensor networks
  • cloud-based data collection techniques
  • machine learning for IIoT
  • improved manufacturing through the IIoT
  • predictive maintenance for/with/in IIoT
  • the future workforce under IIoT
  • interoperability of IIoT
  • special IIoT protocols, such as MQTT

Published Papers (5 papers)

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Research

Open AccessArticle
Hardware Mechanism for Energy Saving in WiFi Access Points
Sensors 2019, 19(21), 4745; https://doi.org/10.3390/s19214745 - 01 Nov 2019
Cited by 1
Abstract
Wireless fidelity (WiFi) networks are deployed in several varied environments all around the World. Usually, the wireless fidelity access points are always on in houses and other small companies. In buildings of large companies and public organizations and in university campuses the number [...] Read more.
Wireless fidelity (WiFi) networks are deployed in several varied environments all around the World. Usually, the wireless fidelity access points are always on in houses and other small companies. In buildings of large companies and public organizations and in university campuses the number of access points is elevated; they are powered using power over the ethernet and are always on. Consequently, they consume a considerable amount of electric energy. The last versions of the International Electric and Electronic Engineers 802.11 standardized procedures to save energy in a wireless fidelity terminal but not in the access point. We designed a formal method to show when energy can be saved in wireless fidelity access points considering different power supplies for the access point: an electric energy battery and a standard voltage supply. We use an external battery that stores electric energy during an interval of time from a standard voltage supply (Charge period). After that interval (Discharge period), the energy supply for the access point is the external battery. Those intervals of time are repeated sequentially (Charge and Discharge cycles). We verified our formal model implementing a hardware circuit that controls the power supply for the access point. The amount of energy saving for a large number of of access points during a long period of time is considerably high. Full article
(This article belongs to the Special Issue Towards an Industrial Internet of Things (IIoT))
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Open AccessArticle
The Advent of the Internet of Things in Airfield Lightning Systems: Paving the Way from a Legacy Environment to an Open World
Sensors 2019, 19(21), 4724; https://doi.org/10.3390/s19214724 - 31 Oct 2019
Abstract
This paper discusses the design and prototype implementation of a software solution facilitating the interaction of third-party developers with a legacy monitoring and control system in the airfield environment. By following the Internet of Things (IoT) approach and adopting open standards and paradigms [...] Read more.
This paper discusses the design and prototype implementation of a software solution facilitating the interaction of third-party developers with a legacy monitoring and control system in the airfield environment. By following the Internet of Things (IoT) approach and adopting open standards and paradigms such as REpresentational State Transfer (REST) and Advanced Message Queuing Protocol (AMQP) for message dispatching, the work aims at paving the way towards a more open world in the airfield industrial sector. The paper also presents performance results achieved by extending legacy components to support IoT standards. Quantitative results not only demonstrate the feasibility of the proposed solution, but also its suitability in terms of prompt message dispatching and increased fault tolerance. Full article
(This article belongs to the Special Issue Towards an Industrial Internet of Things (IIoT))
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Open AccessArticle
Smart Industrial IoT Monitoring and Control System Based on UAV and Cloud Computing Applied to a Concrete Plant
Sensors 2019, 19(15), 3316; https://doi.org/10.3390/s19153316 - 28 Jul 2019
Cited by 11
Abstract
Unmanned aerial vehicles (UAVs) are now considered one of the best remote sensing techniques for gathering data over large areas. They are now being used in the industry sector as sensing tools for proactively solving or preventing many issues, besides quantifying production and [...] Read more.
Unmanned aerial vehicles (UAVs) are now considered one of the best remote sensing techniques for gathering data over large areas. They are now being used in the industry sector as sensing tools for proactively solving or preventing many issues, besides quantifying production and helping to make decisions. UAVs are a highly consistent technological platform for efficient and cost-effective data collection and event monitoring. The industrial Internet of things (IIoT) sends data from systems that monitor and control the physical world to data processing systems that cloud computing has shown to be important tools for meeting processing requirements. In fog computing, the IoT gateway links different objects to the internet. It can operate as a joint interface for different networks and support different communication protocols. A great deal of effort has been put into developing UAVs and multi-UAV systems. This paper introduces a smart IIoT monitoring and control system based on an unmanned aerial vehicle that uses cloud computing services and exploits fog computing as the bridge between IIoT layers. Its novelty lies in the fact that the UAV is automatically integrated into an industrial control system through an IoT gateway platform, while UAV photos are systematically and instantly computed and analyzed in the cloud. Visual supervision of the plant by drones and cloud services is integrated in real-time into the control loop of the industrial control system. As a proof of concept, the platform was used in a case study in an industrial concrete plant. The results obtained clearly illustrate the feasibility of the proposed platform in providing a reliable and efficient system for UAV remote control to improve product quality and reduce waste. For this, we studied the communication latency between the different IIoT layers in different IoT gateways. Full article
(This article belongs to the Special Issue Towards an Industrial Internet of Things (IIoT))
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Open AccessArticle
Power Factor Compensation Using Teaching Learning Based Optimization and Monitoring System by Cloud Data Logger
Sensors 2019, 19(9), 2172; https://doi.org/10.3390/s19092172 - 10 May 2019
Cited by 9
Abstract
The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) [...] Read more.
The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) calculates the optimal capacitor combination to improve power factor of the installation by measure of voltage, current, and active power. CBO algorithm determines the best solution of capacitor values to install, by applying TLBO in different phases of the algorithm. Electrical variables acquired by the sensors and the variables calculated are stored in CDL using Google Sheets (GS) to monitor and analyse the installation by means of a TLBO algorithm implemented in PFCMS, that optimizes the compensation power factor of installation and determining which capacitors are connected in real time. Moreover, the optimization of the power factor in facilities means economic and energy savings, as well as the improvement of the quality of the operation of the installation. Full article
(This article belongs to the Special Issue Towards an Industrial Internet of Things (IIoT))
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Open AccessArticle
Toward Security Enhanced Provisioning in Industrial IoT Systems
Sensors 2018, 18(12), 4372; https://doi.org/10.3390/s18124372 - 10 Dec 2018
Cited by 6
Abstract
Through the active development of industrial internet of things (IIoT) technology, there has been a rapid increase in the number of different industrial wireless sensor networks (IWSNs). Accordingly, the security of IWSNs is also of importance, as many security problems related to IWSN [...] Read more.
Through the active development of industrial internet of things (IIoT) technology, there has been a rapid increase in the number of different industrial wireless sensor networks (IWSNs). Accordingly, the security of IWSNs is also of importance, as many security problems related to IWSN protocols have been raised and various studies have been conducted to solve these problems. However, the provisioning process is the first step in introducing a new device into the IIoT network and a starting point for IIoT security. Therefore, leakage of security information in the provisioning process makes exposure of secret keys and all subsequent security measures meaningless. In addition, using the exploited secret keys, the attacker can send false command to the node or send false data to the network manager and it can cause serious damage to industrial infrastructure depending on the IWSN. Nevertheless, a security study on the provisioning process has not been actively carried out, resulting in a provisioning process without guaranteed security. Therefore, in this paper, we analyzed security issues of the provisioning process in IWSN by researching prominent IWSN standards, including ISA 100.11a, WirelessHART, and Zigbee, and also an ISA 100.11a-certified device and provisioning process-related studies. Then, we verified the security issues of the provisioning process through testing and analyzing the provisioning process using the ISA 100.11a standard-implemented devices and ISA 100.11a-certified devices. Finally, we discuss security considerations and the direction of future research on provisioning security for IWSN in the IIoT era. Full article
(This article belongs to the Special Issue Towards an Industrial Internet of Things (IIoT))
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