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Special Issue "Real-Time Sensor Networks and Systems for the Industrial IoT"

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

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Dr. Christos Koulamas

Industrial Systems Institute / “Athena” Research Center, PSP Bldg, Stadiou Strt, 26504, Patras, Greece
Website | E-Mail
Interests: real-time distributed embedded systems; wired/wireless industrial networks; Industrial IoT; WSN/ RFID systems
Guest Editor
Dr. Mihai T. Lazarescu

Dipartimento di Elettronica e Telecomunicazioni Politecnico di Torino Turin, Italy
E-Mail
Interests: cost- and energy-efficient design of wireless sensor nodes; high-level synthesis of wireless sensor applications; distributed data processing on embedded devices, learning, adaptability; efficient and secure communication, privacy

Special Issue Information

Dear Colleagues,

Real-time embedded systems are quickly achieving ubiquity, both in people’s everyday life and in industrial environments. While many processes already depend on real-time cyber-physical systems and embedded sensors, IoT integration with cognitive computing and real-time data exchanges is essential for real-time analytics and realizations of digital twins in smart environments and services under the Industrial Internet and Industry 4.0 frameworks’ provisions. IoT-enabled industrial sensor networks encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges.

This Special Issue aims to receive contributions on advances of real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. The topics of interest include (but are not limited to):

  • Real-time sensing interfaces, processing, specific operating system support
  • Real-time sensor and sensing techniques and technologies
  • Real-time communication for sensing and sensor-driven actuation
  • Middleware and tools for complex real-time sensing and actuation systems
  • Reference architectures, frameworks and performance evaluations for edge and fog computing in real-time IIoT
  • Formal methods, modelling, simulation and development tools for real-time sensor networks
  • Real-time sensor network support for digital twins and edge-based analytics
  • Security for real-time systems: low-overhead, efficient bootstrapping and session establishment, resilience to side-channel attacks, etc.
  • Real-time low-power and low-cost sensing
  • Energy supplies suitable for low maintenance, high availability real-time sensing
  • Real-time sensing for safety systems
  • Applications and use-cases of IoT-enabled, smart, real-time sensor networks and systems in manufacturing, transportation, health and energy sectors.

Dr. Christos Koulamas
Dr. Mihai T. Lazarescu
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 1800 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

  • Industrial IoT
  • Real-Time Embedded Systems
  • Sensor & Actuator Networks
  • Industrial Internet & Industry 4.0
  • Edge & Fog Computing
  • Real-Time Analytics
  • Real-Time Digital Twins

Published Papers (3 papers)

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Research

Open AccessArticle Transmission Scheduling Schemes of Industrial Wireless Sensors for Heterogeneous Multiple Control Systems
Sensors 2018, 18(12), 4284; https://doi.org/10.3390/s18124284
Received: 14 November 2018 / Revised: 30 November 2018 / Accepted: 1 December 2018 / Published: 5 December 2018
Cited by 1 | PDF Full-text (740 KB) | HTML Full-text | XML Full-text
Abstract
The transmission scheduling scheme of wireless networks for industrial control systems is a crucial design component since it directly affects the stability of networked control systems. In this paper, we propose a novel transmission scheduling framework to guarantee the stability of heterogeneous multiple [...] Read more.
The transmission scheduling scheme of wireless networks for industrial control systems is a crucial design component since it directly affects the stability of networked control systems. In this paper, we propose a novel transmission scheduling framework to guarantee the stability of heterogeneous multiple control systems over unreliable wireless channels. Based on the explicit control stability conditions, a constrained optimization problem is proposed to maximize the minimum slack of the stability constraint for the heterogeneous control systems. We propose three transmission scheduling schemes, namely centralized stationary random access, distributed random access, and Lyapunov-based scheduling scheme, to solve the constrained optimization problem with a low computation cost. The three proposed transmission scheduling schemes were evaluated on heterogeneous multiple control systems with different link conditions. One interesting finding is that the proposed centralized Lyapunov-based approach provides almost ideal performance in the context of control stability. Furthermore, the distributed random access is still useful for the small number of links since it also reduces the operational overhead without significantly sacrificing the control performance. Full article
(This article belongs to the Special Issue Real-Time Sensor Networks and Systems for the Industrial IoT)
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Open AccessArticle A New Method of Priority Assignment for Real-Time Flows in the WirelessHART Network by the TDMA Protocol
Sensors 2018, 18(12), 4242; https://doi.org/10.3390/s18124242
Received: 17 October 2018 / Revised: 27 November 2018 / Accepted: 29 November 2018 / Published: 3 December 2018
PDF Full-text (863 KB) | HTML Full-text | XML Full-text
Abstract
WirelessHART is a wireless sensor network that is widely used in real-time demand analyses. A key challenge faced by WirelessHART is to ensure the character of real-time data transmission in the network. Identifying a priority assignment strategy that reduces the delay in flow [...] Read more.
WirelessHART is a wireless sensor network that is widely used in real-time demand analyses. A key challenge faced by WirelessHART is to ensure the character of real-time data transmission in the network. Identifying a priority assignment strategy that reduces the delay in flow transmission is crucial in ensuring real-time network performance and the schedulability of real-time network flows. We study the priority assignment of real-time flows in WirelessHART on the basis of the multi-channel time division multiple access (TDMA) protocol to reduce the delay and improve the ratio of scheduled. We provide three kinds of methods: (1) worst fit, (2) best fit, and (3) first fit and choose the most suitable one, namely the worst-fit method for allocating flows to each channel. More importantly, we propose two heuristic algorithms—a priority assignment algorithm based on the greedy strategy for C (WF-C) and a priority assignment algorithm based on the greedy strategy for U(WF-U)—for assigning priorities to the flows in each channel, whose time complexity is O ( m a x ( N m l o g ( m ) , ( N m ) 2 ) ) . We then build a new simulation model to simulate the transmission of real-time flows in WirelessHART. Finally, we compare our two algorithms with WF-D and HLS algorithms in terms of the average value of the total end-to-end delay of flow sets, the ratio of schedulable flow sets, and the calculation time of the schedulability analysis. The optimal algorithm WF-C reduces the delay by up to 44.18 % and increases the schedulability ratio by up to 70.7 % , and it reduces the calculation time compared with the HLS algorithm. Full article
(This article belongs to the Special Issue Real-Time Sensor Networks and Systems for the Industrial IoT)
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Graphical abstract

Open AccessArticle Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor
Sensors 2018, 18(11), 3933; https://doi.org/10.3390/s18113933
Received: 27 October 2018 / Revised: 9 November 2018 / Accepted: 11 November 2018 / Published: 14 November 2018
PDF Full-text (4635 KB) | HTML Full-text | XML Full-text
Abstract
Sensing the voltage developed over a superconducting object is very important in order to make superconducting installation safe. An increase in the resistive part of this voltage (quench) can lead to significant deterioration or even to the destruction of the superconducting device. Therefore, [...] Read more.
Sensing the voltage developed over a superconducting object is very important in order to make superconducting installation safe. An increase in the resistive part of this voltage (quench) can lead to significant deterioration or even to the destruction of the superconducting device. Therefore, detection of anomalies in time series of this voltage is mandatory for reliable operation of superconducting machines. The largest superconducting installation in the world is the main subsystem of the Large Hadron Collider (LHC) accelerator. Therefore a protection system was built around superconducting magnets. Currently, the solutions used in protection equipment at the LHC are based on a set of hand-crafted custom rules. They were proved to work effectively in a range of applications such as quench detection. However, these approaches lack scalability and require laborious manual adjustment of working parameters. The presented work explores the possibility of using the embedded Recurrent Neural Network as a part of a protection device. Such an approach can scale with the number of devices and signals in the system, and potentially can be automatically configured to given superconducting magnet working conditions and available data. In the course of the experiments, it was shown that the model using Gated Recurrent Units (GRU) comprising of two layers with 64 and 32 cells achieves 0.93 accuracy for anomaly/non-anomaly classification, when employing custom data compression scheme. Furthermore, the compression of proposed module was tested, and showed that the memory footprint can be reduced four times with almost no performance loss, making it suitable for hardware implementation. Full article
(This article belongs to the Special Issue Real-Time Sensor Networks and Systems for the Industrial IoT)
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