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Sensing Technologies and Wireless Communications for Industrial IoT
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Sensing Technologies and Wireless Communications for Industrial IoT

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 1978

Special Issue Editors

Dr. Ignacio Rodríguez Larrad

E-Mail Website
Guest Editor
Area of Signal Theory and Communications, Department of Electrical Engineering, University of Oviedo, 33203 Gijon, Spain
Interests: radio propagation; 5G; 6G; industrial communications; non-terrestrial networks; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Rafael González Ayestarán

E-Mail Website
Guest Editor
Area of Signal Theory and Communications, Department of Electrical Engineering, University of Oviedo, 33203 Gijon, Spain
Interests: wireless communications; digital signal processing; 5G; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few years, advances in wireless communications and signal processing have facilitated the development of novel technologies and methods, allowing for integrated sensing and communication in diverse scenarios. One such scenario where these wireless-based techniques are expected to be successful is the industrial IoT scenario, as the need for detailed and ubiquitous positioning, monitoring, control, and coordination between static and mobile production elements is paramount. The present Special Issue addresses the innovative design, development, and application of wireless technologies applied to sensing and control in industrial settings. Furthermore, the current Special Issue invites the submission of research that addresses and investigates new sensing methods, industrial integrated wireless control systems, technologies bridging the physical and virtual industrial worlds, or new cloud-based industrial application methods.

The Guest Editors invite contributions to this Special Issue of Sensors that cover the following topics, which include, but are not limited to:

  • Advanced integrated industrial sensing, wireless control, and monitoring systems;
  • Wireless deployments and field trials in operational industrial scenarios;
  • Industrial scenarios and applications requiring wireless connectivity or sensing;
  • Performance evaluations, simulations, and measurements of wireless IoT systems;
  • Novel sensing schemes and protocol design;
  • Digital twins and integration of the physical world and virtual world in industrial settings.

Dr. Ignacio Rodríguez Larrad
Dr. Rafael González Ayestarán
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

  • Industry 4.0
  • IoT
  • wireless technologies
  • smart production
  • edge-cloud
  • sensing
  • digital twin
  • industrial metaverse

Published Papers (3 papers)

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Research

19 pages, 4597 KiB  
Article
Personal Air-Quality Monitoring with Sensor-Based Wireless Internet-of-Things Electronics Embedded in Protective Face Masks
by Lajos Kuglics, Attila Géczy, Karel Dusek, David Busek and Balázs Illés
Sensors 2024, 24(8), 2601; https://doi.org/10.3390/s24082601 - 18 Apr 2024
Viewed by 86
Abstract
In this paper, the design and research of a sensor-based personal air-quality monitoring device are presented, which is retrofitted into different personal protective face masks. Due to its small size and low power consumption, the device can be integrated into and applied in [...] Read more.
In this paper, the design and research of a sensor-based personal air-quality monitoring device are presented, which is retrofitted into different personal protective face masks. Due to its small size and low power consumption, the device can be integrated into and applied in practical urban usage. We present our research and the development of the sensor node based on a BME680-type environmental sensor cluster with a wireless IoT (Internet of Things)-capable central unit and overall low power consumption. The integration of the sensor node was investigated with traditional medical masks and a professional FFP2-type mask. The filtering efficiency after embedding was validated with a head model and a particle counter. We found that the professional mask withstood the embedding without losing the protective filtering aspect. We compared the inner and outer sensor data and investigated the temperature, pressure, humidity, and AQI (Air Quality Index) relations with possible sensor data-fusion options. The novelty is increased with the dual-sensor layout (inward and outward). It was found that efficient respiration monitoring is achievable with the device. With the analysis of the recorded data, characteristic signals were identified in an urban environment, enabling urban altimetry and urban zone detection. The results promote smart city concepts and help in endeavors related to SDGs (Sustainable Development Goals) 3 and 11. Full article
(This article belongs to the Special Issue Sensing Technologies and Wireless Communications for Industrial IoT)
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17 pages, 12521 KiB  
Article
Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing
by Chenyang Song and Zhipeng Wu
Sensors 2024, 24(6), 1813; https://doi.org/10.3390/s24061813 - 12 Mar 2024
Viewed by 628
Abstract
Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive [...] Read more.
Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain. Full article
(This article belongs to the Special Issue Sensing Technologies and Wireless Communications for Industrial IoT)
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25 pages, 7776 KiB  
Article
Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
by Christian Nelson, Xuhong Li, Aleksei Fedorov, Benjamin Deutschmann and Fredrik Tufvesson
Sensors 2024, 24(5), 1385; https://doi.org/10.3390/s24051385 - 21 Feb 2024
Cited by 2 | Viewed by 595
Abstract
Many concepts for future generations of wireless communication systems use coherent processing of signals from many distributed antennas. The aim is to improve communication reliability, capacity, and energy efficiency and provide possibilities for new applications through integrated communication and sensing. The large bandwidths [...] Read more.
Many concepts for future generations of wireless communication systems use coherent processing of signals from many distributed antennas. The aim is to improve communication reliability, capacity, and energy efficiency and provide possibilities for new applications through integrated communication and sensing. The large bandwidths available in the higher bands have inspired much work regarding sensing in the millimeter-wave (mmWave) and sub-THz bands; however, the sub-6 GHz cellular bands will still be the main provider of wide cellular coverage due to the more favorable propagation conditions. In this paper, we present a measurement system and results of sub-6 GHz distributed multiple-input-multiple-output (MIMO) measurements performed in an industrial environment. From the measurements, we evaluated the diversity for both large-scale and small-scale fading and characterized the link reliability. We also analyzed the possibility of multistatic sensing and positioning of users in the environment, with the initial results showing a mean-square error below 20 cm on the estimated position. Further, the results clearly showed that new channel models are needed that are spatially consistent and deal with the nonstationary channel properties among the antennas. Full article
(This article belongs to the Special Issue Sensing Technologies and Wireless Communications for Industrial IoT)
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