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Recent Advances in Sensor Networks and Industrial IoT Technologies
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Recent Advances in Sensor Networks and Industrial IoT Technologies

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 11611

Special Issue Editors

Dr. Federico Tramarin

E-Mail Website
Guest Editor
Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, 41125 Modena, Italy
Interests: performance measurements on networks; smart sensors and measurement systems; wide-area measurement; industrial communication systems; wireless sensor networks; Industrial IoT; time-sensitive networking; WiFi-TSN integration; machine learning/artificial intelligence for networks
Special Issues, Collections and Topics in MDPI journals
Dr. Aamir Mahmood

E-Mail Website
Guest Editor
Department of Information Systems and Technology, Mid Sweden University, 851 70 Sundsvall, Sweden
Interests: 5G-and-beyond networks; ultra-reliable low-latency communications (URLLC); radio network optimization and management; time synchronization and positioning; wireless sensor networks; 5G-TSN integration; industrial IoT; RF coexistence mitigation; machine learning/artificial intelligence for networks
Dr. Tommaso Fedullo

E-Mail Website
Guest Editor
1. Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy
2. Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, 41125 Modena, Italy
Interests: instrumentation and measurements; real time communications; wireless communications; smart and distributed measurement systems; industrial IoT; industrial communication systems; wireless sensors networks; artificial intelligence for sensors networks; machine learning; time sensitive networking; digital twins; safety industrial systems
Dr. Alberto Morato

E-Mail Website
Guest Editor
National Research Council of Italy, CNR–IEIIT, Via Gradenigo 6/B, 35131 Padova, Italy
Interests: real time communications; wireless communications; wireless sensors networks; instrumentation and measurements; distributed measurement systems; IoT and IIoT; artificial intelligence and machine learning for networks and sensors; time sensitive networking; industrial functional safety protocols and systems; real time embedded systems

Special Issue Information

Dear Colleagues,

The use of Industrial IoT systems for the development of ever performant smart and wide-area distributed measurement systems and sensor networks is gaining fundamental importance. The technological transformations fostered by Industry 4.0 and Industry 5.0 paradigms deeply rely on a data-centric vision, where information and measurements are seamlessly exchanged and shared among distributed nodes. To enable these concepts, it is paramount to design scalable and efficient wired, wireless and hybrid communication networks, also capable of operating in harsh and safety/security-critical environments. At present, industrial communication is moving towards time-sensitive (ultra-reliable and low-latency) networking, paving the way for the adoption of innovative technologies such as, among others, 5G/6G, WiFi6, LPWANs, and Time Sensitive Networks (TSN). This is enabling both vertical integration of sensors and networked manufacturing systems from the field level up to the cloud, and horizontal integration, which aggregates distributed and heterogeneous computing and control infrastructures forming a global cyber-physical system. In this context, the aim of this Special Issue is to gather all the recent advances in Industrial IoT and sensor networks technologies, comprising innovative communication systems, Cyber-Physical Systems, Artificial Intelligence, Edge and Fog architectures, and techniques applied to both measurements transmission and analysis.   

This Special Issue focuses on (but is not limited to) the following:

  • Real Time Industrial Sensors networks;
  • Industrial Wireless Communication;
  • Industrial applications of Time Sensitive Networking;
  • Artificial Intelligence (AI) techniques for measurements analysis;
  • Machine Learning and AI for industrial networks;
  • Edge and Fog computing architectures for Industrial applications;
  • Industrial CPSs;
  • 5G/6G industrial networks;
  • WiFi6 for industrial applications;
  • Industrial LPWANs;
  • Security and Safety of Industrial Networks;
  • Modeling, simulation, and analysis of IoT Industrial Systems;
  • Ultra Reliable Low Latency (URLL) industrial communications;
  • Digital Twins for the novel smart factory;
  • IoT-based measurement systems;
  • Bluetooth sensors networks.

Dr. Federico Tramarin
Dr. Aamir Mahmood
Dr. Tommaso Fedullo
Dr. Alberto Morato
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.

Published Papers (4 papers)

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Research

18 pages, 1282 KiB  
Article
Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes
by Jisu Ahn, Younjeong Lee, Namji Kim, Chanho Park and Jongpil Jeong
Sensors 2023, 23(17), 7331; https://doi.org/10.3390/s23177331 - 22 Aug 2023
Cited by 5 | Viewed by 2232
Abstract
In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two [...] Read more.
In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two main techniques to enable effective predictive maintenance in this environment. We propose a 1DCNN-Bilstm model for time series anomaly detection and predictive maintenance of manufacturing processes. The model combines a 1D convolutional neural network (1DCNN) and a bidirectional LSTM (Bilstm), which is effective in extracting features from time series data and detecting anomalies. In this paper, we combine a federated learning framework with these models to consider the distributional shifts of time series data and perform anomaly detection and predictive maintenance based on them. In this paper, we utilize the pump dataset to evaluate the performance of the combination of several federated learning frameworks and time series anomaly detection models. Experimental results show that the proposed framework achieves a test accuracy of 97.2%, which shows its potential to be utilized for real-world predictive maintenance in the future. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Networks and Industrial IoT Technologies)
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20 pages, 4656 KiB  
Article
An Integrated Artificial Intelligence of Things Environment for River Flood Prevention
by Zakaria Boulouard, Mariyam Ouaissa, Mariya Ouaissa, Farhan Siddiqui, Mutiq Almutiq and Moez Krichen
Sensors 2022, 22(23), 9485; https://doi.org/10.3390/s22239485 - 5 Dec 2022
Cited by 11 | Viewed by 3053
Abstract
River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of [...] Read more.
River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Networks and Industrial IoT Technologies)
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21 pages, 19796 KiB  
Article
Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI
by Raihan Bin Mofidul, Md. Morshed Alam, Md. Habibur Rahman and Yeong Min Jang
Sensors 2022, 22(22), 8980; https://doi.org/10.3390/s22228980 - 20 Nov 2022
Cited by 8 | Viewed by 3335
Abstract
The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual [...] Read more.
The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Networks and Industrial IoT Technologies)
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18 pages, 1759 KiB  
Article
An IoT Measurement System Based on LoRaWAN for Additive Manufacturing
by Tommaso Fedullo, Alberto Morato, Giovanni Peserico, Luca Trevisan, Federico Tramarin, Stefano Vitturi and Luigi Rovati
Sensors 2022, 22(15), 5466; https://doi.org/10.3390/s22155466 - 22 Jul 2022
Cited by 8 | Viewed by 2205
Abstract
The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power [...] Read more.
The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Networks and Industrial IoT Technologies)
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