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Sensors and IoT Technologies for the Smart Industry
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Sensors and IoT Technologies for the Smart Industry

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

Deadline for manuscript submissions: 20 February 2026 | Viewed by 4162

Special Issue Editors

Dr. Michalis Michaelides

E-Mail Website
Guest Editor
Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 30 Arch. Kyprianos Street, 3036 Limassol, Cyprus
Interests: wireless sensor networks; event detection and localization; fault detection and diagnosis; fault tolerance; collaborative signal and information processing; environmental monitoring; intelligent irrigation systems; intelligent buildings and intelligent transport
Special Issues, Collections and Topics in MDPI journals
Dr. Herodotos Herodotou

E-Mail Website
Guest Editor
Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 30 Arch. Kyprianos Street, 3036 Limassol, Cyprus
Interests: smart grids; data analytics; data-intensive computing; data processing systems; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Sheraz Aslam

E-Mail Website
Guest Editor
Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 30 Arch. Kyprianos Street, Limassol 3036, Cyprus
Interests: smart grids; data analytics; sustainable energy generations; intelligent transportation systems; sea transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industry 4.0 paradigm is boosting the relevance of sensors and Internet of Things (IoT) technologies in the manufacturing and production industries. Some of the primary opportunities offered by Industry 4.0 are predictive maintenance (PdM), which makes use of condition monitoring data to diagnose and prognose anomalies in production processes, manufacturing equipment, and products; smart factory automation that enhances efficiency and reduces human error by automating repetitive tasks; enhanced supply chain management that uses IoT sensor data to enable real-time tracking of materials, products, and shipments; and data-driven decision making that analyzes the data to provide insights into operations, customer behavior, and market trends. Industry 4.0 strongly relies on IoT, which digitalizes physical actions, enabling human-to-human, human-to-machine, and machine-to-machine connections for intelligent perception.

This Special Issue of Sensors is entitled ‘Sensors and IoT Technologies for the Smart Industry’. It seeks papers that present recent achievements in the area and enable the community to address some of the existing challenges associated with the smart industry, particularly those related to the development and deployment of IoT-enabled solutions. We are interested in contributions that address one or more aspects of the whole data lifecycle, from intelligent sensing and data acquisition to efficient data storage, data analysis, and visualization. This Special Issue particularly welcomes papers that present intelligent algorithms and approaches to converting the collected data into meaningful information to enable decision support for smart industry applications.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Intelligent sensors and actuators for the smart industry;
  • Smart grid, smart agriculture, smart factories, smart cities, smart buildings, smart ports, and smart transport;
  • Predictive maintenance (PdM);
  • Prognostics and health management (PHM);
  • Enhanced supply chain management;
  • Data-driven decision making for Industry 4.0;
  • Development and deployment of IoT-enabled solutions for the smart industry.

Dr. Michalis Michaelides
Dr. Herodotos Herodotou
Dr. Sheraz Aslam
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
  • Internet of Things (IoT)
  • predictive maintenance (PdM)

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Published Papers (3 papers)

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25 pages, 2820 KiB  
Article
Fault Detection of Cyber-Physical Systems Using a Transfer Learning Method Based on Pre-Trained Transformers
by Pooya Sajjadi, Fateme Dinmohammadi and Mahmood Shafiee
Sensors 2025, 25(13), 4164; https://doi.org/10.3390/s25134164 - 4 Jul 2025
Viewed by 508
Abstract
As industries become increasingly dependent on cyber-physical systems (CPSs), failures within these systems can cause significant operational disruptions, underscoring the critical need for effective Prognostics and Health Management (PHM). The large volume of data generated by CPSs has made deep learning (DL) methods [...] Read more.
As industries become increasingly dependent on cyber-physical systems (CPSs), failures within these systems can cause significant operational disruptions, underscoring the critical need for effective Prognostics and Health Management (PHM). The large volume of data generated by CPSs has made deep learning (DL) methods an attractive solution; however, imbalanced datasets and the limited availability of fault-labeled data continue to hinder their effective deployment in real-world applications. To address these challenges, this paper proposes a transfer learning approach using a pre-trained transformer architecture to enhance fault detection performance in CPSs. A streamlined transformer model is first pre-trained on a large-scale source dataset and then fine-tuned end-to-end on a smaller dataset with a differing data distribution. This approach enables the transfer of diagnostic knowledge from controlled laboratory environments to real-world operational settings, effectively addressing the domain shift challenge commonly encountered in industrial CPSs. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on publicly available datasets generated from a laboratory-scale replica of a modern industrial water purification facility. The results show that the model achieves an average F1-score of 93.38% under K-fold cross-validation, outperforming baseline models such as CNN and LSTM architectures, and demonstrating the practicality of applying transformer-based transfer learning in industrial settings with limited fault data. To enhance transparency and better understand the model’s decision process, SHAP is applied for explainable AI (XAI). Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
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21 pages, 1764 KiB  
Article
Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data
by Sheraz Aslam, Alejandro Navarro, Andreas Aristotelous, Eduardo Garro Crevillen, Alvaro Martınez-Romero, Álvaro Martínez-Ceballos, Alessandro Cassera, Kyriacos Orphanides, Herodotos Herodotou and Michalis P. Michaelides
Sensors 2025, 25(13), 3923; https://doi.org/10.3390/s25133923 - 24 Jun 2025
Viewed by 1324
Abstract
Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend [...] Read more.
Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
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23 pages, 1475 KiB  
Article
Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies
by Danilo Pietro Pau, Simone Tognocchi and Marco Marcon
Sensors 2025, 25(12), 3679; https://doi.org/10.3390/s25123679 - 12 Jun 2025
Viewed by 1450
Abstract
This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, [...] Read more.
This work devised an on-device learning approach to self-calibrate Micro-Electro-Mechanical Systems-based Inertial Measurement Units (MEMS-IMUs), integrating a digital signal processor (DSP), an accelerometer, and a gyroscope in the same package. The accelerometer and gyroscope stream their data in real time to the DSP, which runs artificial intelligence (AI) workloads. The real-time sensor data are subject to errors, such as time-varying bias and thermal stress. To compensate for these drifts, the traditional calibration method based on a linear model is applicable, and unfortunately, it does not work with nonlinear errors. The algorithm devised by this study to reduce such errors adopts Radial Basis Function Neural Networks (RBF-NNs). This method does not rely on the classical adoption of the backpropagation algorithm. Due to its low complexity, it is deployable using kibyte memory and in software runs on the DSP, thus performing interleaved in-sensor learning and inference by itself. This avoids using any off-package computing processor. The learning process is performed periodically to achieve consistent sensor recalibration over time. The devised solution was implemented in both 32-bit floating-point data representation and 16-bit quantized integer version. Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. It integrates 32 KiB of program RAM and 8 KiB of data RAM. No permanent memory is integrated into the package. The two (fp32 and int16) RBF-NN models occupied less than 21 KiB out of the 40 available, working in real-time and independently in the sensor package. The models, respectively, compensated between 46% and 95% of the accelerometer measurement error and between 32% and 88% of the gyroscope measurement error. Finally, it has also been used for attitude estimation of a micro aerial vehicle (MAV), achieving an error of only 2.84°. Full article
(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)
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