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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 12102

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Special Issue Editors

Dr. Michalis Michaelides

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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

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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

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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 (6 papers)

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Research

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37 pages, 1545 KB

Open AccessArticle

BFL-SDWANTrust: Blockchain Federated-Learning-Enabled Trust Framework for Secure East–West Communication in Multi-Controller SD-WANs

by Muddassar Mushtaq and Kashif Kifayat

Sensors 2025, 25(16), 5188; https://doi.org/10.3390/s25165188 - 21 Aug 2025

Viewed by 994

Abstract

Software-Defined Wide-Area Networks (SD-WAN) efficiently manage and route traffic across multiple WAN connections, enhancing the reliability of modern enterprise networks. However, the performance of SD-WANs is largely affected due to malicious activities of unauthorized and faulty nodes. To solve these issues, many machine-learning-based malicious-node-detection techniques have been proposed. However, these techniques are vulnerable to various issues such as low classification accuracy and privacy leakage of network entities. Furthermore, most operations of traditional SD-WANs are dependent on a third-party or a centralized party, which leads to issues such single point of failure, large computational overheads, and performance bottlenecks. To solve the aforementioned issues, we propose a Blockchain Federated-Learning-Enabled Trust Framework for Secure East–West Communication in Multi-Controller SD-WANs (BFL-SDWANTrust). The proposed model ensures local model learning at the edge nodes while utilizing the capabilities of federated learning. In the proposed model, we ensure distributed training without requiring central data aggregation, which preserves the privacy of network entities while simultaneously improving generalization across heterogeneous SD-WAN environments. We also propose a blockchain-based network that validates all network communication and malicious node-detection transactions without the involvement of any third party. We evaluate the performance of our proposed BFL-SDWANTrust on the InSDN dataset and compare its performance with various benchmark malicious-node-detection models. The simulation results show that BFL-SDWANTrust outperforms all benchmark models across various metrics and achieves the highest accuracy (98.8%), precision (98.0%), recall (97.0%), and F1-score (97.7%). Furthermore, our proposed model has the shortest training and testing times of 12 s and 3.1 s, respectively. Full article

(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)

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40 pages, 18923 KB

Open AccessArticle

Twin-AI: Intelligent Barrier Eddy Current Separator with Digital Twin and AI Integration

by Shohreh Kia, Johannes B. Mayer, Erik Westphal and Benjamin Leiding

Sensors 2025, 25(15), 4731; https://doi.org/10.3390/s25154731 - 31 Jul 2025

Viewed by 652

Abstract

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)

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25 pages, 2820 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1061

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 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 KB

Open AccessArticle

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 4191

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 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 KB

Open AccessArticle

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 3356

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, 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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Review

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34 pages, 8775 KB

Open AccessReview

Towards Fault-Aware Image Captioning: A Review on Integrating Facial Expression Recognition (FER) and Object Detection

by Abdul Saboor Khan, Muhammad Jamshed Abbass and Abdul Haseeb Khan

Sensors 2025, 25(19), 5992; https://doi.org/10.3390/s25195992 - 28 Sep 2025

Viewed by 685

Abstract

The term “image captioning” refers to the process of converting an image into text through computer vision and natural language processing algorithms. Image captioning is still considered an open-ended topic despite the fact that visual data, most of which pertains to images, is readily available in today’s world. This is despite the fact that recent developments in computer vision, such as Vision Transformers (ViT) and language models using BERT and GPT, have opened up new possibilities for the field. The purpose of this review paper is to provide an overview of the present status of the field, with a specific emphasis on the use of facial expression recognition and object detection for the purpose of image captioning, particularly in the context of fault-aware systems and Prognostics and Health Management (PHM) applications within Industry 4.0 environments. However, to the best of our knowledge, no review study has focused on the significance of facial expressions in relation to image captioning, especially in industrial settings where operator facial expressions can provide valuable insights for fault detection and system health monitoring. This is something that has been overlooked in the existing body of research on image captioning, which is the primary reason why this study was conducted. During this paper, we will talk about the most important approaches and procedures that have been utilized for this task, including fault-aware methodologies that leverage visual data for PHM in smart manufacturing contexts, and we will highlight the advantages and disadvantages of each strategy. The purpose of this review is to present a comprehensive assessment of the current state of the field and to recommend topics for future research that will lead to machine-translated captions that are more detailed and accurate, particularly for Industry 4.0 applications where visual monitoring plays a crucial role in system diagnostics and maintenance. Full article

(This article belongs to the Special Issue Sensors and IoT Technologies for the Smart Industry)

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