Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
4.0
Journals
Sensors
Special Issues
Intelligent Sensors and Advanced Computing: Developments in the Era of...
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition

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

Deadline for manuscript submissions: closed (20 July 2026) | Viewed by 4609

Editors

Dr. Grigore Stamatescu

E-Mail Website
Guest Editor
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: networked-embedded sensing; information processing; control engineering; building automation; smart city; data analytics; computational intelligence; industry and energy applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anatoliy Sachenko

E-Mail Website
Guest Editor
Research Institute for Intelligent Computer Systems, Department of Information Computer Systems and Control, West Ukrainian National University, 46020 Ternopil, Ukraine
Interests: artificial neural network applications; distributed sensor networks; computation intelligence for homeland security and safety; mobile systems and technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Serhii Vladov

E-Mail Website
Guest Editor
Department of Scientific Activity Organisation and Department of Combating Cybercrime, Kharkiv National University of Internal Affairs, 61080 Kharkiv, Ukraine
Interests: neural networks; automatic control systems; machine learning; cybersecurity; neutralization of DDoS attacks; sensor systems of critical infrastructure facilities; helicopter turboshaft engines; fuzzy logic
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first Special Issue, we have decided to launch the second edition, titled “Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition”.

This Special Issue aims to collect high-quality, timely contributions on the interface between modern data acquisition systems and new computing frameworks that are used to build and deploy intelligent integrated systems. This requires expert knowledge and large-scale evaluations in various subdomains that include but are not limited to advanced instrumentation and data acquisition systems, advanced mathematical methods for data acquisition and computing systems, bio-informatics, computational intelligence for instrumentation and data acquisition systems, computer systems for healthcare and medicine, data analysis and modeling, embedded systems, intelligent distributed systems and remote control, intelligent information systems, data mining and ontology, intelligent software systems and tools, intelligent instrumentation and data acquisition systems in advanced manufacturing for Industry 4.0, Big Data, the Internet of Things, pattern recognition, digital image and signal processing, virtual instrumentation systems, 5G network technologies and security, advanced automatic control and information technology, cybersecurity, advanced computer architectures and embedded systems, designing and testing advanced computer systems, human–computer interaction, intelligent robotics and sensors, machine learning, smart building and smart city systems, wireless systems, virtual and augmented reality, cyber-physical systems, and IoT dependability and resilience.

Contributions will strengthen the scientific profile of the Sensors journal, considering the growing role of advanced algorithms in the design and validation of intelligent sensor systems. Emphasis is on experimental laboratory systems and meaningful real-world applications with extensive evaluation for replicable outcomes.

The authors of the selected papers presented at the 13th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2025) https://idaacs.net/2025 are invited to submit significantly revised and extended versions of their work to this Special Issue. Contributions from other researchers also working in this area of critical interest are welcome to submit.

Prof. Dr. Grigore Stamatescu
Prof. Dr. Anatoliy Sachenko
Dr. Serhii Vladov
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • intelligent sensors
  • data acquisition
  • advanced computing systems
  • information processing
  • wireless sensor networks
  • internet of things

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 2983 KB  
Article
Charge Air System in an Experimental Combustion Engine—Combined Simulation Model: A Digital Twin Approach Including Advanced Control Concepts
by Miki Sirola, Jaber McBreen and Mohammad Raisi Esfarjani
Sensors 2026, 26(12), 3854; https://doi.org/10.3390/s26123854 - 17 Jun 2026
Viewed by 463
Abstract
The larger research problem is to get combustion engines more effective and flexible and reduce or even eliminate greenhouse gas emissions. Here we concentrate more on a smaller-scale and focused research problem about the significance of air feeding in engine operation. Therefore, the [...] Read more.
The larger research problem is to get combustion engines more effective and flexible and reduce or even eliminate greenhouse gas emissions. Here we concentrate more on a smaller-scale and focused research problem about the significance of air feeding in engine operation. Therefore, the need for modeling a charge air system is obvious. The interaction and co-operation between the charge air systems and combustion engines is a central issue in this article. A literature review was carried out on related topics, and it reveals a research gap in this area. A simulation model of a charge air system based on first principles is developed. It is based on physical and systemic modeling, and it is constructed including control loops reducing and controlling the pressures in the charge air chain. The simulation models of this auxiliary system and engine are successfully combined, and functioning together is demonstrated. The composed models represent real research laboratory equipment in the University of Vaasa Energy Laboratory under construction. The research laboratory equipment and the whole research environment are described. Simulation scenarios are presented both with the charge air system alone and with the combined model, including also the engine part. The significance of the developed models is discussed, and the path towards a digital twin experiment environment is outlined. As a conclusion, we can claim that the combined simulation model is successfully constructed and shown to operate in a stable and physically plausible manner. The digital twin concept can be tested completely only when the research laboratory is constructed and ready and the test runs begin to produce measurement data for the digital part. Then also the simulation models can be tuned to a better accuracy level, and the operation as a digital twin will be verified. Full article
(This article belongs to the Special Issue Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition)
►▼ Show Figures

Figure 1

22 pages, 361 KB  
Article
An Integrated Testbed for MITRE-Mapped Attack Emulation in Industrial Control Networks
by Jaafer Rahmani, Kai Oliver Detken and Axel Sikora
Sensors 2026, 26(11), 3514; https://doi.org/10.3390/s26113514 - 2 Jun 2026
Cited by 1 | Viewed by 480
Abstract
Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control [...] Read more.
Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article
(This article belongs to the Special Issue Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition)
►▼ Show Figures

Figure 1

13 pages, 1071 KB  
Article
Contactless Respiratory Waveform Estimation Using a Depth Camera and AI-Based Body Detection
by Yuto Kojima, Toru Higaki, Hirotaka Inoue, Bisser Raytchev, Yanlei Gu and Yuko Nakamura
Sensors 2026, 26(11), 3476; https://doi.org/10.3390/s26113476 - 1 Jun 2026
Viewed by 415
Abstract
Computed tomography (CT) examinations pose challenges for continuous patient observation, particularly when adverse events such as severe reactions to contrast media occur. To improve patient monitoring in such situations, this preliminary study proposes a contactless method for respiratory waveform estimation using a depth [...] Read more.
Computed tomography (CT) examinations pose challenges for continuous patient observation, particularly when adverse events such as severe reactions to contrast media occur. To improve patient monitoring in such situations, this preliminary study proposes a contactless method for respiratory waveform estimation using a depth camera and AI-based body detection. The method identifies anatomically relevant respiratory regions and extracts depth-based motion signals while subjects are seated facing the camera, which is positioned approximately 2 m away. Performance was evaluated experimentally using a wearable force-sensor respiration belt as the reference. Quantitative assessment was conducted using waveform error metrics, Pearson correlation coefficients, respiratory-rate agreement, and Bland–Altman analysis, while qualitative analysis was used to examine the influence of clothing conditions on measurement performance. The results show that the proposed method can provide stable respiratory waveform estimation, with the chest region yielding the lowest waveform error and the highest correlation among the evaluated ROIs. Bland–Altman analysis further indicated small systematic errors in respiratory-rate estimation, although variability-related indices were affected by ROI selection and clothing conditions. These findings support the feasibility of the proposed approach for contactless respiratory monitoring during CT examinations and indicate that the main contribution of this study is to clarify the importance of anatomical ROI selection for robust waveform extraction under CT-oriented monitoring conditions. Full article
(This article belongs to the Special Issue Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition)
►▼ Show Figures

Figure 1

12 pages, 304 KB  
Article
Self-Organizing Neural Grove for Malware Detection in IoT Edge Devices
by Hirotaka Inoue, Tsukasa Komura and Ibuki Hashimoto
Sensors 2026, 26(11), 3399; https://doi.org/10.3390/s26113399 - 27 May 2026
Viewed by 507
Abstract
Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated exceptional classification performance across various practical applications. However, their training time scales significantly with network depth, rendering them suboptimal for resource-constrained environments. As a practical alternative, multiple classifier systems (MCSs) based on self-generating [...] Read more.
Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated exceptional classification performance across various practical applications. However, their training time scales significantly with network depth, rendering them suboptimal for resource-constrained environments. As a practical alternative, multiple classifier systems (MCSs) based on self-generating neural trees provide faster training and lower computational overhead. In this study, we propose the Self-Organizing Neural Grove (SONG), an ensemble learning model featuring a novel pruning technique designed to optimize classification efficiency. We evaluate SONG’s performance on a Raspberry Pi 3, a standard edge computing platform. Through comparative experiments against an unpruned MCS, a C4.5-based MCS, and the k-nearest neighbors (k-NN) algorithm, we demonstrate that SONG achieves superior classification accuracy while substantially reducing both computation time and memory footprint. These advantages are consistent across benchmark datasets and real-world cybersecurity tasks, underscoring the high suitability of SONG for edge computing applications. Full article
(This article belongs to the Special Issue Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition)
►▼ Show Figures

Figure 1

18 pages, 400 KB  
Article
An Operational Hybrid SIEM Framework for OT Anomaly Detection
by Jaafer Rahmani, Salva Daneshgadeh Çakmakçı, Kai Oliver Detken and Axel Sikora
Sensors 2026, 26(10), 3155; https://doi.org/10.3390/s26103155 - 16 May 2026
Cited by 1 | Viewed by 565
Abstract
Security monitoring in Industrial Internet of Things environments requires telemetry that spans Information Technology (IT) and Operational Technology (OT) network layers, and most public datasets capture only one such view. We describe a design pattern for hybrid Security Information and Event Management (SIEM) [...] Read more.
Security monitoring in Industrial Internet of Things environments requires telemetry that spans Information Technology (IT) and Operational Technology (OT) network layers, and most public datasets capture only one such view. We describe a design pattern for hybrid Security Information and Event Management (SIEM) deployments in OT environments (rule-based detection plus edge-deployed machine learning anomaly detection writing into a shared index) and validate it on a Modbus/Jetson/Elastic instance. The pattern is platform-independent: any rule engine that exposes a query language and any edge device with adequate memory headroom can host an instance, and the paper documents the architectural choices that make this portability concrete. The validated instance comprises 27 rules in Kibana Query Language mapped to MITRE Adversarial Tactics, Techniques, and Common Knowledge, plus a CNN-BiLSTM autoencoder on a Jetson Orin Nano that reaches a true positive rate of 1.000 at the 98th-percentile validation threshold and 0.997 at the 99.5th-percentile threshold on a 9997-flow held-out attack partition. Runtime behaviour on the edge hardware is characterised under steady state and adversarial burst, including the queue-wait regime that dominates tail latency. A self-contained calibration step projects rule and model evidence onto a common scale for downstream fusion. Full article
(This article belongs to the Special Issue Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition)
►▼ Show Figures

Figure 1

21 pages, 2519 KB  
Article
PyAO: PyTorch-Based Memory-Efficient LLM Training on Ethernet-Interconnected Clusters
by Daemin Kim, Hyorim Kim, Juncheol Ahn and Sejin Park
Sensors 2026, 26(7), 2269; https://doi.org/10.3390/s26072269 - 7 Apr 2026
Viewed by 959
Abstract
As large language models (LLMs) pursue higher accuracy, their model sizes have surged, substantially increasing GPU memory consumption. Prior work mitigates this issue by distributing the memory burden across multiple GPUs. However, on clusters interconnected via Ethernet, the resulting computational intensity is insufficient [...] Read more.
As large language models (LLMs) pursue higher accuracy, their model sizes have surged, substantially increasing GPU memory consumption. Prior work mitigates this issue by distributing the memory burden across multiple GPUs. However, on clusters interconnected via Ethernet, the resulting computational intensity is insufficient to hide the significant network latency. Achieving a favorable compute-to-communication ratio is further constrained by the memory required to cache the massive activations generated during the forward pass. PyAO, proposed in this paper, effectively offloads activations, selects offloading strategies based on their offloading efficiency, and minimizes data-movement bottlenecks, thereby enabling larger micro-batch sizes. In Ethernet-interconnected cluster environments, experiments on popular models—including OPT-1.3B, GPT-0.8B, and Llama-1.2B—demonstrate that PyAO reduces peak GPU memory by up to 1.94× at the same micro-batch size, enables up to 2.5× larger batch sizes, and accelerates training by up to 3.63× relative to the baseline. Full article
(This article belongs to the Special Issue Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop