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Cited by 7Anomaly Detection Techniques and ApplicationsFault Detection and Control SystemsNetwork Security and Intrusion Detection

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Detection and Classification of Anomalies in WSN-Enabled Cyber-Physical Systems

Open access
Gutierrez-Rojas, Daniel, Kalalas, Charalampos, Christou, Ioannis, Almeida, Gustavo, Eldeeb, Eslam, Bakri, Sihem, Marchetti, Nicola, Sant'Ana, Jean M. S., Alcaraz López, Onel L., Alves, Hirley, Papadias, Constantinos, Haroon Tariq, Muhammad, Nardelli, Pedro H. J.·2025·IEEE Sensors Journal
DOI 10.1109/jsen.2024.3520507
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Abstract

Detection and classification of anomalies in industrial applications has long been a focus of interest in the research community. The integration of computational and physical systems has increased the complexity of interactions between processes, leading to vulnerabilities in both the physical and cyber layers. This work presents a model structure for anomaly detection in the Internet of Things (IoT)-enabled industrial cyber-physical systems (CPSs), enabled by wireless sensor networks (WSNs). The model comprises three primary data blocks in the cyber layer: sensor-based data acquisition, data fusion to convert raw data into useful information, and analytics for decision-making. The rationale behind these blocks highlights the critical role of anomaly detection and is demonstrated through three use cases, namely fault selection in power grids, anomaly detection in an industrial chemical process, and prediction of the CO2 level in a room. Furthermore, we integrate explainable AI (XAI) algorithms into an IoT-based system to enhance error detection and correction, while fostering user engagement by offering useful insights into the decision-making process. Our numerical results demonstrate high accuracy in anomaly detection across these scenarios, significantly improving system reliability and enabling timely interventions, which could ultimately reduce operational risks.

Overview

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Registry

Crossref · 10.1109/jsen.2024.3520507

Cites

35 of 35 references verified0 citation errors · 0 not checkable

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Citations

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Show all 35 references

Research works printed but not linked: 8

  1. Lundberg, A unified approach to interpreting model predictions, 4768
  2. Srebro, Weighted low-rank approximations, 720
  3. Yu, Temporal regularized matrix factorization for high-dimensional time series prediction, 847
  4. Linderman, Bayesian learning and inference in recurrent switching linear dynamical systems, 914
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Metadata

Authors
Gutierrez-Rojas, D., Kalalas, C., Christou, I., Almeida, G., Eldeeb, E., Bakri, S., Marchetti, N., Sant'Ana, J. M. S., Alcaraz López, O. L., Alves, H., Papadias, C., Haroon Tariq, M., & Nardelli, P. H. J.
Affiliations
  • Department of Electrical Engineering, Lappeenranta–Lahti University of Technology, Lappeenranta, Finland
  • Centre Tecnologic de Telecomunicacions de Catalunya (ES)
  • The American College of Greece (GR)
  • Lappeenranta-Lahti University of Technology (FI)
  • University of Oulu (FI)
  • Trinity College Dublin (IE)
  • Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
  • American College of Greece (ACG), Athens, Greece
ORCID
Venue
IEEE Sensors Journal
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2025-02-15
Volume
25
Issue
4
Pages
7193-7204
ISSN
  • 1530-437X (ISSN-L)
  • 1558-1748
  • 2379-9153
DOI
10.1109/jsen.2024.3520507
OpenAlex
W4405907276
Cited by
7
References
35
Citations by year
2026: 4 · 2025: 3
FWCI
1.04
Type
journal article
Language
en
Open access
Licence
Locations
IEEE Sensors Journal (publishedVersion, cc-by)
Funders
  • HORIZON EUROPE Marie Sklodowska-Curie Actions (101130739)
  • Irish Research Council for the Humanities and Social Sciences (13/RC/2077_P2)
  • Research Council of Finland (321265, 328869, 349965, 352654, 358928)
  • CHIST-ERA
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
anomaly detection, anomaly classification, cyber-physical systems, wireless sensor networks, Internet of Things, Industrial Internet of Things, data fusion, explainable AI, power system fault detection

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CoChat. (2026). Citation check: Detection and Classification of Anomalies in WSN-Enabled Cyber-Physical Systems. https://app.cochat.ai/papers/10.1109/jsen.2024.3520507
The paper
Gutierrez-Rojas, D., Kalalas, C., Christou, I., Almeida, G., Eldeeb, E., Bakri, S., Marchetti, N., Sant'Ana, J. M. S., Alcaraz López, O. L., Alves, H., Papadias, C., Haroon Tariq, M., & Nardelli, P. H. J. (2025). Detection and Classification of Anomalies in WSN-Enabled Cyber-Physical Systems. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2024.3520507