{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T07:55:12Z","timestamp":1778054112007,"version":"3.51.4"},"reference-count":22,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The proliferation of IoT devices has created vast sensor networks that generate continuous time-series data. Efficient and real-time processing of these signals is crucial for applications such as predictive maintenance, healthcare monitoring, and environmental sensing. This paper proposes a lightweight framework that combines classical signal processing techniques (Fourier and Wavelet-based feature extraction) with edge-deployed machine learning models for anomaly detection. By performing feature extraction and classification locally, the approach reduces communication overhead, minimizes latency, and improves energy efficiency in IoT nodes. Experiments with synthetic vibration, acoustic, and environmental datasets showed that the proposed Shallow Neural Network achieved the highest detection performance (F1-score \u2248 0.94), while a Quantized TinyML model offered a favorable trade-off (F1-score \u2248 0.92) with a 3\u00d7 reduction in memory footprint and 60% lower energy consumption. Decision Trees remained competitive for ultra-constrained devices, providing sub-millisecond latency with limited recall. Additional analyses confirmed robustness against noise, missing data, and variations in anomaly characteristics, while ablation studies highlighted the contributions of each pipeline component. These results demonstrate the feasibility of accurate, resource-efficient anomaly detection at the edge, paving the way for practical deployment in large-scale IoT sensor networks.<\/jats:p>","DOI":"10.3390\/s25216629","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T05:48:46Z","timestamp":1761716926000},"page":"6629","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8872-5721","authenticated-orcid":false,"given":"Manuel J. C. S.","family":"Reis","sequence":"first","affiliation":[{"name":"Engineering Departement and IEETA, University of Tr\u00e1s-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100568","DOI":"10.1016\/j.iot.2022.100568","article-title":"IoT anomaly detection methods and applications: A survey","volume":"19","author":"Chatterjee","year":"2022","journal-title":"Inter. Things"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abdulhussain, S.H., Mahmmod, B.M., Alwhelat, A., Shehada, D., Shihab, Z.I., Mohammed, H.J., Abdulameer, T.H., Alsabah, M., Fadel, M.H., and Ali, S.K. (2025). A Comprehensive Review of Sensor Technologies in IoT: Architecture, Challenges, and Future Directions. Computers, 14.","DOI":"10.3390\/computers14080342"},{"key":"ref_3","unstructured":"Waldhauser, F., Boukabache, H., Perrin, D., and Dazer, M. (September, January 28). Wavelet-based Noise Extraction for Anomaly Detection Applied to Safety-Critical Electronics at CERN. Proceedings of the 32nd European Safety and Reliability Conference, Research Publishing Services, Dublin, Ireland."},{"key":"ref_4","first-page":"1751","article-title":"TinyML-Based Classification in an ECG Monitoring Embedded System","volume":"75","author":"Kim","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Reis, M.J.C.S. (2025). AI-Driven Anomaly Detection for Securing IoT Devices in 5G-Enabled Smart Cities. Electronics, 14.","DOI":"10.3390\/electronics14122492"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dubey, K., Batra, I., Bajpai, A., and Malik, A. (2025, January 11\u201312). A Survey on Anomaly Detection in IoT Networks: From Classical Methods to AI-Powered Solutions. Proceedings of the 2025 Seventh International Conference on Computational Intelligence andCommunication Technologies (CCICT), Sonepat, India.","DOI":"10.1109\/CCICT65753.2025.00062"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.future.2023.09.028","article-title":"EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network","volume":"151","author":"Guo","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103692","DOI":"10.1016\/j.compind.2022.103692","article-title":"Light-weight federated learning-based anomaly detection for time-series data in industrial control systems","volume":"140","author":"Truong","year":"2022","journal-title":"Comput. Ind."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, S., Tong, X., Chi, K., Gao, W., Chen, X., and Shi, Z. (2025). Stackelberg Game-Based Multi-Agent Algorithm for Resource Allocation and Task Offloading in MEC-Enabled C-ITS. IEEE Trans. Intell. Transp. Syst., 1\u201312.","DOI":"10.1109\/TITS.2025.3553487"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhao, Y., Hu, Y., Ma, Y., and Guo, Z. (2025). Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA model. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-89576-y"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zonzini, F., Carbone, A., Romano, F., Zauli, M., and De Marchi, L. (2022). Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring. Sensors, 22.","DOI":"10.3390\/s22062229"},{"key":"ref_12","unstructured":"Forough, J. (2024). Machine Learning for Anomaly Detection in Edge Clouds, Ume\u00e5 University."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dembski, J., Wiszniewski, B., and Ko\u0142akowska, A. (2025). Anomaly Detection and Segmentation in Measurement Signals on Edge Devices Using Artificial Neural Networks. Sensors, 25.","DOI":"10.3390\/s25175526"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103281","DOI":"10.1016\/j.asej.2025.103281","article-title":"Safeguarding IoT consumer devices: Deep learning with TinyML driven real-time anomaly detection for predictive maintenance","volume":"16","author":"Katib","year":"2025","journal-title":"Ain Shams Eng. J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Khatoon, A., Wang, W., Wang, M., Li, L., and Ullah, A. (2025). TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-01981-5"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121608","DOI":"10.1016\/j.eswa.2023.121608","article-title":"Tackling the problem of noisy IoT sensor data in smart agriculture: Regression noise filters for enhanced evapotranspiration prediction","volume":"237","author":"Corchado","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100477","DOI":"10.1016\/j.hitech.2023.100477","article-title":"IoT-based data quality and data preprocessing of multinational corporations","volume":"34","author":"Sirisha","year":"2023","journal-title":"J. High Technol. Manag. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Antonini, M., Pincheira, M., Vecchio, M., and Antonelli, F. (2023). An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments. Sensors, 23.","DOI":"10.3390\/s23042344"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Martinez-Rau, L.S., Zhang, Y., Oelmann, B., and Bader, S. (2024, January 23\u201325). TinyML Anomaly Detection for Industrial Machines with Periodic Duty Cycles. Proceedings of the 2024 IEEE Sensors Applications Symposium (SAS), Naples, Italy.","DOI":"10.1109\/SAS60918.2024.10636584"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"128791","DOI":"10.1016\/j.neucom.2024.128791","article-title":"A review of AI edge devices and lightweight CNN and LLM deployment","volume":"614","author":"Sun","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3629522","article-title":"ULEEN: A Novel Architecture for Ultra-low-energy Edge Neural Networks","volume":"20","author":"Susskind","year":"2023","journal-title":"ACM Trans. Archit. Code Optim."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Elhanashi, A., Dini, P., Saponara, S., and Zheng, Q. (2024). Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices. Electronics, 13.","DOI":"10.3390\/electronics13173562"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/21\/6629\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:29:09Z","timestamp":1761888549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/21\/6629"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,28]]},"references-count":22,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["s25216629"],"URL":"https:\/\/doi.org\/10.3390\/s25216629","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,28]]}}}