{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:52:10Z","timestamp":1778169130396,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,10]],"date-time":"2025-08-10T00:00:00Z","timestamp":1754784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia, I.P.","award":["UID\/00127"],"award-info":[{"award-number":["UID\/00127"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) increasingly demand robust onboard diagnostic frameworks to ensure safe operation under irregular telemetry and mission-critical conditions. This paper presents a real-time fault detection framework for unmanned aerial vehicles (UAVs), optimized for deployment on edge devices and designed to handle irregular, nonuniform telemetry. The system reconstructs raw sensor data using compactly supported B-spline interpolation, ensuring stable recovery of flight dynamics under jitter, dropouts, and asynchronous sampling. A lightweight hybrid anomaly detection module\u2014combining a Long Short-Term Memory (LSTM) autoencoder with an Isolation Forest\u2014analyzes both temporal patterns and statistical deviations across reconstructed signals. The full pipeline operates entirely onboard embedded platforms such as the Raspberry Pi 4 and NVIDIA Jetson Nano, with end-to-end inference latency under 50 milliseconds. Experiments using real PX4 UAV flight logs and synthetic fault injection demonstrate a detection accuracy of 93.6% and strong resilience to telemetry disruptions. These results support the feasibility of autonomous, sensor-based health monitoring in UAV systems and broader real-time cyber\u2013physical applications.<\/jats:p>","DOI":"10.3390\/s25164944","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:59:13Z","timestamp":1754906353000},"page":"4944","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Edge-Based Real-Time Fault Detection in UAV Systems via B-Spline Telemetry Reconstruction and Lightweight Hybrid AI"],"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\/IEETA, Quinta de Prados, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7139-5842","authenticated-orcid":false,"given":"Ant\u00f3nio J. D.","family":"Reis","sequence":"additional","affiliation":[{"name":"Escola de Engenharia, Campus de Azur\u00e9m, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-013-0120-x","article-title":"UAV for 3D mapping applications: A review","volume":"6","author":"Nex","year":"2014","journal-title":"Appl. Geomat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.3390\/smartcities6040096","article-title":"Pseudolites to Support Location Services in Smart Cities: Review and Prospects","volume":"6","author":"Liu","year":"2023","journal-title":"Smart Cities"},{"key":"ref_3","unstructured":"Cabahug, J. (2022). Autonomous UAV Health Monitoring and Failure Detection Based on Vibration Signals. [Master\u2019s Thesis, Southern Illinois University Carbondale]."},{"key":"ref_4","first-page":"1081","article-title":"Health Monitoring and Failure Detection of Electronic and Structural Components in Small Unmanned Aerial Vehicles","volume":"11","author":"Kandaswamy","year":"2017","journal-title":"World Acad. Sci. Eng. Technol. Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng."},{"key":"ref_5","unstructured":"Fahlstrom, P.G., and Gleason, T.J. (2020). Introduction to UAV Systems, Wiley. [5th ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Beard, R.W., and McLain, T.W. (2012). Small Unmanned Aircraft: Theory and Practice, Princeton University Press.","DOI":"10.1515\/9781400840601"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.clsr.2014.03.007","article-title":"The Regulation of Civilian Drones\u2019 Impacts on Public Safety","volume":"30","author":"Clarke","year":"2014","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Young, S.D., Quach, C., Goebel, K., and Nowinski, J. (2018, January 23\u201327). In-Time Safety Assurance Systems for Emerging Autonomous Flight Operations. Proceedings of the 2018 IEEE\/AIAA 37th Digital Avionics Systems Conference (DASC), London, UK.","DOI":"10.1109\/DASC.2018.8569689"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Du, S., Zhong, G., Wang, F., Pang, B., Zhang, H., and Jiao, Q. (2024). Safety Risk Modelling and Assessment of Civil Unmanned Aircraft System Operations: A Comprehensive Review. Drones, 8.","DOI":"10.3390\/drones8080354"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1145\/3723871","article-title":"UAV Operations Safety Assessment: A Systematic Literature Review","volume":"57","author":"Asghari","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Feng, O., Zhang, H., Tang, W., Wang, F., Feng, D., and Zhong, G. (2025). Digital Low-Altitude Airspace Unmanned Aerial Vehicle Path Planning and Operational Capacity Assessment in Urban Risk Environments. Drones, 9.","DOI":"10.3390\/drones9050320"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yin, Y., Wang, Z., Zheng, L., Su, Q., and Guo, Y. (2024). Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning. Electronics, 13.","DOI":"10.3390\/electronics13132432"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chandran, I., and Vipin, K. (2024, January 21\u201322). Comparative Analysis of Stand-alone and Hybrid Multi-UAV Network Architectures for Disaster Response Missions. Proceedings of the 2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI), Kannur, India.","DOI":"10.1109\/APCI61480.2024.10616974"},{"key":"ref_14","first-page":"749","article-title":"The Ethical Assessment of Autonomous Systems in Practice","volume":"4","author":"Trusilo","year":"2021","journal-title":"J"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Li, X., Zhu, G., Li, H., Deng, J., Han, K., Shen, C., Shi, Q., and Zhang, R. (2025). Integrated Sensing and Communication for Low Altitude Economy: Opportunities and Challenges. IEEE Commun. Mag., 1\u20137.","DOI":"10.1109\/MCOM.001.2400685"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s12555-010-0105-z","article-title":"Autopilots for small UAVs: A survey","volume":"8","author":"Chao","year":"2010","journal-title":"Int. J. Control. Autom. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Q., Qi, Z., Wang, S., and Liu, Q. (2024). Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy. Drones, 8.","DOI":"10.3390\/drones8090485"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, J., Cheng, Z., and Guo, B. (2022). Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method. Sensors, 22.","DOI":"10.3390\/s22176358"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107961","DOI":"10.1016\/j.engappai.2024.107961","article-title":"Unmanned Aerial Vehicles anomaly detection model based on sensor information fusion and hybrid multimodal neural network","volume":"132","author":"Deng","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, G., Ai, J., Mo, L., Yi, X., Wu, P., Wu, X., and Kong, L. (2023). Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism. Drones, 7.","DOI":"10.3390\/drones7050326"},{"key":"ref_21","unstructured":"Malhotra, P., Vig, L., Shroff, G., and Agarwal, P. (2016). LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv."},{"key":"ref_22","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15\u201319). Isolation Forest. Proceedings of the IEEE International Conference on Data Mining (ICDM), Pisa, Italy."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10846-024-02188-y","article-title":"Human Factors and AI in UAV Systems: Enhancing Operational Efficiency Through AHP and Real-Time Physiological Monitoring","volume":"111","author":"Alharasees","year":"2024","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.arcontrol.2004.12.002","article-title":"Model-based fault detection and diagnosis\u2014Status and applications","volume":"29","author":"Isermann","year":"2005","journal-title":"Annu. Rev. Control"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1007\/s00170-020-06168-y","article-title":"Real-time fault detection and process control based on multi-channel sensor data fusion","volume":"115","author":"Xia","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s44163-024-00209-1","article-title":"In-depth review of AI-enabled unmanned aerial vehicles: Trends, vision, and challenges","volume":"4","author":"Pal","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_27","unstructured":"Abshari, D., and Sridhar, M. (2025). A Survey of Anomaly Detection in Cyber-Physical Systems. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/MAES.2023.3318226","article-title":"Cybersecurity of Unmanned Aerial Vehicles: A Survey","volume":"39","author":"Yu","year":"2024","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Toma, C., Popa, M., Iancu, B., Doinea, M., Pascu, A., and Ioan-Dutescu, F. (2022). Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones. Electronics, 11.","DOI":"10.3390\/electronics11213507"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/3701997","article-title":"MemoriaNova: Optimizing Memory-Aware Model Inference for Edge Computing","volume":"22","author":"Zhang","year":"2025","journal-title":"ACM Trans. Archit. Code Optim."},{"key":"ref_31","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_32","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_33","doi-asserted-by":"crossref","unstructured":"Puder, A., Zink, M., Seidel, L., and Sax, E. (2024). Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models. Sensors, 24.","DOI":"10.3390\/s24092895"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Oyelade, J., Isewon, I., Oladipupo, O., Emebo, O., Omogbadegun, Z., Aromolaran, O., Uwoghiren, E., Olaniyan, D., and Olawole, O. (2019, January 1\u20134). Data Clustering: Algorithms and Its Applications. Proceedings of the 2019 19th International Conference on Computational Science and Its Applications (ICCSA), St. Petersburg, Russia.","DOI":"10.1109\/ICCSA.2019.000-1"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_36","first-page":"4567","article-title":"Deploying Lightweight CNNs for UAV-Based Surveillance: A TinyML Perspective","volume":"24","author":"Alzahrani","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.1109\/LCOMM.2021.3095362","article-title":"Fully Decentralized Federated Learning-Based On-Board Mission for UAV Swarm System","volume":"25","author":"Xiao","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/MNET.001.2100253","article-title":"Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities","volume":"35","author":"Qu","year":"2021","journal-title":"IEEE Netw."},{"key":"ref_39","unstructured":"Fallah, A., Mokhtari, A., and Ozdaglar, A. (2020, January 6\u201312). Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sakurada, M., and Yairi, T. (2014, January 2). Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction. Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis, Gold Coast, QLD, Australia.","DOI":"10.1145\/2689746.2689747"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vancin, P.H., Garibotti, R.F., Ost, L.C., Calazans, N.L.V., and Moraes, F.G. (2024, January 18\u201320). Trade Offs between Energy Consumption and Control Quality in Unmanned Aerial Vehicles. Proceedings of the 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS), Nancy, France.","DOI":"10.1109\/ICECS61496.2024.10849212"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, H., Lyu, Y., Shi, J., and Zhang, W. (2024). UAV Anomaly Detection Method Based on Convolutional Autoencoder and Support Vector Data Description with 0\/1 Soft-Margin Loss. Drones, 8.","DOI":"10.3390\/drones8100534"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"10","DOI":"10.20517\/ces.2024.13","article-title":"Health status assessment of unmanned aerial vehicle (UAV) attitude control system based on an improved multivariate state estimation method","volume":"4","author":"Yuan","year":"2024","journal-title":"Complex Eng. Syst."},{"key":"ref_44","first-page":"475","article-title":"Machine Learning-Based GPS Spoofing Detection in UAV Networks: A Comparative Analysis of Anomaly Detection Models","volume":"6","author":"Airlangga","year":"2025","journal-title":"J. Comput. Syst. Inform. (JoSYC)"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Alotaibi, T., Jambi, K., Khemakhem, M., Eassa, F., and Bourennani, F. (2025). Deep Learning-Based Autonomous Navigation of 5G Drones in Unknown and Dynamic Environments. Drones, 9.","DOI":"10.3390\/drones9040249"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/79.799930","article-title":"Splines: A perfect fit for signal and image processing","volume":"16","author":"Unser","year":"1999","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_47","unstructured":"Haykin, S.S., and Haykin, S.S. (2009). Neural Networks and Learning Machines, Prentice Hall. [3rd ed.]."},{"key":"ref_48","unstructured":"Reddy, C.K., and Aggarwal, C.C. (2015). Data Clustering: Algorithms and Applications, CRC Press."},{"key":"ref_49","unstructured":"Brescia, G.F. (2025, August 06). Signal Processing in Embedded Systems. Available online: https:\/\/www.embedded.com\/signal-processing-in-embedded-systems\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/16\/4944\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:27:42Z","timestamp":1760034462000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/16\/4944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,10]]},"references-count":49,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["s25164944"],"URL":"https:\/\/doi.org\/10.3390\/s25164944","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,10]]}}}