{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:56:18Z","timestamp":1773806178181,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,17]],"date-time":"2021-01-17T00:00:00Z","timestamp":1610841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002568","name":"University of Ulsan","doi-asserted-by":"publisher","award":["Ulsan"],"award-info":[{"award-number":["Ulsan"]}],"id":[{"id":"10.13039\/501100002568","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensors\u2019 existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over\/bias, spike, erratic\/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.<\/jats:p>","DOI":"10.3390\/s21020617","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T03:34:25Z","timestamp":1611113665000},"page":"617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7666-838X","authenticated-orcid":false,"given":"Umer","family":"Saeed","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3958-6235","authenticated-orcid":false,"given":"Young-Doo","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3950-4719","authenticated-orcid":false,"given":"Sana Ullah","family":"Jan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7476-8782","authenticated-orcid":false,"given":"Insoo","family":"Koo","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1109\/TII.2019.2905295","article-title":"A survey on model-based distributed control and filtering for industrial cyber-physical systems","volume":"15","author":"Ding","year":"2019","journal-title":"IEEE Trans. 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