{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:05:04Z","timestamp":1780463104229,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Science and Technology Project of Fuzhou","award":["2023-ZD-003"],"award-info":[{"award-number":["2023-ZD-003"]}]},{"name":"Industrial Technology Development and Application Program of Fujian Province","award":["2025H0043"],"award-info":[{"award-number":["2025H0043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Anomaly detection performance in sensor data is highly sensitive to model hyperparameters, which is central to reliable monitoring in mobile Internet security and industrial IoT (IIoT) scenarios. We propose an IWOA-LightGBM-based anomaly detection method for sensor data. For machine learning-based anomaly detection methods, hyperparameter selection often determines model performance, so we propose an Improved Whale Optimization Algorithm (IWOA) and further use it to optimize the hyperparameters of the LightGBM algorithm. To avoid falling into local optima and accelerate algorithm convergence, the WOA is improved by integrating nonlinear convergence factor, adaptive inertia weight factor and stochastic differential mutation strategy. Experimental results show that during hyperparameter optimization for LightGBM model training, the IWOA achieves faster convergence and higher computational efficiency compared to the Whale Optimization Algorithm (WOA), with anomaly detection accuracy exceeding 90%.<\/jats:p>","DOI":"10.3390\/info17060518","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T01:45:08Z","timestamp":1779673508000},"page":"518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IWOA-LightGBM: Hyperparameter Optimization for Sensor Data Anomaly Detection"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3031-5857","authenticated-orcid":false,"given":"Rong","family":"Huang","sequence":"first","affiliation":[{"name":"The Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5641-2791","authenticated-orcid":false,"given":"Qiqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China"},{"name":"Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwei","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China"},{"name":"Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6076-9968","authenticated-orcid":false,"given":"Yanhua","family":"Liu","sequence":"additional","affiliation":[{"name":"The Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China"},{"name":"College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China"},{"name":"Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108, China"},{"name":"Zhicheng College, Fuzhou University, Fuzhou 350002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baokang","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1080\/02564602.2023.2246426","article-title":"Integration of SCADA and industrial IoT: Opportunities and challenges","volume":"41","author":"Nechibvute","year":"2024","journal-title":"IETE Tech. 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