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Open AccessArticle
IWOA-LightGBM: Hyperparameter Optimization for Sensor Data Anomaly Detection †
by
Rong Huang
Rong Huang
,
Qiqiang Wu
Qiqiang Wu
Mingwei Yang
Mingwei Yang
Yanhua Liu
Yanhua Liu
Baokang Zhao
Baokang Zhao
1
The Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China
2
College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
3
Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350108, China
4
Zhicheng College, Fuzhou University, Fuzhou 350002, China
5
College of Computer, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
†
A preliminary version of this work was presented at MobiSec2025 and has not been officially published in any conference proceedings or journals.
Information 2026, 17(6), 518; https://doi.org/10.3390/info17060518 (registering DOI)
Submission received: 20 March 2026
/
Revised: 17 April 2026
/
Accepted: 21 May 2026
/
Published: 23 May 2026
Abstract
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%.
Share and Cite
MDPI and ACS Style
Huang, R.; Wu, Q.; Yang, M.; Liu, Y.; Zhao, B.
IWOA-LightGBM: Hyperparameter Optimization for Sensor Data Anomaly Detection. Information 2026, 17, 518.
https://doi.org/10.3390/info17060518
AMA Style
Huang R, Wu Q, Yang M, Liu Y, Zhao B.
IWOA-LightGBM: Hyperparameter Optimization for Sensor Data Anomaly Detection. Information. 2026; 17(6):518.
https://doi.org/10.3390/info17060518
Chicago/Turabian Style
Huang, Rong, Qiqiang Wu, Mingwei Yang, Yanhua Liu, and Baokang Zhao.
2026. "IWOA-LightGBM: Hyperparameter Optimization for Sensor Data Anomaly Detection" Information 17, no. 6: 518.
https://doi.org/10.3390/info17060518
APA Style
Huang, R., Wu, Q., Yang, M., Liu, Y., & Zhao, B.
(2026). IWOA-LightGBM: Hyperparameter Optimization for Sensor Data Anomaly Detection. Information, 17(6), 518.
https://doi.org/10.3390/info17060518
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