Exploring K-Means Meta-Heuristic Techniques For Prediction of Anomalies In IoT-Enabled Industrial Systems
T. Gnanasekaran, S. Girinath, K Venkatesh, N. Valarmathi, Sunil Kumar Bandili, S. Balasubramani · 2024
Due to the broad implementation of the Internet of Things (IoT) in industrial systems, the introduction of Industry 4.0 resulted in a significant increase in operational efficiency. However, it also resulted in the introduction of new challenges when it came to identifying abnormalities. This paper presents a new method for boosting the flexibility and precision of anomaly detection models by integrating the best aspects of K-Means clustering with meta-heuristic approaches. The utilisation of K-Means meta-heuristic techniques is the driving force behind this research, which is being driven by the growing demand for reliable anomaly prediction in industrial systems that are enabled by the IoT. Aiming to discover methods that can improve the reliability of anomaly detection algorithms is the objective of this research. K-Means clustering is systematically integrated with metaheuristic techniques in this study, which allows for the optimisation of the clustering algorithm for the purpose of detecting abnormalities in industrial data that is enabled by the IoT. We use real-world datasets from a wide range of businesses in order to measure the effectiveness of the proposed strategy in comparison to more traditional ways. The results demonstrate that the K-Means meta-heuristic strategy greatly enhances the accuracy of anomaly detection in comparison to the approaches that are considered conventional. Recall, false positive rates, and precision are all areas in which it excels beyond its capabilities. The results indicate that this unique approach has the potential to be useful when applied to the challenges of anomaly detection in industrial systems that are enabled by the IoT.