Machine learning for Anomaly detection in sensor networks

Identification of unusual patterns in the sensor network plays an important role in maintaining the dependability of the system and protecting it from safety threats in various applications, including the Internet of Things devices, factory monitoring systems, and urban technology infrastructure. This research examines the methods of computational learning to identify irregular events in data collected from sensor networks, addressing challenges such as complex multidimensional data, intervention signs, and real-time processing capabilities. The study presents a joint computational structure that merges both unexpected and supervised learning approaches to improve the accuracy of identity and system expandability. Without the need for pre-classified information, pre-liable examples are not required to understand specific sensor operations and spot irregularities, including neural networks and data grouping algorithms, including a neural network encoder and data grouping algorithms. These approaches successfully identify the duration in sensor measurements and patterns in geographical locations. When pre- versatile data is available, using the characteristics obtained from sequential data analysis, decision to classify irregularities with high accuracy, includes supervised learning models such as Decision Tree Ensemble and Busting algorithms such as decision trees and boosting algorithms such as supervised learning models.