Accurate Anomaly Detection using various Machine Learning methods for IoT devices in Indoor Environment

The Internet of Things i.e. IoT is a collection of specialized devices, or "things" that communicate real-world data through networks. IoT devices are computer devices that can exchange data and connect wirelessly to a network. IPS is a technology that can be used to locate people or items inside buildings using a mobile device such as a smartphone or tablet. Indoor positioning is achieved by using devices already in use such as cellphones, Wi-Fi and Bluetooth antennae, digital cameras, and clocks. RSSI is a metric for assessing the quality of a wireless connection in Wi-Fi devices. But using RSSI values of Wi-Fi may cause inaccuracies in locating any nodes. RSSIis far more prone to interference than a wired network. And these interferences cause the irregularity in Signal strengths. In the case of indoor Wi-Fi positioning, irregular and anomalous RSS data can't be used to pinpoint the location of any unknown node. As a result, this article investigates the RSSI values utilizing machine learning approaches such as supervised, unsupervise. and ensemble learning in an indoor localization scenario employing Wi-Fi devices. The supervised learning techniques utilized in this study include Naive Bayes, Random Forest, and Decision Tree. The SOM (Self-Organizing Map) method was utilized in unsupervisedlearning. Then, this research work has used stacking method that is used for combining different classifiers implemented for ensemble algorithm. Precision, recall, and fl-score are utilized for experiments. After removing the outliers, the technique was found to be successful, with a high accuracy of 98.2 percent.