Distributed Decomposed Data Analytics of IoT, SAR and Social Network data

The weather nowadays has become so unpredictable that there is a need for a system that predicts correct weather patterns and allows them to take a precautionary plan of action to cope up with it. Three major sources of providing very crucial weather data are IoT sensors, SAR data and social media posts from a particular location. In this paper, we are proposing a system that takes IoT sensor data, SAR images and twitter feeds from a geographical location and creates a learning model that will provide a decision-making system for anomaly detection in order to minimize or nullify any casualties. A temperature, pressure and humidity dataset of BME 280 sensor is processed with K-NN Classifiers and the results are presented here.