Edge Assisted Framework for Smart Environment Monitoring System using Machine Learning: Comprehensive Review of LiteratureMr. Dewarka Nath

The progressing era of industrialization and urbanization contribute for the poor environment quality. The improper treatment of environment pollutants has resulted into the contamination of basic elements of ecosystem which serve the basis to sustain human life. With the evolving technology of Internet of Things(IOT), sensors and machine learning, it's been possible to build potential and predictive models for monitoring and controlling pollution based on the real-time data. A predictive model is built on the real-time data available and predicting the values of environment pollutants in real time. Sensors are embedded in environment to measure the pollutants levels. By using the smart monitoring devices with AI techniques,  current situation of pollution at the location is sent to users for information in real time. Machine learning model is used for the prediction of pollution level in the environment based on the previous data and the current data obtained by the sensors. The following paper reviews the existing works on smart and intelligent environment pollution monitoring with reference to water pollution using machine learning systems, strikes shortcomings of existing framework and explore the areas of improvement and move towards a better and efficient model which allows data from environment sensors  to be processed locally at the edge, and performs inferencing related to pollutants while ensuring the privacy, security, and low latency in the system.