Application of Machine Learning in Air Purification for Sustainable Environment
In the present times we are living in a tech-driven and highly industrialized environment. Every single day we interact with the machines to perform any task. It is obvious that this mechanization and industrialization have an adverse impact on our surrounding environment. Therefore, monitoring the Air Quality (AQ) is the major concern in the environment where we work and breath to survive. The air is the combination of various gases that are present in a fix ratio to compose the pure air. However, any disbalance in the ratio of gases may cause to excessive harm to the life of living beings on the Earth The aim of the proposed work is to generate a hybrid model for Effective Air Purification System with Internet of Things (IoT) enabled Sensor Reporting on Web Portal, helping live data analytics & report on AQ operating area and to use machine learning algorithm (Linear Regression) to get the trend of Sensor parameters that will be recorded using sensor that is remotely controlled. The objective is to implement remote power control mechanism to start and off the sensing device and thereby applying Machine Learning (Linear Regression) on cloud portal for getting trend of parameters collected by sensing device. This real-time data is used to estimate the time to purify the air. Proposed objective is achieved by utilizing Wi-Fi enabled microcontroller ESP-8266 NodeMCU, interfaced with humidity sensor DHT-11, and MQ135 for monitor the air quality parameters. 16X2 LCD Module is used to display AQ Parameters. The proposed method uses indigenous cloud of Froyo Integrated Solutions to host and analysis industrial IoT Sensors, hosted on GoDaddy.