IoT Based on Environmental Monitoring and Data Analytic Systems using the Naive Bayes Algorithm and Exponential Smoothing

Abstract

Machine-to-Machine communication is a subset of the Internet of Things in which Real-Time Sensor Data is captured and monitored from one machine to another within a certain distance. The Internet of Things is the concept that enables Machine-to-Machine Communication, Machine-to-Human Communication, and Human-to-Machine Communication for data transmission. The detrimental impact of carbon monoxide on our ecosystem needs to be studied. There are occasions when prolonged exposure to flammable gas from our heaters and generators within the house causes specific health problems. Using two ZigBee protocols and an Arduino Uno Atmega328p microcontroller programmed with embedded C to detect real-time air quality within the selected locations, a Wireless Sensor Network (WSN) was created for the real-time monitoring of generator emissions. The MQ7 sensor could detect LPG (Liquefied Petroleum Gas) and carbon monoxide concentrations between 20ppm and 2000ppm (Part per million). While the data is being collected through the base station via communication connection utilizing TTL-UART configured ZigBee linked with the distant node, a Sensor node is positioned close to a machine generating gas. The acquired data was sent to the Internet of Things for further analysis and prediction. The outcomes demonstrate the level of the environment's air quality index during deployment. To forecast the kind of gas that was released in real-time, Naive Bayes and Exponential Smoothing Algorithms were utilized.