IoT-Based Harmful Toxic Gases Monitoring and Fault Detection on the Sensor Dataset Using Deep Learning Techniques

Abstract

One of the main reasons for accidents among workers is harmful gas leakage. Many people die in chemical industries and their surrounding areas. The present invention is responsible for monitoring and controlling hazardous toxic gases like nitrogen dioxide (NO2), carbon monoxide, ozone (O3), sulfur dioxide (SO2), LPG, hydrocarbon gases, silicones, hydrocarbons, alcohol, CH4, hexane, benzine, as well as environmental conditions, such as temperature and relative humidity to prevent industrial accidents. The Arduino UNO R3 board is used as the central microcontroller. It is connected to the Cloud via AQ3 sensor, Minipid 2 HS PID sensor, IR5500 open path infrared gas detector, DHT11 Temperature and Humidity Sensor, MQ3 sensor, and ESP8266 and WIFI Module, which can store real-time sensor data and send alert messages to the industry's safety control board. Machine learning and artificial intelligence will be used to make an intelligent prediction (AI). The information gathered will be examined in real-time. The real-time data provided through the sensor can be accessed worldwide. Sensor data quality is critical in the Internet of Things (IoT) applications because poor data quality renders them useless. Error detection in sensor data improves the IoT-based toxic gas monitoring, controlling, and prediction system. Live data from sensors or datasets should be analyzed properly using appropriate techniques. Hence, hybrid hidden Markov and artificial intelligence models are applied as an error detection technique in the sensor dataset. This technique outperformed the dataset gas sensor array under dynamic gas mixtures and lived data. Our method outperformed harmful gas monitoring and error detection in sensor datasets compared to other existing technologies. The hybrid HMM and ANN fault detection methods performed well on the datasets and produced 0.01% false positive rate.