IoT-based CO2 Gas-level Monitoring and Automated Decision-making System in Smart Factory using UAV-assisted MEC

Abstract

Monitoring the CO2 gas level in a smart factory is essential as the high levels of CO2 gas negatively affect the human body, causing various physical problems. This paper presents an Internet of Things (IoT) based CO2 gas level monitoring and automated decision-making system inside a smart factory using the unmanned aerial vehicle (UAV) and multi-access edge computing (MEC) technique. Firstly, different IoT device is used to continuously monitor and detect the CO2 gas level data using gas sensors. Due to the drawback of sink node failure and the centralized data collection technique of wireless sensor networks, a UAV-based continuous CO2 gas level monitoring approach has been introduced in this study. Moreover, the MEC-enabled data processing technique is utilized by offloading the sensor data from the UAV considering its limited battery capacity and low processing power. Finally, a blockchain-based secure decision-making system is designed to evacuate the smart factory premises by alerting all employees in an emergency case of an excessive level of CO2 gas existence. Result analysis shows that the IoT devices can successfully monitor and detect the CO2 gas level in the smart factory using the UAV. Furthermore, the UAV can securely offload sensor data to the MEC server to analyze and make an automated decision to alert all employees in a smart factory to evacuate if CO2 levels are too high.