UAV based long range environment monitoring system with Industry 5.0 perspectives for smart city infrastructure

Air quality defines the condition of the air within the environment. However, the quality of the air is deteriorating due to the emission of pollutants from vehicles, industries, and landfill sites. Concerning the landfill scenario, it is a challenge for a human to monitor the air quality continuously due to the emission of stinky smells and gases from the landfill sites. In this study, we implement a UAV network based on IoT (Internet of Things) and a cloud server for the smart city for tracking the air quality of the landfill sites in real-time and alerting the UAV for capturing the visuals from the camera for detecting the exact cause of the pollutant in the landfill sites. The gas that causes the most air pollution is Nitrogen dioxide (NO2), Volatile Organic Compounds (VOC), Carbon monoxide (CO), Sulphur dioxide (SO2), Ozone, dioxins and furans, etc. In this paper, continuous monitoring of these harmful gases is done to alert the people to protect them from harm. The study is also carrying the discussion about the industry 5.0 perspectives using UAV long-range communication. The objective of this study is to record the sensory data and visuals of the various gases (NO2, VOC, CO2, SO2) of landfill sites using IoT & UAV and then visualized in a graphical user interface form. The concentration of each gas that is stored in the cloud server is presented in the graphical representation. Different parameters such as Eigenvalues, Correlation Coefficients, and Regression Parameter have also been analysed graphically for the recorded data. The visuals captured from the UAV camera are employed for estimating the cause of the rise in pollutants in landfill sites.

Automated summary

This study implements a UAV network based on IoT and a cloud server to track the air quality of landfill sites in real-time. It utilizes the visuals captured by UAV cameras to detect the exact cause of pollutants, while continuously monitoring harmful gases. The study also presents graphical representations and analysis of the recorded data.