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Smart city article details
| Title | Internet Of Things-Based Drone Case Study For Atmospheric Data Collection |
|---|---|
| ID_Doc | 33058 |
| Authors | Özer M.M. |
| Year | 2024 |
| Published | Lecture Notes in Networks and Systems, 1138 LNNS |
| DOI | http://dx.doi.org/10.1007/978-3-031-70924-1_38 |
| Abstract | Although ground-based monitoring, manned aircrafts and satellites are used for atmospheric measurements, rapid and comprehensive data collection is not always possible near pollution sources due to the complexity of the areas to be measured, moving sources or physical obstacles. Therefore, drone solutions equipped with different sensors offer new approaches and research opportunities in air pollution and emissions monitoring, as well as on-site air quality monitoring to study atmospheric trends such as climate change. While the potential of unmanned aerial vehicles for air quality research has been identified, several challenges still need to be addressed, including flight durability, payload capacity, sensor consistency/accuracy and sensitivity. In this study, a rotary-wing drone architecture that utilizes artificial intelligence (AI), computer vision algorithms, as well as 4G-LTE IoT-based monitoring to collect atmospheric data, is presented. Designed as a new scalable platform to accommodate sensors suitable for flight control and communication requirements, the system has the ability to take off, fly and land with full autonomy. In case of possible communication interruption, it can switch to the fail- safe mode set in the ground control station. In addition to being able to make atmospheric measurements, a design has been developed in such a way that wide variety of parameters such as urban planning and development of smart cities, monitoring of industrial emissions, determination of pollution points in the city, redirection and management of traffic can be monitored in real time. The designed drone has a small, lightweight and low- cost multi-sensor system capable of measuring temperature, humidity, pressure, air velocity, noise, amount of UV, light intensity, PM2.5, PM10. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords | air quality; atmospheric data collection; IoT; multiple sensing systems; quadrotor |
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