A Cloud-Based Urban Monitoring System by Using a Quadcopter and Intelligent Learning Techniques

The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve the flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. Main objectives of this system are to monitor the climate status, temperature, humidity, and smoke as well as to detect the fire occurrences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the pre-determined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status, effects of the sensors’ data changes on climate status. An illustrative example is used to draw the simulated area by RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices.