| Summary: | 碩士 === 東海大學 === 資訊工程學系 === 107 === With the maturity of the Internet of Things technology and the rising awareness of the peoples environmental protection, more and more attention is paid to health risks. The body information generated by the wearable device and the data of the environmental monitoring sensor (such as the airbox) will also explode. This work will explore how to process and store the data most effectively. This work collects data of air quality sensors in Taiwan, and also applies Low-Power Wide-Area Network (LPWAN) technology, integrating Arduino development board, PMS5003T four-in-one environmental sensor, LoRa module to make own sensors. The self-made air quality sensor is used to collect the campus air quality information and achieved a transmission success rate of 70\%. In addition, the integration of data from nearly 3,000 sites in Taiwan is visualized on the web platform. On the other hand, using Raspberry Pi implement an edge computing architecture, the data collected by the sensor is processed by the Message Passing Interface (MPI) on the edge of the Raspberry Pi. Instead of all original data is transmitted to the cloud server for processing and calculation, by reducing the amount of data transmission, thereby reducing energy consumption. This work is also used to implement the object detection environment on the Raspberry Pi. This study combined the Neural Compute Stick (NCS) to enhance the ability to process computer vision images on Raspberry Pi. Through the aid of NCS, the Raspberry Pis frames per second (FPS) is increased by 4 times when the object detection program is executed, and the energy consumption of the Raspberry Pi is also recorded.
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