Browsing by Author "Hsu, Chun-Chia"

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Long-Term Performance Studies of a LoRaWAN-based PM2.5 Application on Campus
(2018-01-01)
Wang, Shie-Yuan
;
Zou, Ji-Jhe
;
Chen, Yo-Ru
;
Hsu, Chun-Chia
;
Cheng, Yu-Hsiang
Recently, LoRa/LoRaWAN has been proposed as a promising LPWAN technology for Internet-of-Things (IoT) applications. For monitoring the long-term PM2.5 air quality on our campus, starting from February 1 2017, we have set up a LoRaWAN-based PM2.5 air quality monitoring system on our campus. In total, twenty-two PM2.5 sensor devices have been operating at the rooftop of buildings or on the lamp poles since then. Each of these PM2.5 sensor devices uses a LoRaWAN module to send its most recent PM2.5 value to the LoRaWAN gateway every 72 seconds. After being received successfully by the LoRaWAN gateway, the packets carrying these values were stored into a database with their SNR/RSSI information. With the sequence numbers added by our PM2.5 application running on each device, the packets stored in the database provide many important information about the long-term packet loss rate performance of LoRaWAN on campus. In this paper, we process these data to report real long-term performance of LoRaWAN when used on campus.
Performance of LoRa-based IoT Applications on Campus
(2017-01-01)
Wang, Shie-Yuan
;
Chen, Yo-Ru
;
Chen, Tzu-Yang
;
Chang, Chia-Hung
;
Cheng, Yu-Hsiang
To promote smart campus, National Chiao Tung University is deploying several location-based IoT applications based on wireless communications technologies including LoRa and NB-IoT. The applications are indoor/outdoor environment conditions monitoring (such as PM2.5, temperature, and humidity), emergency buttons, and so on. This paper uses PM2.5 measurement application as an example to present the performance of LoRa when it is used on campus. We investigated the LoRa transmission performance from LoRa end-devices to the LoRa gateway. We show how packet losses were affected by the distance between the end-device and the gateway, the transmit power, the payload length, the antenna angle, the time of day, and the weather conditions. The pattern of LoRa packet losses were also measured and we found that in our long-term PM2.5 monitoring application, more than 99% of LoRa packet losses occurred with three or less consecutive packet losses.