Abstract
This paper proposes a method to analyze the temporal change in the quality of LoRa communication, which is a low-power wide-area (LPWA) wireless technology for the IoT, using massive measurement data from a bus location management system. To analyze this temporal change in LoRa communication quality, many measurement data at the same position must be compared. However, buses do not always follow the same route every day. Even if buses follow the same route on different days, the measurement data are not acquired from exactly the same position. To solve these problems, we use locality-sensitive hashing (LSH) to extract comparable data from massive measurement data. Furthermore, we treat the extracted data with different distances on the same day as a series and compare regression formulas determined respectively from the plural series. As a result, it was confirmed that the received signal strength indicator (RSSI) of LoRa communication had almost no effect even in heavy rainfall of 30 mm/h. In addition, we confirmed that the radio attenuation was smaller about 2 dB than usual when there was over 7 cm of snow cover.
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Suzuki, K., Takahashi, T. (2021). Analyzing Temporal Change in LoRa Communication Quality Using Massive Measurement Data. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science(), vol 12280. Springer, Cham. https://doi.org/10.1007/978-3-030-69886-7_11
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DOI: https://doi.org/10.1007/978-3-030-69886-7_11
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