本研究運用資料壓縮技術，促使資料傳輸量降低，減少頻寬與功率消耗，間接節省系統建置成本與維護成本；本研究的資料壓縮流程共有兩道程序，首先使用向量量化器將感測資料進行壓縮，接著利用可變長度編碼法則，將壓縮失真再進行壓縮，使得通過兩道壓縮程序的感測資料，能夠完整還原，為無失真壓縮；也可以單獨行使向量量化器進行壓縮，為失真壓縮。

無失真壓縮能夠應用在所有的物聯網量測，實用性廣泛，不過壓縮率較低；失真壓縮的應用範圍，局限於部分能夠容許誤差的物聯網量測，實用性狹隘，但是壓縮率較高。

本研究將壓縮法則應用於溫度量測與細懸浮微粒量測，並說明系統裝設之流程，解釋及分析實驗參數的選擇方式。

本論文於最後進行本法則的效能評估，證明本法則的碼簿具有較低的儲存所需位元組，同時本法則也有效的降低了平均每天傳送位元數。

[1] Y. Li, Z. Zhang, W. Huangfu, X. Chai, X. Zhu and H. Zhu, “Sea Route Monitoring System Using Wireless Sensor Network Based on the Data Compression Algorithm,” China Communications, no.1, pp. 219-222, 2014.

[2] A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic, 1992.

[3] J. Fang and H. Li, “Hyperplane-Based Vector Quantization for Distributed Estimation in Wireless Sensor Networks,” IEEE Transactions on I. T., vol. 55, no. 12, pp. 5682-5699, Dec. 2009.

[4] H. Si, B. L. Ng, Md. S. Rahman and J. Zhang, “A Novel and Efficient Vector Quantization Based CPRI Compression Algorithm,” IEEE Transactions on V. T., vol. 66, no. 8, pp. 7061-7071, Aug. 2017.

[5] J. Paek and J. Ko, “K-Means Clustering-Based Data Compression Scheme for Wireless Imaging Sensor Networks,” IEEE Systems J., vol. 11, no. 4, pp. 2652-2662, Dec. 2017.

[6] Central Weather Bureau, CODiS(CWB Observation Data Inquire System), Available：http://e-service.cwb.gov.tw/HistoryDataQuery/

[7] Ernie's leisure code, Web crawler on observation data using python, Available：http://ernie55ernie.github.io/python/2016/08/26/web-crawler-on-observation-data-using-python.html

[8] Environmental Protection Administration Executive Yuan R.O.C.(Taiwan), 歷年監測資料下載, Available：https://taqm.epa.gov.tw/taqm/tw/YearlyDataDownload.aspx

[9] C. Cheng-Ta, “Design of a High-Sensitivity Ambient Particulate Matter 2.5 Particle Detector for Personal Exposure Monitoring Devices,” IEEE Sensors J., vol. 18, no. 1, pp. 165-169, Jan. 2018.

[10] H. Yin, H. Wan and A. J. Mason, “Separation and Electrochemical Detection Platform for Portable Individual PM2.5 Monitoring,” 2017 IEEE I.S.C.A.S., pp. 1-4, 2017.