An Internet of Things Data Compression Method Based on a Railway Project
Haotian Chen, Wen Yang, Xiya Li, Jinglin Xu, Pengyu Sun, Cannan Yu · 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) · 2022
Due to the rapid development of the Internet of Things, monitoring sensors have become increasingly popular in railway construction. The project has relatively high requirements for real-time monitoring, data storage, and rapid display to ensure construction safety. Therefore, data compression is a fundamental issue regarding time series generated by a wide range of sensors. For sequential data, a reasonable compression method should possess the following characteristics: a high degree of data reduction, the preservation of local and global features, and rapid response to user requests. The computer performs better as a result of several features. This paper utilizes a piecewise approximation algorithm to encode sequential data without compression. Comparatively to coding compression, this method can process and display data more quickly, and the compression rate is as high as 24%.