Fast Algorithm of Truncated Burrows-Wheeler Transform Coding for Data Compression of Sensors

Lots of sensors in the IoT (Internet of things) may generate massive data, which will challenge the limited sensor storage and network bandwidth. So the study of big data compression is very useful in the field of sensors. In practice, BWT (Burrows-Wheeler transform) can gain good compression results for some kinds of data, but the traditional BWT algorithms are neither concise nor fast enough for the hardware of sensors, which will limit the BWT block size in a very small and incompetent scale. To solve this problem, this paper presents a fast algorithm of truncated BWT named “CZ-BWT algorithm” and implements it in the shareware named “ComZip.” CZ-BWT supports the BWT block up to 2 GB (or larger) and uses the bucket sort. It is very fast with the time complexity O(N) and fits the big data compression. The experiment results indicate that ComZip with the CZ-BWT filter is obviously faster than bzip2, and it can obtain better compression ratio than bzip2 and p7zip in some conditions. In addition, CZ-BWT is more concise than current BWT with SA (suffix array) sorts and fits the hardware BWT implementation of sensors.