Compression of IoT operational Data Time Series in Vehicle Embedded Systems

This thesis examines compression algorithms for time series operational data which are collected from a vehicle’s CAN bus in an automotive Internet of Things (IoT) setting.

The purpose of a compression algorithm is to decrease the size of a set of time series data (such as vehicle speed, wheel speed, etc.) so that the data to be transmitted from the vehicle is small size, thus decreasing the cost of transmission while providing potentially better offboard data analysis.

The project helped improve the quality of data collected by the data analysts and reduced the cost of data transmission. Since the time series data compression mostly concerns data storage and transmission, the difficulties in this project were where to locate the combination of data compression and transmission, within the limited performance of the onboard embedded systems. These embedded systems have limited resources (concerning hardware and software resources). Hence the efficiency of the compression algorithm becomes very important. Additionally, there is a tradeoff between the compression ratio and real-time performance. Moreover, the error rate introduced by the compression algorithm must be smaller than a given value.

The compression algorithm in this thesis contains two phases: (1) an online lossy algorithm to shrink the total number of data samples while maintaining a guaranteed error bound and (2) a lossless compression algorithm that compresses the output of the lossy algorithm. The algorithm was tested with four typical time series data samples from real CAN logs with different functions and properties. The similarities and differences between these logs are discussed. These differences helped to determine which algorithm should be chosen in both phases. After the experiments which compares performances of different algorithms, a simulation is implemented based on the experiment results. The results of this simulation show that the combined compression algorithm can meet the need of certain compression ratio by controlling the error bound with the data samples on vehicle. Finally, the possibility of improving the compression algorithm in the future is discussed.

Keywords: Data compression, Data transmission, Time series data, IoT, Vehicle connectivity