Class-based delta-encoding for high-speed train data stream

Railway transportation plays an important role in both economic and social development. The requirements of the railway traffic increase in recent decades. In order to meet the growing demand, a new generation control system of railway transportation emerges. It consists of collection, transmission, analysis and scheduling module. In such a context, an information transmission system is built to connect trains and scheduling center. However, the infrastructure of the railway system cannot provide enough bandwidth for such amount of data. As a result, the efficiency of data transmission cannot be ensured. In this paper, we focus on the compression algorithm that reduce the amount of transmitted data and improve the system performance. Based on the analysis of the common algorithms, an efficient compression algorithm, named delta-encoding, is proposed. It consists of two steps: preprocessing and compression. Delta-encoding utilizes a class-based difference model, which reduces the data redundancy, to realize a preprocessing algorithm. With the combination of preprocessing algorithm and a regular compression algorithm, delta-encoding has better performance on compression ratio, and becomes a universal hybrid algorithm for structured data in IoT system rather than a specific algorithm in high-speed train system. Finally, several experiments are provided to prove that delta-encoding have advantages in both compression ratio and compression time.