Efficient Compression Method Based on Object Model for Massive Real-time Data in Power IOT
Jixin Hou, Jinghu Zhao, Zidong Wu, Fan Ying, Xiaochao Wang, Jie Meng · Journal of Physics Conference Series · 2022
Abstract With rapid development of the Internet of things (IOT) in power field, equipment of various majors began to access the smart IOT system on a large scale. In this paper, a new data compression algorithm based on object model (CABM) is proposed to solve the problems of large-scale applications of power Internet of Things, such as high-frequency transmission of large quantities, real-time short data can not be effectively compressed, resulting in low efficiency of cloud edge information transmission and serious waste of bandwidth resources. The CABM adds a compression and decompression module between the IOT management platform and the edge agent; the IOT management platform automatically generates a compression dictionary based on the object model file and saves it synchronously in the edge agent. During data transmission, both the sender and receiver complete automatic compression and decompression according to the compression dictionary. The experimental results show that for real-time message data below 1KB, the compression rate using CABM algorithm is 3-5 times higher than that using GZIP compression algorithm; and the compression time is 10-20 times lower than that using GZIP; the efficiency using CABM algorithm is 4.5 times higher than that using GZIP algorithm; that is, CABM can reduce redundant data transmission between cloud edges and improve the efficiency of cloud edge transmission.