In-Network Time-Series Data Compression for Electric Internet of Things

Abstract

The IoT is considered as one of the most important supporting technologies of the smart grid and the smart grid is considered as one of the most important application areas of the IoT. However, the electric IoT has vast amount and many kinds of sensors, very high data acquisition frequency, and a highly heterogeneous network, which lead to the challenge that if the raw time-series data gathered by sensors is all transmitted to the sensing data center via network and then compressed and reserved, the bandwidth and the computing resource requirements of the network and the sensing data center, respectively, are unacceptable. In this paper, in order to cope with the challenge, we propose an in-network time-series data compression algorithm, i.e., the DSDT (Distributed Swinging Door Trending) algorithm, for the electric IoT, which utilizes the computing resource of the sensors to compress the raw sensing data, and then transmits the compressed data to sensing data center where the data will be further compressed and reserved. In this way, the bandwidth and the computing resource requirements of the network and the sensing data center, respectively, are significantly reduced. A performance study shows the superiority of the algorithm.