A Reliable Data Compression Scheme in Sensor-Cloud Systems Based on Edge Computing

Abstract

The rapid development of the IoT and cloud computing has spawned a new network structure — sensor-cloud system (SCS) where sensors, sensor networks, and cloud computing are integrated to perform data sensing, collection, transmission, and decision making. The large-scale deployment of sensors creates a massive amount of data, posing new challenges in data transmission and storage. As an intermediate platform between IoT and cloud platforms, edge computing provides IoT with data collection, processing, and scheduling services. This paper proposes a hybrid data compression scheme that incorporates lossy and lossless compression in SCS based on edge computing to address the increasing challenges. Moreover, we propose a new reliable lossy compression algorithm DFan, based on the simplified Fan algorithm with a high compression ratio (CR). By introducing the data tolerable deviation, DFan transforms single-factor decision-making into multi-factor decision-making, reducing the error of lossy compression. Through experiments on IntelLab and MIT-BIH datasets, the proposed hybrid data compression scheme achieves an overall CR of $4.21\times $ and $3.88\times $ , respectively. The lossy CR of DFan is $6.42\times $ and $5.1\times $ , respectively, and the Percentage RMS Difference (PRD) caused by lossy compression is 0.27% and 0.56%, respectively. The hybrid compression scheme, high compression ratio, and reliable data restoration make this scheme attractive to the data processing of sensors in SCS.

Keywords

• Sensor-cloud system (SCS)
• data compression
• edge computing
• Internet of Things (IoT)