Efficient Time-Series Data Delivery in IoT With Xender
Authors: 
Libin Liu, Jingzong Li, Zhixiong Niu, Wei Zhang, Jason Chun Xue, 
Hong XuAuthors Info & Claims
Pages 4777 - 4792
Abstract
Large amounts of time-series data need to be continually delivered from IoT devices to the cloud for real-time data analytics. The data delivery process is intrinsically slow and costly. Therefore, lots of work proposes various data reduction methods to accelerate it. Yet, they are either designed for the simple linear time-series data or computation-intensive, which is not suitable for the IoT devices with limited resources. In this paper, we propose Xender, a system to accelerate time-series data delivery. Xender consists of two key components: data sampler and data generator. Data sampler works on IoT devices to sample time-series data with low resource footprint, and data generator works on the cloud to efficiently generate data that significantly resembles the original. Besides, Xender can adapt to the dynamic characteristics of the time-series data with the content-aware mechanism, as well as the dynamic computation resources by supporting multiple data generation quality levels and using the anytime generation mechanism. We implement Xender and evaluate it with testbed experiments using six real-world datasets. The results show that it can significantly reduce data delivery time by 45.79% on average compared against existing schemes, and adapt to computation resources with up to 1014.40Mbps data generation throughput.
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