Application-driven sensing data reconstruction and selection based on correlation mining and dynamic feedback
Zhichuan Huang, Tiantian Xie, Ting Zhu, Jianwu Wang, Qingquan Zhang · 2016
As sensors spread across almost every industry, the Internet of Things (IoT) is going to trigger an era of big data. However, the abundance of available sensing data causes new challenges when building IoT applications. One main challenge is how to select proper data from large amount of sensing data for learning useful information efficiently. Existing approaches require developers to manage data for each specific application, which is very time consuming since the developers may not have enough knowledge about the dynamic changing data quality of different sensors. In this paper, we propose a data management middleware to learn the correlations between time series sensor data without prior knowledge. The learned correlation is then applied to select the useful sensor and reconstruct the incorrect data. To generalize the correlation models for each application, we utilize the dynamic feedback from the application to update the data selection and reconstruction. We evaluate our data management middleware in smart grids. The evaluation results show that our middleware can achieve better application performance with the help of dynamic feedback, data reconstruction and data selection.