Data collection for mobile crowd sensing based on tensor completion

Abstract

Mobile crowd sensing can set up a large real-time sensing network, which is closely related to the society, from intelligent mobile terminals carried by ordinary users. However, the current crowd sensing systems face problems like high cost and variable quality of data provided by users. To maximize the accuracy of mobile crowd sensing system, this paper designs the architecture of mobile crowd sensing system in the context of big data, and determines the principle of data optimization, from the following two perspectives: selecting sampling points that benefit the recovery of the entire data, and full utilization of the spatial and temporal correlations between sensing data. Next, an adaptive collection method was developed for crowd sensing data in sparse form or in the form of three-dimensional (3D) tensor. The proposed method was proved effective through experiments. The research results provide reference for applying tensor completion in other data collection tasks.