A Hybrid Approach for Improving the Data Quality of Mobile Phone Sensing

Abstract

Few studies have researched the temporal and spatial effects of insufficient exposure of sensors in mobile phone sensing. In this paper, the missing data problem in mobile phone sensing is addressed by using a hybrid approach to design an estimation model. This estimation model reflects the effects of participatory and opportunistic nodes based on the success probability model. The proposed model considers the spatial and temporal correlation of sensing data to accurately estimate the missing information. By applying the linear regression and linear interpolation models to sample data from neighboring nodes of the missing data, the spatial and temporal context can be described. The experiment results show that the proposed model can estimate the missing data accurately in terms of simulated and real-world datasets.