Trust-based time series data model for mobile crowdsensing

Abstract

The recent proliferation of mobile devices embedded with capable sensors, provides an opportunity to the popular concept of mobile crowdsensing. By studying the correlation of crowd-sensed data in both spatial and temporal dimensions, we can get a clear understanding of the intrinsic pattern of data in mobile crowdsensing, which is the basic for further data analysis, such as data filtering, smoothing and prediction. However, the crowd-sensed data are normally noise and unreliable due to the diverse mobility patterns and selfish behaviours of mobile users, making the classical data models in wireless sensor networks fail in this new context. In this paper, we propose a robust and reliable time series data model based on Dynamic Bayesian Network to describe the characteristics of the crowd-sensed data. The proposed data model can figure out the spatial and temporal correlation of data in the environment, where the data has high noise levels and mobile users are untrustworthy. We conduct extensive evaluations based on both simulation and a real-world data set. Our evaluation results show that our method successfully modeled the crowd-sensed time series data with effectiveness, efficiency and trustworthiness.