Towards enabling probabilistic databases for participatory sensing

Participatory sensing has emerged as a new data collection paradigm, in which humans use their own devices (cell phone accelerometers, cameras, etc.) as sensors. This paradigm enables to collect a huge amount of data from the crowd for world-wide applications, without spending cost to buy dedicated sensors. Despite of this benefit, the data collected from human sensors are inherently uncertain due to no quality guarantee from the participants. Moreover, the participatory sensing data are time series that not only exhibit highly irregular dependencies on time, but also vary from sensor to sensor. To overcome these issues, we study in this paper the problem of creating probabilistic data from given (uncertain) time series collected by participatory sensors. We approach the problem in two steps. In the first step, we generate probabilistic times series from raw time series using a dynamical model from the time series literature. In the second step, we combine probabilistic time series from multiple sensors based on the mutual relationship between the reliability of the sensors and the quality of their data. Through extensive experimentation, we demonstrate the efficiency of our approach on both real data and synthetic data.