Minimizing Efforts in Reconciling Participatory Sensing Data

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 reconciling probabilistic data from given (uncertain) time series collected by participatory sensors. More precisely, an iterative process is executed in which we exchange between two mutual reinforcing routines: (i) aggregating probabilistic time series from multiple sensors and expert input, (ii) validating them by expert knowledge with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data.