An Adaptive and Compressive Data Gathering Scheme in Vehicular Sensor Networks

In vehicular sensor networks, probe vehicles can act as mobile sensors to monitor physical world and report to an urban sensing center. However, the distribution of probe vehicles is uneven over space and time. Data redundancy and vacancy are common phenomena for different spatiotemporal positions, which seriously degrade sensing efficiency and accuracy. To address this issue, we propose an adaptive and compressive data gathering scheme (AC-Sense) based on matrix completion theory. The scheme adaptively determines the locations where to obtain samples from so that the principal features of physical world can be captured with a reduced number of probe vehicles. The spatio-temporal correlation between sensor data is exploited to estimate the un-sampled data. Furthermore, we introduce a feedback mechanism to stabilize sensing performance according to the evaluation of data error. We perform extensive experiments based on real taxicab mobility traces and air quality data in Beijing. The experimental results show that the proposed scheme largely improves sensing efficiency while ensuring required data quality.