CESense: Cost-Effective Urban Environment Sensing in Vehicular Sensor Networks

Abstract

In vehicular sensor networks, vehicles can act as mobile sensors to monitor the dynamic features of the physical world such as traffic flow, air quality, and temperature. However, the conventional full-coverage sensing approach is neither realizable nor cost-effective since the sensor-equipped vehicles are unevenly distributed and the environmental data are spatio-temporally correlated. To this end, we propose a cost-effective urban environment sensing solution (CESense), that exploits the sensing data correlations to improve the sensing accuracy and efficiency. CESense gathers data only at some specific areas of the whole sensing space and reliably infers the status of unsensed areas. Particularly, CESense uses a probabilistic matrix factorization model to reveal the latent features that impact the environmental status. Then, an appropriate set of sensing areas can be selected by fully taking advantage of these latent features and the sensing resource distribution patterns. In addition, to be adaptive to the dynamic environment, a checkpoint mechanism is designed to supervise the data gathering progress. Extensive experiments, which are based on the real taxicab mobility traces and air quality data collected in Beijing city, demonstrate that CESense can significantly improve the accuracy and efficiency of vehicular sensing.