Efficient sampling and compressive sensing for urban monitoring vehicular sensor networks

Abstract

Vehicular sensor network (VSN) using vehicle-based sensors is an emerging technology that can provide an inexpensive solution for surveillance and urban monitoring applications. For the constantly moving vehicles, resulting in unpredictable network topology, data transmission in VSN is vulnerable to packet losses, thus deteriorating the surveillance quality. To resolve this problem, a cooperative data sampling and compression approach is proposed. Based on compressive sensing, this approach does not require inter-sensor communication and adopts sparse random projections to remove redundancy in spatially neighbouring measurements. It is experimentally shown that the proposed algorithm provides fairly accurate reconstruction of the field under surveillance, and incurs much less communication traffic load compared to conventional sampling strategies. Practical data sets, including the temperature distribution in Beijing and the global position system (GPS) tracking data of over 6000 taxis in the city, are used in our experiments to verify the reconstruction accuracy and energy efficiency of the scheme. Different vehicular mobility models are also employed to study the impact of movement behavior. Simulation results show that our proposed approach outperforms the conventional sampling and interpolation strategy, which propagates data in uncompressed format, by 5 dB in reconstruction quality and by 50% in communication complexity reduction for the same sampling rate.