Data gathering with compressive sensing for Urban traffic sensing in vehicular networks

Abstract

Vehicular networks have become as an important platform to monitor metropolitan-scale traffic information. However, it is a challenge to deliver and process the huge amount of data from vehicular devices to a data center. By studying a large number of taxi data collected from around 3,000 taxis from Shenzhen city in China, we find that the data readings collected by vehicular devices have a strong spatial correlation. In this paper, we propose a novel scheme based on compressive sensing for traffic monitoring in vehicular networks. In this scheme, we construct a new type of random matrix with only one nonzero element of each row, which can significantly reduce the number of data needed to be transmitted while guaranteeing good reconstruction quality at the data center. Simulation results demonstrate that our scheme can achieve high reconstruction accuracy at a much lower sampling rate.