Dynamic Measurement Policy for Vehicular Sensor Network Based on Compressive Sensing

Abstract

Compressive Sensing (CS) is a promising approach to compress the data with spatial-temporal correlation, so as to reduce the communication cost in Vehicular Sensor Network(VSN). However, the current research on the application of CS in the VSN does not consider dynamic changes in data sparsity and vehicle distribution, which may lead to unacceptable reconstruction accuracy. In order to ensure the accuracy of data reconstruction, this paper first analyses the factors that affect the choice of measurement quantity in VSN. Then, due to the real-time changes in the data sparsity and vehicle distribution, a dynamic measurement policy for VSN based on CS is proposed, that can adjust the number of measurements according to the real-time data sparsity and vehicle distribution. Through the adjustment of measurement quantity, the accuracy of reconstruction is improved to achieve higher quality data communication. The experiment shows that the proposed dynamic measurement policy improves the reconstruction accuracy by 15.3% compared with the existing CS approach in the VSN.