Compressive Data Retrieval with Tunable Accuracy in Vehicular Sensor Networks

Abstract

On-demand data retrieval is a crucial routine operation in a vehicular sensor network. However, on-demand data retrieval in a vehicular environment is particularly challenging because of frequent network disruption, large number of data readings and limited transmission opportunities. Real world vehicular datasets usually contain a lot of data redundancy. Motivated by this important observation, we propose an approach called CDR with compressive sensing for on-demand data retrieval in the highly dynamic vehicular environment. The distinctive feature of CDR is that it supports tunable accuracy of data collection. There are two major challenges for the design of CDR. First, the sparsity level of the vehicular dataset is typically unknown beforehand. Second, it is even worse that the sparsity level of the dataset is changing over time. To combat the challenge posed by time-varying data sparsity, CDR can terminate from further collection of measurements, based on an adaptive condition on which only localized measurements and computation are needed. Extensive simulations with real datasets and real vehicular GPS traces show that our approach achieves good performance of data retrieval with user-customized accuracy.