VANETCODE: network coding to enhance cooperative downloading in vehicular ad-hoc networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Code torrent: content distribution using network coding in VANET
MobiShare '06 Proceedings of the 1st international workshop on Decentralized resource sharing in mobile computing and networking
Road probing: RSU assisted data collection in vehicular networks
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Roadcast: a popularity aware content sharing scheme in VANETs
ACM SIGMOBILE Mobile Computing and Communications Review
A survey of urban vehicular sensing platforms
Computer Networks: The International Journal of Computer and Telecommunications Networking
A new architecture for data collection in vehicular networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Delay-bounded data gathering in urban vehicular sensor networks
Pervasive and Mobile Computing
Mobeyes: smart mobs for urban monitoring with a vehicular sensor network
IEEE Wireless Communications
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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.