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V-pada: Vehicle-platoon-aware data access in vanets
| Venue: | IEEE Transactions on Vehicular Technology |
| Citations: | 5 - 3 self |
BibTeX
@ARTICLE{Zhang_v-pada:vehicle-platoon-aware,
author = {Yang Zhang and Student Member and Guohong Cao},
title = {V-pada: Vehicle-platoon-aware data access in vanets},
journal = {IEEE Transactions on Vehicular Technology},
year = {},
pages = {2011}
}
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Abstract
Abstract—The high mobility of vehicles and the unreliable wireless communication significantly degrade the performance of data access in vehicular ad hoc networks (VANETs). To address this problem, we propose a novel vehicle-platoon-aware data access solution called V-PADA. In V-PADA, vehicles contribute part of their buffers to replicate data for others in the same platoon and share data with them. When a vehicle leaves the platoon, it prefetches interested data and transfers its buffered data to other vehicles in advance so that they can still access the data after it leaves. To achieve this goal, V-PADA consists of two components: First, a vehicle-platooning protocol is designed to identify platoon formation and predict platoon splits. We use stochastic time series analysis to detect platoon and mobility anomalies and further introduce a two-step split prediction method to reduce the false alarm rate due to road curves. Second, a data management component is designed to guide platoon members to replicate and prefetch the most suitable data so that both high data availability and low data access overhead can be achieved. Extensive simulation results show that V-PADA can effectively improve the data access performance in VANETs. Index Terms—Data replication, platoon, vehicular ad hoc network (VANET). I.
Keyphrases
vehicle-platoon-aware data access vehicular ad hoc network unreliable wireless communication vehicle-platooning protocol stochastic time series analysis predict platoon split novel vehicle-platoon-aware data access solution false alarm rate suitable data data access performance share data high mobility two-step split prediction method road curve extensive simulation result data management component mobility anomaly platoon member interested data platoon formation low data access overhead index term data replication data access high data availability
