Paper Info
Title
ECDS: Efficient collaborative downloading scheme for popular content distribution in urban vehicular networks.
Abstract
The recent development of the Vehicular Ad-hoc Networks (VANETs) has motivated an increasing interest in in-vehicle consumption, and hence, the Popular Content Distribution (PCD) has become a heated issue. Compared with PCD solutions based on the widely-used cellular networks and Dedicated Short Range Communications (DSRC), solutions based on Collaborative Downloading (CD) are more economical and efficient. Due to the limited bandwidth, the On-Board Units (OBUs) passing through a Road Side Unit (RSU) can only download a portion of the popular content. To get over that drawback and to effect a collaborative downloading, a P2P network should be constructed among the OBUs which fall out of the RSUs coverage. In this paper, we address the efficient collaborative downloading scheme (ECDS) for PCD in urban traffic scenarios. To adapt to the rapid-changing characteristics of the VANET topology, a new cell-based clustering scheme is proposed, which greatly simplifies the modeling. Besides a strategy of inter-cluster Relay Selection is proposed to construct a pear-to-pear (P2P) network of scale-free property, which will help enhancing the information spread. Furthermore, another inter-cluster strategy of generation selection is to be collaborated to accelerate the dissemination process in the P2P network. The comparison experiments to two up-to-date collaborative PCD protocols demonstrate the high performance of the proposed scheme, i.e. ECDS.
Year
DOI
Venue
2016
https://doi.org/10.1016/j.comnet.2016.02.006
Computer Networks
Keywords
Field
DocType
Popular content distribution,Collaborative downloading,Relay selection,Generation selection,Scale-free property
Drawback,Computer science,Upload,Computer network,Bandwidth (signal processing),Cellular network,Cluster analysis,Dedicated short-range communications,Relay,Vehicular ad hoc network,Distributed computing
Journal
Volume
Citations 
PageRank 
101
8
0.51
References 
Authors
24
2
Name
Order
Citations
PageRank
Wei Huang15322.67
Liangmin Wang215910.11