CCR: Capacity-constrained replication for data delivery in vehicular networks

2013 ◽  
Author(s):  
Yuchen Wu ◽  
Yanmin Zhu ◽  
Hongzi Zhu ◽  
Bo Li
Keyword(s):  
Vehicular Networks ◽  
Data Delivery ◽  
Capacity Constrained

Enhancing data delivery in vehicular networks using dual-radio architecture

2018 ◽  
Vol 1 (1-4) ◽  
pp. 52-64 ◽  
Author(s):  
Linghe Kong ◽  
Fan Wu ◽  
Xiaofeng Gao ◽  
Min-You Wu ◽  
Guihai Chen
Keyword(s):  
Vehicular Networks ◽  
Data Delivery ◽  
Radio Architecture

scholarly journals Hybrid Position-Based and DTN Forwarding for Vehicular Sensor Networks

2012 ◽  
Vol 8 (4) ◽  
pp. 186146 ◽  
Author(s):  
Fan Li ◽  
Lei Zhao ◽  
Xiumei Fan ◽  
Yu Wang
Keyword(s):  
Sensor Networks ◽  
Routing Protocols ◽  
Large Scale ◽  
Vehicular Networks ◽  
Ad Hoc ◽  
Delivery Ratio ◽  
Data Delivery ◽  
Ad Hoc Routing ◽  
Efficient Data ◽  
Set Up

Efficient data delivery in vehicular sensor networks is still a challenging research issue. Position-based routing protocols have been proven to be more suitable for dynamic vehicular networks or large-scale mobile sensor networks than traditional ad hoc routing protocols. However, position-based routing assumes that intermediate nodes can always be found to set up an end-to-end connection between the source and the destination; otherwise, it suffers from network partitions which are very common in vehicular networks and leads to poor performances. This paper addresses data delivery challenge in the possible intermittently connected vehicular sensor networks by combining position-based forwarding strategy with store-carry-forward routing scheme from delay tolerant networks. The proposed routing method makes use of vehicle driving direction to determine whether holding or forwarding the packet. Experimental results show that the proposed mechanism outperforms existing position-based solutions in terms of packet delivery ratio.

Deep convolutional neural networks for data delivery in vehicular networks

2021 ◽  
Vol 432 ◽  
pp. 216-226
Author(s):  
Hejun Jiang ◽  
Xiaolan Tang ◽  
Kai Jin ◽  
Wenlong Chen ◽  
Juhua Pu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicular Networks ◽  
Data Delivery ◽  
Deep Convolutional Neural Networks
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