Paper Info
Title
SPIDER: A Social Computing Inspired Predictive Routing Scheme for Softwarized Vehicular Networks
Abstract
Software-defined vehicular network (SDVN) is a promising networking paradigm that can provide intelligent information exchanges by separating network management and data transmission. Although the transmission quality of vehicles can he greatly improved by deploying softwarized networking schemes, critical networking issues such as the timeliness of data packets remain due to the dynamic nature of vehicular networks. It is vital to design efficient networking schemes by deeply considering the characteristics of the network, transportation system, and users, to improve overall network performance. To this end, this paper proposes a social computing inspired predictive routing scheme (SPIDER) for SDVNs that has a comprehensive consideration to enable low-latency reliable data exchange under dynamic vehicular networks. As for the link lifetime grounded on the vehicular historical data, we introduce the context feature mining and one-shot prediction method to predict vehicle movements with considering the energy saving. We also involve social computing techniques to find the relay nodes with good data spreading abilities. The extensive experiments prove our proposed scheme outperforms four existing schemes.
Year
DOI
Venue
2022
10.1109/TITS.2021.3122438
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Keywords
DocType
Volume
Software-defined vehicular network, social computing, context prediction, predictive routing
Journal
23
Issue
ISSN
Citations 
7
1524-9050
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Liang Zhao131.45
Tong Zheng200.34
Mingwei Lin300.34
Ammar Hawbani400.34
Jiaxing Shang500.34
Chunlong Fan632.10