Network selection and data dissemination in heterogeneous software-defined vehicular network

Vehicular Ad Hoc Network (VANET) is a promising network that is anticipated to be, adaptable, cost-effective, and able to provide safety, infotainment, and related services on the road via inter-vehicle or vehicle-to-roadside unit communication. However, the requirements of various vehicular applica...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2019-10, Vol.161, p.32-44
Main Authors: Chahal, Manisha, Harit, Sandeep
Format: Article
Language:English
Subjects:
Bandwidths
Cellular communication
Game theory
Mobile ad hoc networks
Network selection
Roadsides
SDVN
Software
Software-defined networking
Stackelberg game theory
VANET
Vehicles
Wireless communications
Wireless networks
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Summary:Vehicular Ad Hoc Network (VANET) is a promising network that is anticipated to be, adaptable, cost-effective, and able to provide safety, infotainment, and related services on the road via inter-vehicle or vehicle-to-roadside unit communication. However, the requirements of various vehicular applications are diverse. In a network where applications always employ IEEE 802.11p, bandwidth and coverage range may become insufficient for an application while cellular networks involve high cost. In this context, Software-defined Network (SDN) is an efficient technology for network management in VANETs. This paper presents a Software-defined Vehicular Network (SDVN) Communication using heterogeneous wireless interfaces. Using SDN, network selection is made by applying utility functions and game theory approach. Moreover, we have proposed a data dissemination approach on the selected network, which uses the concept of layering of controllers and link duration. The simulation results prove the effectiveness and efficiency of the proposed scheme, which provides better network performance as compared to the two existing schemes. The proposed scheme under varying density results in an improvement of average End-to-End (E2E) delay by 39.32%, packet delivery ratio by 30.38%, average throughput by 34.87%, and routing overhead by 27%.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2019.06.008