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IEEE Wireless Communications and Networking Conference 2016

Han, C, Dianati, M and Nekovee, M (2016) IEEE Wireless Communications and Networking Conference 2016

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Abstract

Hybrid vehicular networks (HVNs) are foreseen to provision effective solutions in terms of broadcast services, increased capacity and expanding coverage. Cooperative Awareness Messages (CAMs) are one of the standard means of exchanging information among connected vehicles and smart road infrastructures. Timely dissemination of CAMs is a non-trivial challenge, particularly, in congested areas. This paper proposes a novel and effective decentralised network segmentation based multichannel MAC scheme for large-scale dense HVNs, namely, Decentralised Cognitive Segmentation-based Multichannel MAC (DCSMMAC). The proposed scheme helps reduce the contention level in each single hop via the segmentation of the large-scale network and efficient channel allocation scheme. DCSMMAC is a fully distributed solution, in the sense that it handles access to the shared channels without relying on the assistance from roadside units, cluster heads or other interfaces. This approach eliminates the overhead associated with channel allocation and clustering algorithm making the proposed scheme suitable for large-scale self-organised networks. Performance of the proposed scheme is evaluated and compared with the benchmark IEEE 802.11p, in terms of overall packet delivery rate, packet delivery rate on distance basis, and penetration rate. These results demonstrate that DCSMMAC is superior to the benchmark scheme and reliable at offering the desired QoS in the dense large-scale vehicular networks.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Han, Cchong.han@surrey.ac.ukUNSPECIFIED
Dianati, MUNSPECIFIEDUNSPECIFIED
Nekovee, MUNSPECIFIEDUNSPECIFIED
Date : 3 April 2016
Uncontrolled Keywords : Cooperative Awareness Messages, VANET, Segmentation, Channel allocation
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:49
Last Modified : 17 May 2017 15:13
URI: http://epubs.surrey.ac.uk/id/eprint/840442

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