An Efficient Adaptive Broadcast Protocol for Different Scenarios in VANETs

Author(s): Qiubo Huang*, Fei Liu.

Journal Name: Recent Patents on Computer Science

Volume 10 , Issue 2 , 2017

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Graphical Abstract:


Background: VANETs demand a fast and reliable data transmission in real time for safety concerns, which is challenging due to vehicles’ fast movement, network topology dynamics, and communication network varieties. This paper reviews current scientific and patent literature and proposes an efficient and adaptive broadcast protocol using neighbor nodes ’distances and driving directions to determine packet forwarding nodes. The protocol is capable of discovering cross roads utilizing neighbor nodes’ moving direction information, therefore suitable for broader application scenarios including intersections and straight road.

Methods: We use periodic hello packets to establish neighbor list, and use information of neighbors, including the driving direction of neighbors and neighbor distance, to select a forwarding node. We propose a hybrid of distance and neighbor list forwarding broadcast protocol.

Results: This paper focuses mainly on reducing broadcast delay and improving dissemination range in VANET and avoiding impacts of the network topology changes. The density of road vehicles is an important factor for time-critical safety applications. Our OPNET Modeler simulation results demonstrate the following advantages of the protocol: less broadcast packets, broader transmission range, and higher channel utilization.

Conclusion: Our scheme can not only effectively reduce the number of redundant data packets, but also broadcast the packets towards more road traffic intersections. The simulation results show that out scheme can forward the packets to all road segments with minimum overhead and highest successful packet delivery rate. So, our broadcast protocol can be applied to various scenarios in VANETs.

Keywords: VANET, broadcast protocol, neighbor node, distance, driving direction, data transmission.

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Article Details

Year: 2017
Page: [131 - 139]
Pages: 9
DOI: 10.2174/2213275910666161208151325
Price: $58

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