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Cluster based semantic data aggregation in VANETs

 
: Mammu, Aboobeker Sidhik Koyamparambil; Jiru, Josef; Hernandez-Jayo, Unai

:
Preprint urn:nbn:de:0011-n-3642094 (1.0 MByte PDF)
MD5 Fingerprint: 054570e6309472b399c786b24cdd2353
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Created on: 10.11.2015


Barolli, Leonard ; IEEE Computer Society, Technical Committee on Distributed Processing:
IEEE 29th International Conference on Advanced Information Networking and Applications, AINA 2015. Proceedings : Gwangiu, South Korea, 24-27 March 2015
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4799-7906-6
ISBN: 978-1-4799-7904-2
pp.747-753
International Conference on Advanced Information Networking and Applications (AINA) <29, 2015, Gwangju>
English
Conference Paper, Electronic Publication
Fraunhofer ESK ()
cluster member node; VANET; clustering; data aggregation; cluster based semantic data aggregation; CBSDA; cluster; cluster member; CM; channel busy ratio; vehicle to vehicle communication; V2V; vehicle to infrastructure; V2I; vehicular ad hoc network

Abstract
Recently, we are witnessing increased interest in the research of Vehicular Ad-hoc Networks (VANETs). Due to the peculiar characteristics of VANETs, such as high speed, the unstable communication link, and network partitioning, information transfer becomes inevitably challenging. The main communication challenges in vehicle to vehicle communication is scalability, predictability and reliability. With increasing number of vehicles in highway congestion scenarios, the congestion application need to disseminate large amount of information over multiple hops to the control center. This challenge can be solved by reducing the data load through clustering and data aggregation. In this paper, we propose cluster based semantic data aggregation (CBSDA) protocol that divide the road into different segments based on the cluster-ID and aggregate the data in each cluster. The aggregation scheme is a lossy aggregation with maximum precision. CBSDA scheme stores the data using a data structure that consists of super cluster, cluster and cluster member (CM) nodes. CBSDA is proposed to adaptively adjust the number of super cluster nodes. Moreover, the CBSDA scheme consists of weighted deviation scheme that decides which data to be fused for aggregation. Additionally, the aggregation level is controlled based on the density of vehicles and channel busy ratio (CBR). Simulation results show that the CBSDA using weighted deviation decision scheme is able to quickly reduce the channel congestion and improve the data precision even in congested traffic scenarios.

: http://publica.fraunhofer.de/documents/N-364209.html