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Data aggregation in VANETs

A generalized framework for channel load adaptive schemes
: Jiru, Josef; Bremer, Lars; Graffi, Kalman

Postprint urn:nbn:de:0011-n-3180490 (1.1 MByte PDF)
MD5 Fingerprint: a4e1493fe99e90fc9c491de908aeb33e
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Created on: 11.12.2014

Aschenbruck, Nils (Ed.) ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
39th Annual IEEE Conference on Local Computer Networks, LCN 2014. Proceedings : 8-11 September 2014, Edmonton, Canada
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-3780-6
ISBN: 978-1-4799-3778-3 (Print)
ISBN: 978-1-4799-3779-0
ISBN: 978-1-4799-3783-7
ISBN: 978-1-4799-3784-4
ISBN: 978-1-4799-3781-3
Conference on Local Computer Networks (LCN) <39, 2014, Edmonton>
Conference Paper, Electronic Publication
Fraunhofer ESK ()
data aggregation; adaptive systems; V2X communication; cross layer optimization; VANET; telecommunication congestion control; telecommunication traffic; vehicular ad hoc networks; wireless channel; application layer; channel load adaptive scheme; channel load threshold; decentralized congestion control; automotive connectivity; Local Wireless Networks; lokales Funknetz; Vehicle-to-X; V2X

One of the main communication challenges in vehicle-to-x communication is scalability. With increasing number of communication nodes the wireless channel must not get congested especially if a large amount of sensor data has to be forwarded over multiple nodes to a data processing application. This challenge can be solved by reducing the data load through data aggregation. This work introduces a framework for data aggregation as a decentralized congestion control mechanism on the application layer. This framework can be used to flexibly design aggregation schemes that adaptively adjust the generated data load depending on the overall channel load. Three basic aggregation schemes with different complexity and resulting data precision were developed within this framework and they are discussed in this paper. Performance evaluations show that the aggregation schemes are able to adapt to given channel load thresholds within seconds and deliver optimal data quality even in traffic jam situations.