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Adaptive decision algorithms for data aggregation in VANETs with defined channel load limits

: Jiru, Josef; Mammu, Aboobeker Sidhik Koyamparambil; Roscher, Karsten

Postprint urn:nbn:de:0011-n-3600511 (1.7 MByte PDF)
MD5 Fingerprint: ab2cdb065aaaeb61e6610b2af8c526fb
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Erstellt am: 17.9.2015

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Intelligent Vehicles Symposium, IV 2015. Proceedings : June 28 – July 1, 2015, COEX, Seoul, Korea
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4673-7266-4
Intelligent Vehicles Symposium (IV) <2015, Seoul>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer ESK ()
data aggregation; adaptive systems; adaptive decision; V2X; vehicle-to-X; V2X communication; VANET; automotive connectivity; local wireless networks

The main challenges when realizing safety related applications based on vehicle-to-x communication are scalability and reliability. With an increasing number of vehicles, the communication channel must not get congested especially if a large amount of information has to be transmitted over multiple hops to a destination. This challenge can be solved by reducing the data load through data aggregation. In this paper, we present a decentralized congestion control using the channel busy ratio (CBR) on the application layer for an adaptive control of aggregation levels in real time. Adaptive decision algorithms decide which data is aggregated in real time. Two different approaches are compared: One approach relies on two CBR thresholds (min/max) only and one that allows a higher number of CBR thresholds. In both cases, the adaptive aggregation control increases and decreases the data aggregation levels based on these thresholds. Our simulation results show that both approaches are able to adjust the aggregation levels to given channel load thresholds within seconds resulting in improved data quality even in heavy congested situations. Adaptive decision algorithms result in less error introduced by aggregation. The impact of the two aggregation level control approaches is discussed regarding channel load and resulting data precision.