Local Density Estimation for VANETs

Abstract : Local vehicle density estimation is an integral part of various applications of Vehicular Ad-hoc Networks (VANETs) such as congestion control and congestion traffic estimation. Currently, many applications use beacons to estimate this density. However, many studies show that the reception rate of these beacons can significantly drop at short distances due to a broadcast storm problem in high-density situations. Therefore, the local vehicle density estimation helps VANETs' applications in giving an estimate of the number of neighbors in their communication range where a vehicle could send and receive correctly packets. Indeed, an accuracy local density estimation considerably enhances the performance of these applications and makes them adaptable to different road scenarios. Our aim in this work is to extend more the local density to be segmented and within the maximum transmission range. This potential gives VANETs' application the ability to estimate at different ranges depending on their requirements. To this goal, this paper proposes a segment-based approach that ensures high accuracy with low overhead over the maximum vehicles transmission range. Performance results show that the proposed strategy reaches a mean error ratio of approximately 3% with limited overhead over 1000m of range.
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Global Information Infrastructure and Networking Symposium (GIIS), Oct 2016, Porto, Portugal. IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2016 Global Information Infrastructure and Networking Symposium (giis), 2016, 〈http://ieeexplore.ieee.org/document/7814935/〉. 〈10.1109/GIIS.2016.7814935〉
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Noureddine Haouari, Samira Moussaoui, Mohamed Guerroumi, Sidi Mohammed Senouci. Local Density Estimation for VANETs . Global Information Infrastructure and Networking Symposium (GIIS), Oct 2016, Porto, Portugal. IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2016 Global Information Infrastructure and Networking Symposium (giis), 2016, 〈http://ieeexplore.ieee.org/document/7814935/〉. 〈10.1109/GIIS.2016.7814935〉. 〈hal-01543398〉

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