Spatio-temporal Information Ranking in VANET Applications

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Published Jul 1, 2010
https://doi.org/10.47164/ijngc.v1i1.5
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Volume 1, Issue 1, Inaugural Issue 2010

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Piotr Szczurek
University of Illinois at Chicago
Bo Xu
University of Illinois at Chicago
Jie (Jane) Lin
University of Illinois at Chicago
Ouri Wolfson
University of Illinois at Chicago

Abstract

Vehicular ad-hoc networks (VANETs) is a promising approach to the dissemination of spatio-temporal information such as the current traffic condition of a road segment or the availability of a parking space. Due to the constraint of the communication bandwidth, only a limited number of information items may be transmitted upon a vehicle-to- vehicle communication opportunity. Ranking becomes critical in this situation, by enabling the most important information to be transmitted under the bandwidth constraint. In this paper we propose a method for online learning of spatio-temporal information ranking in VANETs. In this method, mobile nodes such as vehicles judge the relevance of incoming information items and use them as training examples for Naive Bayesian learning. Additionally, a separate machine learning algorithm is used to estimate the probability of a duplicate item being transmitted. The method is used in place of commonly used heuristics, and is evaluated for travel time and parking availability dissemination applications.

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How to Cite
Piotr Szczurek, Bo Xu, Jie (Jane) Lin, & Ouri Wolfson. (2010). Spatio-temporal Information Ranking in VANET Applications. International Journal of Next-Generation Computing, 1(1), 54–72. https://doi.org/10.47164/ijngc.v1i1.5
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