Mining vehicular data in VANET

Vehicular networks can generate a large amount of vehicular data which are useful to both car consumers and manufacturers. In this paper we propose to collect vehicular data and to mine vehicular data by using data mining models. The experiment results shows the proposed methods are effective.

[1]  Francesco Bonchi,et al.  Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[2]  Gongjun Yan,et al.  Reliable Routing Protocols in Vehicular Ad hoc Networks , 2010 .

[3]  Raja Sengupta,et al.  Vehicle-Infrastructure Cooperation , 2009 .

[4]  Eric R. Ziegel,et al.  Generalized Linear Models , 2002, Technometrics.

[5]  R. Tyrrell Rockafellar,et al.  Lagrange Multipliers and Optimality , 1993, SIAM Rev..

[6]  Martin Klepal,et al.  Influence of Predicted and Measured Fingerprint on the Accuracy of RSSI-based Indoor Location Systems , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[7]  Gongjun Yan,et al.  Providing VANET position integrity through filtering , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[8]  Sang-il Ko,et al.  Performance Enhancement of Indoor Mobile Localization System using Unscented Kalman Filter , 2006, 2006 SICE-ICASE International Joint Conference.

[9]  Mario Gerla,et al.  Vehicular networks and the future of the mobile internet , 2011, Comput. Networks.

[10]  Gongjun Yan,et al.  Towards Providing Scalable and Robust Privacy in Vehicular Networks , 2014, IEEE Transactions on Parallel and Distributed Systems.

[11]  Gongjun Yan,et al.  Security challenges in vehicular cloud computing , 2013, IEEE Transactions on Intelligent Transportation Systems.

[12]  Yuh-Shyan Chen,et al.  Routing Protocols in Vehicular Ad Hoc Networks , 2010 .

[13]  Yuehua Wu,et al.  A Procedure for Estimating the Number of Clusters in Logistic Regression Clustering , 2009, J. Classif..