Mobility Prediction in Vehicular Networks: An Approach Through Hybrid Neural Networks Under Uncertainty-Reference-Cited by-同舟云学术

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Mobility Prediction in Vehicular Networks: An Approach Through Hybrid Neural Networks Under Uncertainty

Published:2017 Issue: Volume: Page:178-194
ISSN:0302-9743
Container-title:Mobile, Secure, and Programmable Networking
language:
Short-container-title:

Author:

Banerjee Soumya,Bouzefrane Samia,Mühlethaler Paul

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-67807-8_14

Reference15 articles.

1. Omar, H., Zhuang, W., Abdrabou, A., Li, L.: Performance evaluation of vemac supporting safety applications in vehicular networks. IEEE Trans. Emerg. Topics Comput. 1(1), 69–83 (2013)

2. Pack, S., Choi, Y.: Fast handoff scheme based on mobility prediction in public wireless LAN systems. IEEE Proc. Commun. 151(5), 489–495 (2004)

3. Yavas, G., Katsaros, D., Ulusoy, O., Manolopoulos, Y.: A data mining approach for location prediction in mobile environments. Data Knowl. Eng. 54(2), 121–146 (2005)

4. Wang, X., Wang, C., Cui, G., Yang, Q.: Practical link duration prediction model in vehicular ad hoc networks. Int. J. Dist. Sensor Networks, vol. 2015

5. Wu, W., Zhang, J., Luo, A., Cao, J.: Distributed mutual exclusion algorithms for intersection traffic control. IEEE Trans. Parallel Distrib. Syst. 26(1), 65–74 (2015)

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1. Efficient Future Prediction using Neural Network in Vehicular Ad hoc Networks;2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH);2022-05

2. Mobility Prediction issues and Challenges in Vehicular Ad-Hoc Networks;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2020-07-20

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