The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks
Abstract
Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.
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Citations
Cites
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244
Cloud computing: state-of-the-art and research challenges
Zhang, Q., Cheng, L., & Boutaba, R. (2010) · Journal of Internet Services and Applications · doi:10.1007/s13174-010-0007-6 · Cited by 3,515
Vehicular ad hoc networks (VANETS): status, results, and challenges
Zeadally, S., Hunt, R., Chen, Y.-S., Irwin, A., & Hassan, A. (2012) · Telecommunication Systems · doi:10.1007/s11235-010-9400-5 · Cited by 1,208
Self-citationRethinking Vehicular Communications: Merging VANET with cloud computing
Hussain, R., Son, J., Eun, H., Kim, S., & Oh, H. (2012) · 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings · doi:10.1109/cloudcom.2012.6427481 · Cited by 279
The Next Paradigm Shift: From Vehicular Networks to Vehicular Clouds
Olariu, S., Hristov, T. S., & Yan, G. (2013) · Wiley eBooks · doi:10.1002/9781118511305.ch19 · Cited by 162
Abid, H., Phuong, L. T. T., Wang, J., Lee, S., & Qaisar, S. (2011) · International Symposium on Applied Sciences in Biomedical and Communication Technologies · doi:10.1145/2093698.2093863 · Cited by 101
Sarathy, V., Narayan, P., & Mikkilineni, R. (2010) · doi:10.1109/wetice.2010.14 · Cited by 95
Finding a STAR in a Vehicular Cloud
Mershad, K., & Artail, H. A. (2013) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2013.2240041 · Cited by 93
A cloud-assisted design for autonomous driving
Kumar, S., Gollakota, S., & Katabi, D. (2012) · doi:10.1145/2342509.2342519 · Cited by 91
QoS Swarm Bee Routing Protocol for Vehicular Ad Hoc Networks
Bitam, S., & Mellouk, A. (2011) · International Conference on Communications · doi:10.1109/icc.2011.5963424 · Cited by 60
Self-citation
Cited by
Evolutionary V2X Technologies Toward the Internet of Vehicles: Challenges and Opportunities
Zhou, H., Xu, W., Chen, J., & Wang, W. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2961937 · Cited by 655
Autonomous Cars: Research Results, Issues, and Future Challenges
Hussain, R., & Zeadally, S. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2869360 · Cited by 631
Self-citationAdaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems
Sun, Y., Guo, X., Song, J., Zhou, S., Jiang, Z., Liu, X., & Niu, Z. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2895593 · Cited by 369
Arthurs, P., Gillam, L., Krause, P., Wang, N., Halder, K., & Mouzakitis, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3084396 · Cited by 339
Naranjo, P. G. V., Shojafar, M., Mostafaei, H., Pooranian, Z., & Baccarelli, E. (2017) · The Journal of Supercomputing · doi:10.1007/s11227-016-1785-9 · Cited by 286
Metadata
- Authors
- Bitam, S., Mellouk, A., & Zeadally, S.
- Venue
- IEEE Wireless Communications
- Published
- 2015-02
- Volume
- 22
- Issue
- 1
- Pages
- 96-102
- ISSN
- 1536-1284 (ISSN-L)
- OpenAlex
- W1995653314
- Cited by
- 321
- References
- 13
- FWCI
- 16.52
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
