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MDDV: a mobility-centric data dissemination algorithm for vehicular networks

Wu, Hao, Fujimoto, Richard, Guensler, Randall, Hunter, Michael·2004·Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks

Abstract

There has been increasing interest in the exploitation of advances in information technology in surface transportation systems. One trend is to exploit on-board sensing, computing and communication capabilities in vehicles, e.g., to augment and enhance existing intelligent transportation systems. A natural approach is to use vehicle-to-vehicle communications to disseminate information. In this paper, we propose MDDV, a mobility-centric approach for data dissemination in vehicular networks designed to operate efficiently and reliably despite the highly mobile, partitioned nature of these networks. MDDV is designed to exploit vehicle mobility for data dissemination, and combines the idea of opportunistic forwarding, trajectory based forwarding and geographical forwarding. We develop a generic mobile computing approach for designing localized algorithms in vehicular networks. Vehicles perform local operations based on their own knowledge while they collectively achieve a global behavior. We evaluate the performance of the MDDV algorithm using realistic simulation of the vehicle traffic in Atlanta area.

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Metadata

Authors
Wu, H., Fujimoto, R., Guensler, R., & Hunter, M.
Venue
Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks
Publisher
ACM
Published
2004-10
Pages
47-56
OpenAlex
W2060067830
Cited by
465
References
0
FWCI
28.57
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: MDDV: a mobility-centric data dissemination algorithm for vehicular networks. https://app.cochat.ai/papers/10.1145/1023875.1023884
The paper
Wu, H., Fujimoto, R., Guensler, R., & Hunter, M. (2004). MDDV: a mobility-centric data dissemination algorithm for vehicular networks. Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks. https://doi.org/10.1145/1023875.1023884

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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.