Prediction-based Opportunistic Greedy Routing for Vehicular Ad Hoc Networks
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KAMAL, Joarder Mohammad Mustafa, HASAN, Mohammad, GRIFFITHS, Alison and YU, Hongnian (2010) Prediction-based Opportunistic Greedy Routing for Vehicular Ad Hoc Networks. In: The 16th International Conference on Automation and Computing, September, 2010, Birmingham, UK.
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POGR.Vanet-June.2010 ICAC 10.pdf Download (249kB) | Preview |
Abstract or description
In recent years, intelligent transportation systems (ITS) applications e.g. active traffic management, safety application, etc. are gaining popularity. Again internet-based services are also emerging nowadays in vehicular communication networks. Therefore, a suitable routing mechanism is essential to support delay tolerant networking to ensure reliable information exchange. On the other hand, geographical knowledge discovery based on mobility information is another novel approach which has great opportunity to improve existing networking procedures for vehicular communication. In this paper, a Prediction-based Opportunistic Greedy Routing (POGR) algorithm is proposed which utilises mobility data mining to facilitate routing decision in vehicular communications. Hybrid network architecture is considered where both Vehicles-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications may able to take place. The proposed algorithm follows the IEEE WAVE trial use standards as well as the EU GeoPKDD project deliverables. A detail description of the framework, algorithms and arguments with few illustrations are presented.
| Item Type: | Conference or Workshop Item (Paper) |
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| Uncontrolled Keywords: | mobility data mining; VANET routing; ITS; WAVE |
| Subjects: | G400 Computer Science |
| Faculty: | Previous Faculty of Computing, Engineering and Sciences > Computing |
| Event Title: | The 16th International Conference on Automation and Computing |
| Event Location: | Birmingham, UK |
| Event Dates: | September, 2010 |
| Depositing User: | Alison GRIFFITHS |
| Date Deposited: | 02 May 2013 22:48 |
| Last Modified: | 24 Feb 2023 13:37 |
| URI: | https://eprints.staffs.ac.uk/id/eprint/863 |
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