An Optimized Flow Allocation in Vehicular Cloud
In this paper, a vehicular cloud (VC) model is adopted where vehicles offer data as a service. We propose solutions for efficient data delivery based on transmission scheduling methods where vehicles gather data from their mounted sensors. This is done by first organizing vehicles into clusters, so...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2016-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7585028/ |
| _version_ | 1818369986515697664 |
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| author | Meysam Azizian Soumaya Cherkaoui Abdelhakim Hafid |
| author_facet | Meysam Azizian Soumaya Cherkaoui Abdelhakim Hafid |
| author_sort | Meysam Azizian |
| collection | DOAJ |
| description | In this paper, a vehicular cloud (VC) model is adopted where vehicles offer data as a service. We propose solutions for efficient data delivery based on transmission scheduling methods where vehicles gather data from their mounted sensors. This is done by first organizing vehicles into clusters, so that each cluster works as VC. A distributed D-hop cluster formation algorithm is presented to dynamically form vehicle clouds. The algorithm groups vehicles into non-overlapping clusters, which have adaptive sizes according to their mobility. VCs are created in such a way that each vehicle is at most D-hops away from a cloud coordinator (broker). Each vehicle chooses its broker based on relative mobility calculations within its D-hop neighbors. After cloud construction, a mathematical optimization scheduling algorithm is used to maximize throughput and minimize delay in delivering data from vehicles to their VC broker. Our proposed optimization model implements a contention-free-based medium access control where physical conditions of the channel are fully analyzed. Extensive simulations were performed for different scenarios to evaluate the performance of the proposed cloud formation and cloud-based transmission scheduling algorithms. Results show that VCs formed by our algorithms are more stable and provide higher data throughputs compared with others. |
| first_indexed | 2024-12-13T23:32:33Z |
| format | Article |
| id | doaj.art-14a05863b95443a2a56a0f6ce28900ee |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:32:33Z |
| publishDate | 2016-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-14a05863b95443a2a56a0f6ce28900ee2022-12-21T23:27:23ZengIEEEIEEE Access2169-35362016-01-0146766677910.1109/ACCESS.2016.26153237585028An Optimized Flow Allocation in Vehicular CloudMeysam Azizian0https://orcid.org/0000-0002-7706-6361Soumaya Cherkaoui1Abdelhakim Hafid2INTERLAB Research Laboratory, Université de Sherbrooke, Sherbrooke, QC, CanadaINTERLAB Research Laboratory, Université de Sherbrooke, Sherbrooke, QC, CanadaNetwork Research Labaratory, University of Montreal, Montreal, QC, CanadaIn this paper, a vehicular cloud (VC) model is adopted where vehicles offer data as a service. We propose solutions for efficient data delivery based on transmission scheduling methods where vehicles gather data from their mounted sensors. This is done by first organizing vehicles into clusters, so that each cluster works as VC. A distributed D-hop cluster formation algorithm is presented to dynamically form vehicle clouds. The algorithm groups vehicles into non-overlapping clusters, which have adaptive sizes according to their mobility. VCs are created in such a way that each vehicle is at most D-hops away from a cloud coordinator (broker). Each vehicle chooses its broker based on relative mobility calculations within its D-hop neighbors. After cloud construction, a mathematical optimization scheduling algorithm is used to maximize throughput and minimize delay in delivering data from vehicles to their VC broker. Our proposed optimization model implements a contention-free-based medium access control where physical conditions of the channel are fully analyzed. Extensive simulations were performed for different scenarios to evaluate the performance of the proposed cloud formation and cloud-based transmission scheduling algorithms. Results show that VCs formed by our algorithms are more stable and provide higher data throughputs compared with others.https://ieeexplore.ieee.org/document/7585028/Cloud formationtransmission schedulingvehicular cloudVANEToptimization |
| spellingShingle | Meysam Azizian Soumaya Cherkaoui Abdelhakim Hafid An Optimized Flow Allocation in Vehicular Cloud IEEE Access Cloud formation transmission scheduling vehicular cloud VANET optimization |
| title | An Optimized Flow Allocation in Vehicular Cloud |
| title_full | An Optimized Flow Allocation in Vehicular Cloud |
| title_fullStr | An Optimized Flow Allocation in Vehicular Cloud |
| title_full_unstemmed | An Optimized Flow Allocation in Vehicular Cloud |
| title_short | An Optimized Flow Allocation in Vehicular Cloud |
| title_sort | optimized flow allocation in vehicular cloud |
| topic | Cloud formation transmission scheduling vehicular cloud VANET optimization |
| url | https://ieeexplore.ieee.org/document/7585028/ |
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