A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks
In vehicular ad hoc networks (VANETs), network topology and communication links frequently change due to the high mobility of vehicles. Key challenges include how to shorten transmission delays and increase the stability of transmissions. When establishing routing paths, most research focuses on det...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/374391 |
| _version_ | 1797709164289458176 |
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| author | Wei Kuang Lai Mei-Tso Lin Yu-Hsuan Yang |
| author_facet | Wei Kuang Lai Mei-Tso Lin Yu-Hsuan Yang |
| author_sort | Wei Kuang Lai |
| collection | DOAJ |
| description | In vehicular ad hoc networks (VANETs), network topology and communication links frequently change due to the high mobility of vehicles. Key challenges include how to shorten transmission delays and increase the stability of transmissions. When establishing routing paths, most research focuses on detecting traffic and selecting roads with higher vehicle densities in order to transmit packets, thus avoiding carry-and-forward scenarios and decreasing transmission delays; however, such approaches may not obtain accurate real-time traffic densities by periodically monitoring each road because vehicle densities change so rapidly. In this paper, we propose a novel routing information system called the machine learning-assisted route selection (MARS) system to estimate necessary information for routing protocols. In MARS, road information is maintained in roadside units with the help of machine learning. We use machine learning to predict the moves of vehicles and then choose some suitable routing paths with better transmission capacity to transmit packets. Further, MARS can help to decide the forwarding direction between two RSUs according to the predicted location of the destination and the estimated transmission delays in both forwarding directions. Our proposed system can provide in-time routing information for VANETs and greatly enhance network performance. |
| first_indexed | 2024-03-12T06:33:25Z |
| format | Article |
| id | doaj.art-cb94c220ca1c4d5796bbb0c05f615da5 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T06:33:25Z |
| publishDate | 2015-03-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-cb94c220ca1c4d5796bbb0c05f615da52023-09-03T01:32:04ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-03-011110.1155/2015/374391374391A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc NetworksWei Kuang LaiMei-Tso LinYu-Hsuan YangIn vehicular ad hoc networks (VANETs), network topology and communication links frequently change due to the high mobility of vehicles. Key challenges include how to shorten transmission delays and increase the stability of transmissions. When establishing routing paths, most research focuses on detecting traffic and selecting roads with higher vehicle densities in order to transmit packets, thus avoiding carry-and-forward scenarios and decreasing transmission delays; however, such approaches may not obtain accurate real-time traffic densities by periodically monitoring each road because vehicle densities change so rapidly. In this paper, we propose a novel routing information system called the machine learning-assisted route selection (MARS) system to estimate necessary information for routing protocols. In MARS, road information is maintained in roadside units with the help of machine learning. We use machine learning to predict the moves of vehicles and then choose some suitable routing paths with better transmission capacity to transmit packets. Further, MARS can help to decide the forwarding direction between two RSUs according to the predicted location of the destination and the estimated transmission delays in both forwarding directions. Our proposed system can provide in-time routing information for VANETs and greatly enhance network performance.https://doi.org/10.1155/2015/374391 |
| spellingShingle | Wei Kuang Lai Mei-Tso Lin Yu-Hsuan Yang A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks International Journal of Distributed Sensor Networks |
| title | A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks |
| title_full | A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks |
| title_fullStr | A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks |
| title_full_unstemmed | A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks |
| title_short | A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks |
| title_sort | machine learning system for routing decision making in urban vehicular ad hoc networks |
| url | https://doi.org/10.1155/2015/374391 |
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