Broadcasting with Prediction and Selective Forwarding in Vehicular Networks
Broadcasting in vehicular networks has attracted great interest in research community and industry. Broadcasting on disseminating information to individual vehicle beyond the transmission range is based on inter-vehicle communication systems. It is crucial to broadcast messages to other vehicles as...
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| Format: | Article |
| Language: | English |
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Wiley
2013-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/309041 |
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| _version_ | 1832559310384660480 |
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| author | Jianjun Yang Zongming Fei |
| author_facet | Jianjun Yang Zongming Fei |
| author_sort | Jianjun Yang |
| collection | DOAJ |
| description | Broadcasting in vehicular networks has attracted great interest in research community and industry. Broadcasting on disseminating information to individual vehicle beyond the transmission range is based on inter-vehicle communication systems. It is crucial to broadcast messages to other vehicles as fast as possible because the messages in vehicle communication systems are often emergency messages such as accident warning or alarm. In many current approaches, the message initiator or sender selects the node among its neighbors that is farthest away from it in the broadcasting direction and then assigns the node to rebroadcast the message once the node gets out of its range or after a particular time slot. However, this approach may select a nonoptimal candidate because it does not consider the moving status of vehicles including their moving directions and speeds. In this paper, we develop a new approach based on prediction of future velocity and selective forwarding. The current message sender selects the best candidate that will rebroadcast the message to other vehicles as fast as possible. Key to the decision making is to consider the candidates' previous moving status and predict the future moving trends of the candidates so that the message is spread out faster. In addition, this approach generates very low overhead. Simulations demonstrate that our approach significantly decreases end-to-end delay and improves message delivery ratio. |
| format | Article |
| id | doaj-art-ce197da088534772bf880bd1fd424d83 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ce197da088534772bf880bd1fd424d832025-02-03T01:30:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-12-01910.1155/2013/309041309041Broadcasting with Prediction and Selective Forwarding in Vehicular NetworksJianjun Yang0Zongming Fei1 Department of Computer Science, University of North Georgia, Oakwood, GA 30566, USA The Laboratory for Advanced Networking, Department of Computer Science, University of Kentucky, Lexington, KY 40506, USABroadcasting in vehicular networks has attracted great interest in research community and industry. Broadcasting on disseminating information to individual vehicle beyond the transmission range is based on inter-vehicle communication systems. It is crucial to broadcast messages to other vehicles as fast as possible because the messages in vehicle communication systems are often emergency messages such as accident warning or alarm. In many current approaches, the message initiator or sender selects the node among its neighbors that is farthest away from it in the broadcasting direction and then assigns the node to rebroadcast the message once the node gets out of its range or after a particular time slot. However, this approach may select a nonoptimal candidate because it does not consider the moving status of vehicles including their moving directions and speeds. In this paper, we develop a new approach based on prediction of future velocity and selective forwarding. The current message sender selects the best candidate that will rebroadcast the message to other vehicles as fast as possible. Key to the decision making is to consider the candidates' previous moving status and predict the future moving trends of the candidates so that the message is spread out faster. In addition, this approach generates very low overhead. Simulations demonstrate that our approach significantly decreases end-to-end delay and improves message delivery ratio.https://doi.org/10.1155/2013/309041 |
| spellingShingle | Jianjun Yang Zongming Fei Broadcasting with Prediction and Selective Forwarding in Vehicular Networks International Journal of Distributed Sensor Networks |
| title | Broadcasting with Prediction and Selective Forwarding in Vehicular Networks |
| title_full | Broadcasting with Prediction and Selective Forwarding in Vehicular Networks |
| title_fullStr | Broadcasting with Prediction and Selective Forwarding in Vehicular Networks |
| title_full_unstemmed | Broadcasting with Prediction and Selective Forwarding in Vehicular Networks |
| title_short | Broadcasting with Prediction and Selective Forwarding in Vehicular Networks |
| title_sort | broadcasting with prediction and selective forwarding in vehicular networks |
| url | https://doi.org/10.1155/2013/309041 |
| work_keys_str_mv | AT jianjunyang broadcastingwithpredictionandselectiveforwardinginvehicularnetworks AT zongmingfei broadcastingwithpredictionandselectiveforwardinginvehicularnetworks |