Abstract
Traditional vehicular routing protocols cannot accurately foresee future location of each vehicle for efficient packet forwarding. Recently, the data mining approach has been applied to analyze huge vehicle trajectory data. In this paper, we propose a novel trajectory-based routing (NTR) protocol to improve the packet replication efficiency of vehicles in the Vehicular Delay Tolerant Network (VDTN). By integrating the data mining technique, NTR first establishes the trajectory tree of each vehicular node based on its trajectory data. With this kind of trajectory trees, NTR then predicts future location of each vehicle and derives its contact possibilities with all other vehicles. Hence, NTR creates the vehicle encounter trees and the packet delivery graphs to predict the optimal Store-Carry-Forward (SCF) route for spraying the optimal number of packet tokens among intermediate nodes from the packet source vehicle to the destination one. Finally, we use the Opportunistic Network Environment (ONE) simulator to perform simulations. From performance results, we conclude that NTR significantly outperforms several well-known VDTN routing protocols, in terms of the average packet delivery ratio, average end-to-end delay and average packet forwarding overhead.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 389-394 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538616451 |
| DOIs | |
| Publication status | Published - 2017 Nov 27 |
| Event | 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada Duration: 2017 Oct 5 → 2017 Oct 8 |
Publication series
| Name | 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 |
|---|---|
| Volume | 2017-January |
Other
| Other | 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 |
|---|---|
| Country | Canada |
| City | Banff |
| Period | 17-10-05 → 17-10-08 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Control and Optimization
Cite this
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NTR : An efficient trajectory-based routing protocol for the vehicular delay tolerant networks. / Chang, Ing-Chau; Hung, Ming Han; Chang, Ching Ru; Yen, Chin En.
2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 389-394 (2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017; Vol. 2017-January).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - NTR
T2 - An efficient trajectory-based routing protocol for the vehicular delay tolerant networks
AU - Chang, Ing-Chau
AU - Hung, Ming Han
AU - Chang, Ching Ru
AU - Yen, Chin En
PY - 2017/11/27
Y1 - 2017/11/27
N2 - Traditional vehicular routing protocols cannot accurately foresee future location of each vehicle for efficient packet forwarding. Recently, the data mining approach has been applied to analyze huge vehicle trajectory data. In this paper, we propose a novel trajectory-based routing (NTR) protocol to improve the packet replication efficiency of vehicles in the Vehicular Delay Tolerant Network (VDTN). By integrating the data mining technique, NTR first establishes the trajectory tree of each vehicular node based on its trajectory data. With this kind of trajectory trees, NTR then predicts future location of each vehicle and derives its contact possibilities with all other vehicles. Hence, NTR creates the vehicle encounter trees and the packet delivery graphs to predict the optimal Store-Carry-Forward (SCF) route for spraying the optimal number of packet tokens among intermediate nodes from the packet source vehicle to the destination one. Finally, we use the Opportunistic Network Environment (ONE) simulator to perform simulations. From performance results, we conclude that NTR significantly outperforms several well-known VDTN routing protocols, in terms of the average packet delivery ratio, average end-to-end delay and average packet forwarding overhead.
AB - Traditional vehicular routing protocols cannot accurately foresee future location of each vehicle for efficient packet forwarding. Recently, the data mining approach has been applied to analyze huge vehicle trajectory data. In this paper, we propose a novel trajectory-based routing (NTR) protocol to improve the packet replication efficiency of vehicles in the Vehicular Delay Tolerant Network (VDTN). By integrating the data mining technique, NTR first establishes the trajectory tree of each vehicular node based on its trajectory data. With this kind of trajectory trees, NTR then predicts future location of each vehicle and derives its contact possibilities with all other vehicles. Hence, NTR creates the vehicle encounter trees and the packet delivery graphs to predict the optimal Store-Carry-Forward (SCF) route for spraying the optimal number of packet tokens among intermediate nodes from the packet source vehicle to the destination one. Finally, we use the Opportunistic Network Environment (ONE) simulator to perform simulations. From performance results, we conclude that NTR significantly outperforms several well-known VDTN routing protocols, in terms of the average packet delivery ratio, average end-to-end delay and average packet forwarding overhead.
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UR - http://www.scopus.com/inward/citedby.url?scp=85044402932&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8122635
DO - 10.1109/SMC.2017.8122635
M3 - Conference contribution
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 389
EP - 394
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -