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Applying Neural Network on the VANETs Routing Protocol for next-hop selection
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Young-June Choi | - |
| dc.contributor.author | Chen Junyao | - |
| dc.date.accessioned | 2022-11-29T02:32:28Z | - |
| dc.date.available | 2022-11-29T02:32:28Z | - |
| dc.date.issued | 2021-02 | - |
| dc.identifier.other | 30665 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/19994 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :인공지능학과,2021. 2 | - |
| dc.description.tableofcontents | Chapter I. Introduction 1 A. Intelligent transportation system and VANETs 1 B. Artificial Neural network 3 C. Motivation 4 D. Contributions and Outlines 5 Chapter II. Background Discussion 7 A. Metrics of VANETs Routing Protocol 7 B. Categories of VANETs Routing Protocol 7 C. Summary and Analysis 13 Chapter III. Proposed Idea 15 A. Main Idea 15 B. Parameter 16 C. Structure of Convolutional Neural Network 18 D. Collection of Training Set 19 E. Process of NN-GPSR 20 Chapter IV. Experimental Results 22 A. Verification of CNN 22 B. Simulation result 23 Chapter V. Conclusion and Future Work 26 A. Conclusion 26 B. Future work 26 Chapter VI. References 28 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Applying Neural Network on the VANETs Routing Protocol for next-hop selection | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 인공지능학과 | - |
| dc.date.awarded | 2021. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1202867 | - |
| dc.identifier.uci | I804:41038-000000030665 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000030665 | - |
| dc.subject.keyword | VANETs Routing Protocol | - |
| dc.description.alternativeAbstract | The unpredictability and ever changing network topology of VANETs evolve to the biggest challenge of determining the optimal path for the networks. The geographic information-based routing protocol is an important branch of the routing protocol for VANETs. A classic routing protocol in that area is GPSR and a modified version of this is DVA-GPSR. We analyzed the shortcomings of GPSR and DVA-GPSR, and proposed a new routing protocol for VANETs which is based on a neural network. We designed a neural network model and used the node parameter data from the global optimal path to train the neural network. The neural network learns how to choose the better next-hop, in order to overcome the local maximum congestion problem, and improve the network efficiency. In response to the problem of no public dataset, we have established our simulation database. For the input matrix features, a targeted neural network structure model is designed, and we verified the neural network model on our dataset. The verification results prove that our model is applicable, and the accuracy rate is 99%. Compared with GPSR and DVA-GPSR, the protocol proposed in this paper has lower latency and higher packet delivery ratio. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Artificial Intelligence > 3. Theses(Master)
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