AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks
- Title
- AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks
- Creator
- Kanna R.R.; Priya T.M.; Sivakumar V.; Nataraj C.; Musa A.I.A.; Devi M.R.
- Description
- The proposed research presents a two-fold approach for advancing Vehicular Ad-Hoc Networks (VANETs). Firstly, it introduces a Residual Convolutional Neural Network (RCNN) architecture to extract real-time traffic data features, enabling accurate traffic flow prediction and hazard identification. The RCNN model, trained and tested on real- world data, outperforms existing models in both accuracy and efficiency, promising improved road safety and traffic management within VANETs. Secondly, the study introduces a Genetic Algorithm-enhanced Convolutional Neural Network (GACNN) routing algorithm, challenging traditional VANET routing methods with metaheuristic techniques. Experiments in various VANET network scenarios confirm GACNN's superior performance over existing routing protocols, marking a significant step toward more efficient and adaptive VANET traffic management. 2024 Author(s).
- Source
- AIP Conference Proceedings, Vol-3161, No. 1
- Date
- 2024-01-01
- Publisher
- American Institute of Physics
- Coverage
- Kanna R.R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Priya T.M., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Sivakumar V., Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Nataraj C., Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Musa A.I.A., Department of Computer Science, College of Computer, Qassim University, Riyadh, Saudi Arabia; Devi M.R., School of Information Science, Presidency University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 0094243X
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kanna R.R.; Priya T.M.; Sivakumar V.; Nataraj C.; Musa A.I.A.; Devi M.R., “AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/18954.