Earticle

초록

영어

Vehicular Adhoc Network (VANET) are vital in enhancing communication and safety in intelligent transportation systems, autonomous vehicles, and cooperative driving. Efficient and reliable data transmission in V2V scenarios is crucial for road safety and traffic management. However, high mobility and the dynamic nature of vehicular environments lead to challenges such as high packet loss, and packet queue length to mitigate network performance. To address these issues, we propose an innovative approach that leverages packet transmission data using weighted attention mechanism to predict packet flow and reduce congestion. We obtained a comprehensive dataset by integrating the H2V (Highway-to-Vehicle) based DSRC (Dedicated Short- Range Communication) protocol with the OMNET++ and SUMO simulators, encompassing network metrics such as queue length, packet loss and packet delay. The proposed LSTM approach shows promising results in predicting congestion patterns, enhancing the packet delivery ratio. Conclusively, our WDL-H2V model maintains a consistently high PDR to continuously mitigate congestion.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
III. PROPOSED FRAMEWORK
A. Calculation of Congestion Metric
A. Data Processing Pipeline
B. Weighted Function
C. Calculate Congestion
IV. RESULTS & DISCUSSION
A. LSTM Model
B. Result
V. CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES

저자

• Saqib Jamal Syed [ Department of Electronic Engineering Jeju National University ]
• Afaq Muhammad [ Department of Computer Engineering Jeju National University ]
• Wang-Cheol Song [ Department of Computer Engineering Jeju National University ] Corresponding author

참고문헌