TY - GEN
T1 - Reliable Data Dissemination Strategy based on Systematic Network Coding in V2I Networks
AU - Kwon, Jungmin
AU - Park, Hyunggon
N1 - Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00024, Supervised Agile Machine Learning Techniques for Network Automation based on Network Data Analytics Function) and supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2B4005041).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In this paper, we propose a real-time data dissemination system based on systematic network coding (SNC) in Vehicular to infrastructure (V2I) network. We consider the delivery of data broadcasted over packet erasure V2I channels where the speed of vehicles changes over time. Unlike conventional existing SNC, we propose an encoding algorithm that adaptively determines how many packets need to be network coded together while explicitly considering traffic state. Specifically, we adopt regression models for estimating traffic states and show that the proposed approach outperforms in terms of decoding error rate in time-varying traffic. Our experiment results using actual highway traffic data show that the decoding error rate of the proposed approach can be reduced by 11.11% and thus confirm the effectiveness and reliability.
AB - In this paper, we propose a real-time data dissemination system based on systematic network coding (SNC) in Vehicular to infrastructure (V2I) network. We consider the delivery of data broadcasted over packet erasure V2I channels where the speed of vehicles changes over time. Unlike conventional existing SNC, we propose an encoding algorithm that adaptively determines how many packets need to be network coded together while explicitly considering traffic state. Specifically, we adopt regression models for estimating traffic states and show that the proposed approach outperforms in terms of decoding error rate in time-varying traffic. Our experiment results using actual highway traffic data show that the decoding error rate of the proposed approach can be reduced by 11.11% and thus confirm the effectiveness and reliability.
KW - broadcast
KW - regression model
KW - Systematic network coding (SNC)
KW - traffic estimation
KW - vehicle to infrastructure (V2I) network
UR - http://www.scopus.com/inward/record.url?scp=85078268714&partnerID=8YFLogxK
U2 - 10.1109/ICTC46691.2019.8939745
DO - 10.1109/ICTC46691.2019.8939745
M3 - Conference contribution
AN - SCOPUS:85078268714
T3 - ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
SP - 744
EP - 746
BT - ICTC 2019 - 10th International Conference on ICT Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information and Communication Technology Convergence, ICTC 2019
Y2 - 16 October 2019 through 18 October 2019
ER -