TY - GEN
T1 - Toward dynamic computation offloading for data processing in vehicular fog based F-RAN
AU - Ye, Tianpeng
AU - Lin, Xiang
AU - Wu, Jun
AU - Li, Gaolei
AU - Li, Jianhua
N1 - Funding Information:
This work was supported in part by the Foundation of China under Grant Z01TYFC0821305, 2016QY03D0604 and partially supported by the SCST Project Number 18511105902 Foundation.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - With the rapid development of autonomous driving technology and Internet of Vehicle (IoV), massive traffic data need to be collected and processed in real time. Therefore, low latency data transmission and processing requirement are presented. Under the circumstances, the Fog Computing is proposed and seen as an emerging paradigm that extends the data processing towards the network edge. However, the unbalanced data processing requirement caused by the uneven distribution of vehicles in time and space limits the service capability. To enhance the flexibility and data processing capability, we propose a hybrid fog architecture which composed by fog computing radio access network (F-RAN) and Vehicular Fog Computing (VFC), which is called VF-based F-RAN. In addition, we propose a heuristic algorithm to optimize the computation offloading in this hybrid architecture. The simulation result reveals that the proposed hybrid fog architecture with the heuristic algorithm can effectively improve the data processing efficiency.
AB - With the rapid development of autonomous driving technology and Internet of Vehicle (IoV), massive traffic data need to be collected and processed in real time. Therefore, low latency data transmission and processing requirement are presented. Under the circumstances, the Fog Computing is proposed and seen as an emerging paradigm that extends the data processing towards the network edge. However, the unbalanced data processing requirement caused by the uneven distribution of vehicles in time and space limits the service capability. To enhance the flexibility and data processing capability, we propose a hybrid fog architecture which composed by fog computing radio access network (F-RAN) and Vehicular Fog Computing (VFC), which is called VF-based F-RAN. In addition, we propose a heuristic algorithm to optimize the computation offloading in this hybrid architecture. The simulation result reveals that the proposed hybrid fog architecture with the heuristic algorithm can effectively improve the data processing efficiency.
KW - Computation offloading
KW - F-RAN
KW - Heuristic algorithm
KW - Tabu search
KW - Vehicular fog
UR - http://www.scopus.com/inward/record.url?scp=85077112464&partnerID=8YFLogxK
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U2 - 10.1109/DSC.2019.00037
DO - 10.1109/DSC.2019.00037
M3 - Conference contribution
AN - SCOPUS:85077112464
T3 - Proceedings - 2019 IEEE 4th International Conference on Data Science in Cyberspace, DSC 2019
SP - 196
EP - 201
BT - Proceedings - 2019 IEEE 4th International Conference on Data Science in Cyberspace, DSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Data Science in Cyberspace, DSC 2019
Y2 - 23 June 2019 through 25 June 2019
ER -