TY - JOUR
T1 - Intelligence-sharing vehicular networks with mobile edge computing and spatiotemporal knowledge transfer
AU - Guo, Jie
AU - Luo, Wenwen
AU - Song, Bin
AU - Yu, Fei Richard
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Based on recent advances in MEC and knowledge transfer in artificial intelligence, we propose a novel framework named ISVN, in which the intelligence of different MEC servers can be shared to improve performance. Specifically, we present the main techniques in the ISVN framework, including aggregation and representation for context features, relationship mining and reasoning, and knowledge transfer among MEC servers. The results of object detection experiments with the proposed ISVN framework are presented. By taking advantage of MEC and knowledge transfer, the processing speed and accuracy of object detection can be significantly improved in different scenarios of vehicular networks.
AB - Based on recent advances in MEC and knowledge transfer in artificial intelligence, we propose a novel framework named ISVN, in which the intelligence of different MEC servers can be shared to improve performance. Specifically, we present the main techniques in the ISVN framework, including aggregation and representation for context features, relationship mining and reasoning, and knowledge transfer among MEC servers. The results of object detection experiments with the proposed ISVN framework are presented. By taking advantage of MEC and knowledge transfer, the processing speed and accuracy of object detection can be significantly improved in different scenarios of vehicular networks.
UR - http://www.scopus.com/inward/record.url?scp=85082559421&partnerID=8YFLogxK
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U2 - 10.1109/MNET.001.1900512
DO - 10.1109/MNET.001.1900512
M3 - Article
AN - SCOPUS:85082559421
SN - 0890-8044
VL - 34
SP - 256
EP - 262
JO - IEEE Network
JF - IEEE Network
IS - 4
M1 - 9048616
ER -