Journal
IEEE NETWORK
Volume 34, Issue 4, Pages 256-262Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.001.1900512
Keywords
Servers; Knowledge transfer; Deep learning; Task analysis; Spatiotemporal phenomena; Cloud computing; Sensors
Categories
Funding
- National Natural Science Foundation of China [61772387, 61802296, 61906156]
- Fundamental Research Funds for the Central Universities [JB180101]
- China Postdoctoral Science Foundation [2017M620438]
- Fundamental Research Funds of the Ministry of Education [MCM20170202]
- National Natural Science Foundation of Shaanxi Province [2019ZDLGY03-03, 2019JQ-375]
- ISN State Key Laboratory
- Fundamental Research Funds of China Mobile [MCM20170202]
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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.
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