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Lookup NU author(s): Dr Bo WeiORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Institute of Electrical and Electronics Engineers Inc., 2023.
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IEEERecently we have witnessed the rise of Artificial Intelligence of Things (AIoT) and the shift of sensing paradigm from cloud-centric to the edge-centric, which effectively improves the sensing capability of intelligence transportation systems. To improve the real-time sensing performance, in this work we propose an ensemble sensing based scheme to solve the time-constraint synchronized inference problem and achieve robust inference with heterogeneous IoT devices in intelligence transportation systems. We design and implement Ensen, which incorporates various novel techniques such as customized DNN model design, KD-based model training, and dynamic deep ensemble management, etc., to achieve improved accuracy and maximize the computational resource usage of the whole sensing group. Extensive evaluations on different types of common IoT devices have shown that Ensen achieves a robust performance and can be easily extended to different types of convolutional neural networks.
Author(s): Feng X, Luo C, Wei B, Zhang J, Li J, Wang H, Xu W, Chan MC, Leung VCM
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2023
Volume: 24
Issue: 11
Pages: 12949-2960
Print publication date: 01/11/2023
Online publication date: 06/05/2022
Acceptance date: 30/03/2022
Date deposited: 19/06/2023
ISSN (print): 1524-9050
ISSN (electronic): 1558-0016
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/TITS.2022.3170028
DOI: 10.1109/TITS.2022.3170028
ePrints DOI: 10.57711/93kw-b770
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