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Time-Constrained Ensemble Sensing With Heterogeneous IoT Devices in Intelligent Transportation Systems

Lookup NU author(s): Dr Bo WeiORCiD

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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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Abstract

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.


Publication metadata

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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Funding

Funder referenceFunder name
2019A1515011608
61972263
62073225
20200810113310001
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province
Stable Support Plan for Higher Education Institutions in Shenzhen

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