University of Oulu

Hong Zhang, Penghai Wang, Shouhua Zhang, Zihan Wu. An adaptive offloading framework for license plate detection in collaborative edge and cloud computing[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2793-2814. doi: 10.3934/mbe.2023131

An adaptive offloading framework for license plate detection in collaborative edge and cloud computing

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Author: Zhang, Hong1; Wang, Penghai1; Zhang, Shouhua2;
Organizations: 1School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China
2Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
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Language: English
Published: American Institute of Mathematical Sciences, 2022
Publish Date: 2023-08-09


With the explosive growth of edge computing, huge amounts of data are being generated in billions of edge devices. It is really difficult to balance detection efficiency and detection accuracy at the same time for object detection on multiple edge devices. However, there are few studies to investigate and improve the collaboration between cloud computing and edge computing considering realistic challenges, such as limited computation capacities, network congestion and long latency. To tackle these challenges, we propose a new multi-model license plate detection hybrid methodology with the tradeoff between efficiency and accuracy to process the tasks of license plate detection at the edge nodes and the cloud server. We also design a new probability-based offloading initialization algorithm that not only obtains reasonable initial solutions but also facilitates the accuracy of license plate detection. In addition, we introduce an adaptive offloading framework by gravitational genetic searching algorithm (GGSA), which can comprehensively consider influential factors such as license plate detection time, queuing time, energy consumption, image quality, and accuracy. GGSA is helpful for Quality-of-Service (QoS) enhancement. Extensive experiments show that our proposed GGSA offloading framework exhibits good performance in collaborative edge and cloud computing of license plate detection compared with other methods. It demonstrate that when compared with traditional all tasks are executed on the cloud server (AC), the offloading effect of GGSA can be improved by 50.31%. Besides, the offloading framework has strong portability when making real-time offloading decisions.

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Series: Mathematical biosciences and engineering
ISSN: 1547-1063
ISSN-E: 1551-0018
ISSN-L: 1547-1063
Volume: 20
Issue: 2
Pages: 2793 - 2814
DOI: 10.3934/mbe.2023131
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by Science and Technology Research Project of Hebei Higher Education Institutions (No. QN2020133), the Natural Science Foundation of Hebei Province of China (No. F2019201361).
Copyright information: © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (