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

Hong Zhang1, Penghai Wang1, Shouhua Zhang2

  • 1School of Cyber Security and Computer, Hebei University, Baoding, Hebei, China.

Mathematical Biosciences and Engineering : MBE
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid approach for license plate detection, balancing efficiency and accuracy in edge and cloud computing. The proposed Gravitational Genetic Searching Algorithm (GGSA) framework enhances Quality-of-Service (QoS) by optimizing task offloading.

Keywords:
edge computinginternet of thingsresource allocationtask offloading

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Area of Science:

  • Edge computing
  • Cloud computing
  • Object detection

Background:

  • Edge devices generate vast data, posing challenges in balancing detection efficiency and accuracy.
  • Limited computation, network congestion, and latency hinder effective collaboration between edge and cloud computing for object detection tasks.

Purpose of the Study:

  • To propose a hybrid methodology for license plate detection that optimizes the trade-off between efficiency and accuracy.
  • To design a probability-based offloading algorithm for improved initial solutions and detection accuracy.
  • To introduce an adaptive offloading framework (GGSA) for enhancing Quality-of-Service (QoS) in collaborative edge-cloud systems.

Main Methods:

  • Developed a multi-model hybrid methodology for license plate detection across edge and cloud nodes.
  • Designed a probability-based offloading initialization algorithm.
  • Introduced an adaptive offloading framework using Gravitational Genetic Searching Algorithm (GGSA) considering time, queuing, energy, image quality, and accuracy.

Main Results:

  • The GGSA offloading framework demonstrated significant performance improvements in collaborative edge-cloud license plate detection.
  • Achieved a 50.31% improvement in offloading effect compared to the traditional 'all tasks on cloud' (AC) approach.
  • The framework showed strong portability for real-time offloading decisions.

Conclusions:

  • The proposed GGSA framework effectively balances efficiency and accuracy in edge-cloud license plate detection.
  • GGSA enhances Quality-of-Service (QoS) by adaptively managing offloading decisions.
  • The methodology offers a robust and portable solution for real-time collaborative computing challenges.