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Construction and efficiency analysis of an embedded system-based verification platform for edge computing.

Junjie Cao1,2, Zhiyong Yu3, Baohong Zhu4

  • 1Rocket Force University of Engineering, Xi'an, 710025, P. R. China.

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|July 18, 2025
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Summary
This summary is machine-generated.

This study introduces a physical verification platform for edge computing, addressing simulation limitations. The platform, using embedded devices and cloud services, optimizes offloading strategies for reduced latency and energy consumption.

Keywords:
Edge computingEmbedded systemOffloading strategyVerification platform

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Internet of Things

Background:

  • Edge computing is a novel paradigm driven by IoT, big data, and AI.
  • Existing simulation platforms for edge computing lack real-world accuracy and scalability.
  • Discrepancies in device interaction, physical attributes, and real-time responsiveness limit simulation platforms.

Purpose of the Study:

  • To introduce an innovative physical verification platform for edge computing using embedded devices.
  • To overcome the limitations of current edge computing simulation platforms.
  • To enhance the accuracy and applicability of edge computing testing.

Main Methods:

  • Developed a physical verification platform integrating KubeEdge and Serverless frameworks.
  • Utilized Alibaba Cloud for enhanced system stability and scalability.
  • Established a realistic testing environment with embedded devices like Raspberry Pi.
  • Conducted experimental validations focusing on offloading strategies.

Main Results:

  • The platform demonstrated dynamic resource allocation and efficient utilization.
  • Experimental results showed significant improvements in latency, energy consumption, and load balancing.
  • The refined offloading approach yielded outstanding performance metrics.

Conclusions:

  • The developed platform is sound, reliable, and versatile for various edge computing applications.
  • It effectively addresses the limitations of traditional simulation platforms.
  • The findings highlight the platform's potential for real-world edge computing deployments.