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An Efficient Resource Allocation Strategy for Edge-Computing Based Environmental Monitoring System.

Juan Fang1, Juntao Hu1, Jianhua Wei1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
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Edge computing enhances environmental monitoring by reducing task completion latency. Our new resource allocation and task scheduling methods significantly improve performance for environmental monitoring applications.

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edge computingenvironmental monitoringresource allocationtask scheduling

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Area of Science:

  • Environmental Science
  • Computer Science
  • Information Technology

Background:

  • Cloud computing and microsensor technology have transformed environmental monitoring.
  • Existing cloud-based systems struggle with the high computational demands of granular monitoring and expanding applications.
  • Edge computing offers a solution by distributing resources closer to data sources.

Purpose of the Study:

  • To address the limitations of current systems in environmental monitoring.
  • To propose novel algorithms for resource allocation and task scheduling tailored to environmental monitoring.
  • To reduce the average completion latency of environmental monitoring applications.

Main Methods:

  • Developed a resource allocation algorithm specifically for environmental monitoring systems.
  • Designed a task scheduling strategy that accounts for task dependencies.
  • Incorporated considerations for emergency tasks within the proposed methods.
  • Conducted simulations to evaluate the performance of the proposed approaches.

Main Results:

  • The proposed resource allocation and task scheduling methods significantly reduce average completion latency.
  • Compared to traditional algorithms, the new methods show substantial improvements.
  • In the best-case scenario, considering emergency tasks, latency was reduced by 21.6%.

Conclusions:

  • The proposed edge computing strategies are effective in optimizing environmental monitoring.
  • The methods successfully address the unique characteristics and task dependencies of environmental monitoring.
  • This approach offers a more efficient solution for real-time environmental data processing.