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Resource Scheduling and Energy Consumption Optimization Based on Lyapunov Optimization in Fog Computing.

Chenbin Huang1, Hui Wang1, Lingguo Zeng1

  • 1School of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321000, China.

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Summary

This study introduces a new algorithm for Internet of Things (IoT) devices to minimize energy consumption for delay-sensitive tasks. The Lyapunov-based Particle Swarm Optimization (LPSO) algorithm efficiently balances task completion time and energy use.

Keywords:
IoT (Internet of Things)Lyapunov optimizationedge computingfog computing

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Delay-sensitive tasks are increasingly common in Internet of Things (IoT) applications.
  • Existing methods for reducing task delay often increase energy consumption.
  • Balancing task completion time and energy efficiency is a critical challenge in IoT.

Purpose of the Study:

  • To develop a novel algorithm for minimizing energy consumption in IoT systems handling delay-sensitive tasks.
  • To achieve a balance between task processing speed and energy expenditure.
  • To ensure timely task completion while optimizing resource utilization.

Main Methods:

  • Proposed a heuristic Particle Swarm Optimization (PSO) algorithm integrated with a Lyapunov framework, termed LPSO.
  • Focused on guaranteeing task duration and queue stability.
  • Optimized computational energy consumption of IoT nodes, transmission energy, and fog node computing energy.

Main Results:

  • The LPSO algorithm effectively balances energy consumption across IoT nodes, transmission, and fog computing.
  • Guaranteed task duration and queue stability were achieved.
  • Demonstrated significant performance improvements compared to traditional PSO and greedy algorithms.

Conclusions:

  • The LPSO algorithm offers a superior approach to managing delay-sensitive tasks in IoT environments.
  • It successfully minimizes energy consumption while meeting task deadlines.
  • This method provides a viable solution for energy-efficient IoT task scheduling.