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Related Concept Videos

Load-frequency control01:28

Load-frequency control

162
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
162

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Optimizing Internet of Things Fog Computing: Through Lyapunov-Based Long Short-Term Memory Particle Swarm

Sheng Pan1, Chenbin Huang1, Jiajia Fan1

  • 1School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China.

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|February 24, 2024
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This study introduces a novel fog computing algorithm that optimizes task allocation and resource management in rechargeable Internet of Things (IoT) networks. The approach enhances efficiency and user experience by reducing latency and energy consumption.

Keywords:
LSTMLyapunovPSOfog computinginternet of things (IoT)predictive allocationsystem stability

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) systems face challenges with high latency in edge-to-cloud communication.
  • Increasingly, IoT devices are both data producers and consumers, demanding efficient resource management.
  • Traditional cloud computing models struggle with the demands of real-time smart services.

Purpose of the Study:

  • To introduce a novel layered computing network based on fog computing principles.
  • To develop an algorithm for optimizing user tasks and allocating computing resources in rechargeable networks.
  • To address performance limitations and optimize the utilization of low-power edge devices.

Main Methods:

  • A hybrid algorithm combining Lyapunov-based control, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO).
  • Fog servers dynamically train LSTM networks for predictive task data feature forecasting.
  • Lyapunov functions are utilized for dynamic resource control in rechargeable networks.

Main Results:

  • The proposed algorithm demonstrates superior energy efficiency and resource allocation optimization compared to traditional methods.
  • Predictive task allocation based on forecasted data features improves unload decisions.
  • Optimized utilization of low-power devices and effective resource management in rechargeable networks.

Conclusions:

  • The novel fog computing approach significantly enhances the efficiency and user experience of IoT systems.
  • The algorithm effectively reduces latency and energy consumption, overcoming edge device limitations.
  • This research contributes a robust solution for managing resources and tasks in complex, rechargeable IoT environments.