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This study introduces a new artificial intelligence method for task scheduling in Internet of Things applications within small fog computing environments. The AI-powered approach enhances performance by reducing latency and response times with minimal energy use.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Internet of Things (IoT) applications require low latency and fast response times.
  • Fog computing, utilizing edge servers and a management layer, is often used for IoT deployment.
  • Small-scale fog environments face challenges in task scheduling for optimal performance.

Purpose of the Study:

  • To propose a latency-aware task scheduling method for IoT applications in small-scale fog computing.
  • To leverage artificial intelligence, specifically artificial neural networks, for improved task scheduling.

Main Methods:

  • Developed a task scheduling method using artificial neural networks with partitioning capabilities.
  • Implemented parallel learning and calculation of hyperparameters across multiple edge servers.
  • Evaluated performance against state-of-the-art methods.

Main Results:

  • The proposed method effectively reduces task scheduling times and improves service level objectives.
  • Achieved significant improvements in latency and response times for IoT applications.
  • Demonstrated negligible energy consumption.

Conclusions:

  • The AI-based latency-aware task scheduling is effective and efficient for small-scale fog computing environments.
  • The partitioning technique for artificial neural networks is key to parallel processing and reduced scheduling times.
  • This approach offers a viable solution for optimizing IoT application performance in resource-constrained fog environments.