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Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems.

Ruchika Bhakhar1, Rajender Singh Chhillar2

  • 1Department of computer science and applications, Maharshi Dayanand University, Rohtak, India. ruchikabhakhar@gmail.com.

Scientific Reports
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Summary

The Dynamic Multi-Criteria Scheduling (DMCS) algorithm optimizes task scheduling for smart home Internet of Things (IoT) devices. It improves efficiency by balancing tasks between fog and cloud computing, reducing costs and energy use.

Keywords:
Cloud computingDynamic schedulingFog computingInternet of ThingsMulti-criteria optimizationSmart home

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Area of Science:

  • * Computer Science
  • * Artificial Intelligence
  • * Internet of Things (IoT)

Background:

  • * The increasing number of Internet of Things (IoT) devices in smart homes necessitates efficient computational task management.
  • * Existing scheduling algorithms struggle to balance the diverse needs of smart home applications in complex network environments.

Purpose of the Study:

  • * To introduce and evaluate the Dynamic Multi-Criteria Scheduling (DMCS) algorithm for task scheduling in fog-cloud computing environments.
  • * To enhance computational efficiency, reduce operational costs, and minimize energy consumption in smart home IoT systems.

Main Methods:

  • * Development of the Dynamic Multi-Criteria Scheduling (DMCS) algorithm.
  • * Dynamic task allocation based on computational complexity, urgency, and data size.
  • * Implementation and comparison with conventional scheduling algorithms in fog-cloud environments.

Main Results:

  • * DMCS significantly reduces makespan, operational costs, and energy consumption compared to traditional methods.
  • * Improved system responsiveness and overall computational efficiency in smart home applications.
  • * Effective balancing of immediate and delayed task execution.

Conclusions:

  • * DMCS offers a robust and adaptive scheduling solution for smart home IoT ecosystems.
  • * The algorithm enhances the efficiency and performance of computational task management.
  • * Future work includes integrating machine learning for refined task classification and enhancing security.