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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Modified grey wolf optimization for energy-efficient internet of things task scheduling in fog computing.

Deafallah Alsadie¹, Musleh Alsulami²

¹Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, 21961, Makkah, Saudi Arabia. dbsadie@uqu.edu.sa.

Scientific Reports

|April 27, 2025

Summary

This summary is machine-generated.

A new Task Scheduling algorithm using modified Grey Wolf Optimization (TS-GWO) optimizes Internet of Things (IoT) task scheduling in fog-cloud systems. TS-GWO significantly reduces task completion time and energy consumption.

Keywords:

Fog-cloud computing Grey wolf optimizer (GWO)Internet of things (IoT)Metaheuristic algorithm Task scheduling

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Area of Science:

Computer Science
Distributed Computing
Artificial Intelligence

Background:

Fog-cloud computing is essential for Internet of Things (IoT) applications, demanding efficient task scheduling.
Existing methods face challenges in balancing makespan and energy efficiency in dynamic fog-cloud environments.
Resource constraints in fog-cloud systems necessitate advanced scheduling solutions.

Purpose of the Study:

To introduce a novel Task Scheduling algorithm based on modified Grey Wolf Optimization (TS-GWO) for IoT requests.
To enhance exploration and exploitation capabilities for optimal scheduling solutions in fog-cloud systems.
To address the limitations of current task scheduling methods in dynamic and resource-constrained environments.

Main Methods:

Development of a modified Grey Wolf Optimization (TS-GWO) algorithm with innovative operators.
Tailoring the TS-GWO algorithm specifically for Internet of Things (IoT) task scheduling in fog-cloud systems.
Evaluation using synthetic datasets and real-world workloads (NASA Ames iPSC, HPC2N).

Main Results:

TS-GWO demonstrates superior performance compared to established metaheuristic methods.
Achieved up to 46.15% improvement in makespan (task completion time).
Achieved up to 28.57% reduction in energy consumption.

Conclusions:

TS-GWO effectively addresses task scheduling challenges in fog-cloud environments.
The algorithm shows significant potential for optimizing IoT applications.
TS-GWO can be applied to broader optimization tasks in distributed systems.