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Peer-driven task scheduling and resource allocation for enhanced performance in industrial IoT systems.

Ayman Alfahid¹, Chahira Lhioui², Somia Asklany³

¹Department of Information Systems, College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia.

Scientific Reports

|April 25, 2025

Summary

This summary is machine-generated.

This study introduces a new Peer-dependent Scheduling and Allocation Scheme (PSAS) for Industrial Internet of Things (IIoT) systems. PSAS uses predictive learning to reduce task delays and improve resource management in peer-to-peer networks.

Keywords:

Allocation scheme Industrial internet of things Peer-dependent scheduling Peer-to-peer Resource allocation Smart industry Task scheduling

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Area of Science:

Computer Science
Industrial Engineering
Artificial Intelligence

Background:

Peer-to-peer (P2P) systems in smart industries, augmented by the Industrial Internet of Things (IIoT), are crucial for distributed task management.
Sequential resource dependencies in traditional P2P systems can cause task stagnancy, hindering efficiency.
Existing resource allocation methods often struggle with dynamic task requirements and sequential processing.

Purpose of the Study:

To propose a novel Peer-dependent Scheduling and Allocation Scheme (PSAS) to overcome task stagnancy in P2P-based IIoT systems.
To optimize task scheduling and resource allocation using predictive learning for improved system throughput.
To enhance the scalability, reliability, and efficiency of distributed task processing in smart industrial environments.

Main Methods:

Developed a Peer-dependent Scheduling and Allocation Scheme (PSAS) integrating predictive learning.
PSAS evaluates resource availability, task duration, and deadlines for optimized decision-making.
Implemented real-time recommendations based on historical resource utilization analysis.

Main Results:

PSAS significantly reduces task stagnancy and improves task processing ratios.
Demonstrated improved processing ratio by up to 10.62% compared to existing methods.
Achieved a reduction in the stagnancy factor by 5.06%, enhancing overall system performance.

Conclusions:

PSAS offers a scalable and reliable solution for resource management in P2P-based IIoT systems.
Predictive learning enhances decision-making, reducing task completion delays.
The proposed scheme represents a significant advancement in optimizing distributed task processing for smart industries.