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Updated: Mar 19, 2026

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

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

Published on: September 8, 2023

Blockchain-driven trust management and AI computing for sensor networks optimization.

Mekhled Alharbi¹, Khalid Haseeb², N Z Jhanjhi^3,4

¹Department of Software Engineering, College of Computer and Information Sciences, Jouf University, 72311, Sakaka, Saudi Arabia.

Scientific Reports

|March 18, 2026

Summary

This summary is machine-generated.

This study introduces a lightweight, trusted AI-driven model to enhance security in intelligent systems. The model improves data forwarding and ensures fault tolerance, offering superior performance over existing approaches for connected devices.

Keywords:

Artificial Intelligence Blockchain Edge computing Network trust Sensor networks

Related Experiment Videos

Last Updated: Mar 19, 2026

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

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

Published on: September 8, 2023

Area of Science:

Computer Science
Artificial Intelligence
Network Security

Background:

Intelligent systems rely on the Internet of Things (IoT) and edge computing for data processing.
Existing security approaches face challenges like data eavesdropping in dynamic environments.
Developing trustworthy and authenticated systems is crucial for smart system growth.

Purpose of the Study:

To introduce a lightweight, trusted AI-driven model for enhancing security in complex systems.
To ensure a more robust and reliable data-forwarding path.
To address the research challenge of trustworthiness in dynamic IoT environments.

Main Methods:

An adaptive technique explores network conditions for efficient data-transfer policies.
Distributed, collaborative interactions enable load balancing across devices with minimal resources.
Trust is continuously updated using real-time parameters and neighbor communication records.

Main Results:

The proposed model enhances security and ensures a robust data-forwarding path.
Distributed interactions improve system response time through load balancing.
Continuous trust updates provide fault tolerance and trusted communication channels.

Conclusions:

The AI-driven model offers superior security and reliability for intelligent systems.
The approach is validated through simulations, demonstrating effectiveness in realistic scenarios.
This research contributes to developing more trustworthy and authenticated connected systems.