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

Mekhled Alharbi1, Khalid Haseeb2, N Z Jhanjhi3,4

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

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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 IntelligenceBlockchainEdge computingNetwork trustSensor networks

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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.