Blockchain localization spoofing detection based on fuzzy AHP in IoT systems.

Area of Science:

• Computer Science
• Cybersecurity
• Internet of Things (IoT)

Background:

• Location spoofing is a critical vulnerability in Internet of Things (IoT) systems, undermining the reliability of location-based services.
• Existing location spoof detection methods often rely on physical layer features, limiting their applicability in diverse IoT environments.
• There is a need for robust infrastructure to ensure the trustworthiness of location data within blockchain-integrated IoT ecosystems.

Purpose of the Study:

• To develop and evaluate a novel blockchain-based system for detecting location spoofing in IoT networks.
• To establish a framework for assessing the trustworthiness of location proofs using blockchain system features.
• To implement a multilayer fuzzy hierarchical analysis process (AHP) for advanced spoof detection.

Main Methods:

• Designed an IoT system incorporating blockchain features for location proof evaluation.
• Developed a blockchain-based multilayer fuzzy hierarchical analysis process (AHP) for spoof detection at node and trajectory levels.
• Conducted simulations to validate the proposed system's performance.

Main Results:

• The proposed blockchain-based AHP method demonstrated superior performance in detecting location spoofing.
• The system effectively evaluates the trustworthiness of location proofs within the IoT environment.
• Simulation results confirm the efficacy and reliability of the developed detection mechanism.

Conclusions:

• The implemented blockchain-based multilayer fuzzy AHP offers a robust solution for location spoof detection in IoT.
• This approach provides a foundational method for assessing the trustworthiness of location proofs in blockchain-enabled IoT systems.
• The findings support the integration of advanced analytical methods for enhanced IoT security.