Edge Driven Trust Aware Threat Detection for IoT Enabled Intelligent Transportation Systems

Area of Science:

• Computer Science
• Engineering

Background:

• Intelligent Transportation Systems (ITS) integrate wireless communication and the Internet of Things (IoT) for enhanced road safety and urban mobility.
• Existing IoT-ITS environments face challenges with network topology changes, leading to congestion, communication holes, and security vulnerabilities.
• Vehicular networks require secure interactions to prevent data exposure to malicious devices on unpredictable channels.

Purpose of the Study:

• To propose a novel trust-aware edge-assisted model for securing vehicular networks in IoT-ITS.
• To enhance the reliability and optimize routing performance within dynamic urban transportation systems.
• To ensure data privacy, coherence, and tamper-proof computing in the IoT-ITS environment.

Main Methods:

• Developed a trust-aware edge-assisted model integrating localized computing for global trust management.
• Incorporated a blockchain ledger for tamper-proof and transparent data computing across IoT-ITS boundaries.
• Evaluated the model against Graph-Based Trust-Enabled Routing (GBTR) and Bacteria for Aging Optimization Algorithm (BFOA).

Main Results:

• Achieved significant improvements in network throughput (50% and 62.5%).
• Reduced end-to-end delay (33.3% and 37.5%) and routing overhead (34% and 38.7%).
• Demonstrated a substantial decrease in the false positive rate (67.9% and 68.5%) over dynamic network infrastructures.

Conclusions:

• The proposed trust-aware edge-assisted model significantly enhances security, reliability, and routing efficiency in IoT-ITS.
• Blockchain integration ensures data integrity and transparency, crucial for secure vehicular communication.
• The model offers a robust solution for managing dynamic network conditions and mitigating security threats in smart city transportation.