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

## TL;DR

This paper introduces a trust-aware edge computing model to enhance security and performance in IoT-based intelligent transportation systems.

## Contribution

A novel trust-aware edge-assisted model with blockchain for secure and efficient vehicular networks in IoT-ITS environments.

## Key findings

- The proposed model improved network throughput by 50% and 62.5% compared to existing methods.
- End-to-end delay was reduced by 33.3% and 37.5%, and routing overhead by 34% and 38.7%.
- False positive rate was significantly reduced by 67.9% and 68.5% in dynamic network conditions.

## Abstract

Wireless communication and the Internet of Things (IoT) are integrated for the formulation of an emerging Intelligent Transportation System (ITS) for the interaction of vehicles and to enhance road safety. The emerging network manages the traffic flow, real-time data analytics, and resource control for the development of urban transportation systems and smart cities. Extensive research has been conducted on the development of efficient routing response time for the IoT-ITS environment; however, the rapid changes in the network topologies still lead to unmanageable congestion and communication holes. Moreover, it is also often threatened due to high urban mobility and incurs additional transmission with excessive overhead. Such concepts are not able to maintain secure interactions among vehicles and expose confidential data to malicious devices while interacting on unpredictable channels. This research proposes a trust-aware edge-assisted model to secure the vehicular network and offers a more reliable system with optimal routing performance. The global trust model is maintained based on network conditions using localized computing and attaining data privacy and coherence. Furthermore, a blockchain ledger is included along with trust to ensure tamper-proof and transparent computing across the boundaries of the IoT-ITS environment. The proposed model is compared with Graph-Based Trust-Enabled Routing (GBTR) and Bacteria for Aging Optimization Algorithm (BFOA), and the results revealed significant performance for network throughput by 50% and 62.5%, end-to-end delay by 33.3% and 37.5%, routing overhead by 34% and 38.7%, and false positive rate by 67.9% and 68.5% over the dynamic network infrastructure.