A Secure and Efficient Framework for Intelligent Transportation: Leveraging ISDF and Augmented Iot Algorithms

Abstract

The Integrated Sensing Digital Framework (ISDF) serves as a transformative force for the Internet of Things (IoT) by facilitating data collection, execution, and computational services. Intelligent transportation, a key application of Cyber-Physical Systems (CPS), modernizes vehicles through technologies such as GPS, alarms, trackers, and autonomous driving systems. Enhancing real-time interactions among vehicles, pedestrians, and infrastructure requires advanced wireless communication technologies. While integrating ISDF with IoT offers many benefits, it also presents challenges in the reliability, security, and privacy of data transformation, location tracking, and analysis. This article proposes an efficient decision-making framework employing augmented algorithms and n-step bootstrapping learning schemes to identify legitimate devices within ISDF networks. The proposed mechanism is evaluated based on security and privacy metrics, including delivery ratio, accuracy, average trust value, and defense against DoS attacks, validated through simulations.