Secured Scalable Blockchain Networks for Trustworthy Distributed Deep Learning in VANETs

Abstract

Distributed deep learning (DDL) within vehicular ad hoc networks (VANETs) holds profound significance in developing smart applications, such as intelligent transport systems and autonomous driving, where multiple parties are coordinated to leverage the training capability and acquisitive intelligence. Blockchain (BC) is a distribution technology promising for trustworthy DDL, holding the divide-and-conquer concept with decentralized management and consensus algorithms to reduce the exposure of sensitive controllers and thus the risks of malicious system-wide attacks. Moreover, zero trust architecture (ZTA) concepts are promoted as innovative cybersecurity solution which can be integrated with BC to address the resource-limited and infrastructure-less issues to augment the strength of DDL in VANETs. In this dissertation, the BC and ZTA potential is explored to construct reliable VANETs for trustworthy data sharing and thus to support significant within-VANET DDL and relevant applications. Firstly, virtualized distributed ledger technology (vDLT) is developed as the multimedia BC platform, followed by vDLT-based VANETs built to solve the unsteady communication; secondly, vDLT is improved to transmit and secure traffic events in VANETs; subsequently, a vDLT-based DDL system is proposed to enhance object detection (OD) inside VANETs; finally, ZTA and sharding scheme are enabled in vDLT-based VANETs for improved protection. Generally, vDLT runs with virtualized resource and sharding supports for scalability improvement, along with multi-layered consensus algorithm and an adaptable hierarchical and decentralized PBAC (hdPBAC) access control model to agilely protect the DDL procedures and relevant DDL-related applications. The proposed system has been developed, including the vDLT system, the multimedia streaming over vDLT in fixed networks and VANETs, a precursory vDLT-based DDL system for OD purpose, and the integration of ZTA into scalable-BC-based VANETs. The evaluation results show the feasibility of proposed design and demonstrate the significance of performance improvements.