Blockchain-Enabled Vehicle Lifecycle Management With Predictive Maintenance Using Federated Learning
Mritunjay Shall Peelam, Kunjan Shah, Vinay Chamola, Biplab Sikdar
Abstract
The traditional landscape of vehicle lifecycle management systems has several issues, including widespread fraud, opaque processes, and limited accessibility. As a result, there is a need for a paradigm change toward modernized vehicle management techniques, which is connected with the emergence of Intelligent Transport Systems (ITS). This work is a novel solution in the shape of a Blockchain-Assisted Vehicle State Tracking System that is claimed to transform how automobiles are identified, registered, tracked, and controlled inside an Intelligent Transport System. The proposed model offers a secure, auditable ledger for tracking vehicle states. Incorporating federated learning-based predictive maintenance ensures timely servicing while protecting the privacy of user data. This paper explores the intricate architecture and promising capabilities to not only address the shortcomings of existing frameworks but also promote the evolution towards a seamlessly integrated, technologically driven ecosystem for vehicle management and Intelligent Transport Systems.