A Blockchain-Enabled Framework For Traffic Rerouting And Task Offloading On The Internet Of Vehicles (Iov) Using Federated Learning

Abstract

The Internet of Vehicles (IoV) has great potentials in traffic regulation and vehicle synchronization. However, there are challenges of traffic congestion, data confidentiality and optimal allocation of computational resources. Traffic congestion is an important issue with an impact on travel time, fuel consumption and emissions. In the edge-cloud ecosystem, task delegation needs effective methodologies for improving latency, resource efficiency and computational burden. The proposed framework is Joint Federated Learning and Blockchain-Enabled Traffic Rerouting with Efficient Task Offloading of Consumer IoV in Edge-Cloud Environment. This framework solves these issues by integration of federated learning and blockchain technologies. Federated learning allows vehicles to jointly train a global model while not sharing their local data, which preserves data privacy and saves bandwidth. Blockchain ensures the security and integrity of model changes and builds up confidence among the stakeholders. Task offloading techniques can improve the utilization of edge and cloud resources, reduce latency and save energy consumption. The methodology is validated against a large dataset and shows significant improvements in traffic forecasting accuracy, security and overall system effectiveness. This shows that the comprehensive solution is effective to solve the problems related to Consumer Internet of Vehicles (CIoV) .