Iterative Method For Blockchain-Based Qos-Aware Iov Network Using Ai-Driven Anomaly Detection And Dynamic Network Slicing

Abstract

The rapid deployment of 5G networks necessitates the development of secure, scalable, and efficient Internet of Vehicles (IoV) systems. Existing IoV solutions often struggle with real-time threat detection, scalability, efficient resource allocation, and privacy preservation. This work proposes an integrated framework leveraging blockchain technology, AI-driven anomaly detection, dynamic network slicing, and secure multi-party computations. We introduce AI-Driven Anomaly Detection and Mitigation (ADAM) to identify and respond to security threats in real-time. Utilizing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), ADAM analyzes network traffic data to detect anomalies with a detection accuracy of 95%, a false positive rate of 2%, and an average response timestamp of 50 ms. To tackle scalability and latency issues inherent in traditional blockchain systems, we propose Edge-Based Blockchain Sharding (EBBS).The innovative use of a modified Proof-of-Stake (PoS) mechanism tailored for edge environments further enhances the scalability of the IoV system. AI-Enabled Dynamic Network Slicing (ADNS) is implemented to optimize resource allocation based on real-time traffic demands and QoS requirements. Finally, we incorporate Secure Multi-Party Computation for Collaborative Data Processing (SMPC-CDP) to enable secure, privacy-preserving data analysis among IoV entities ensuring privacy with a computation overhead of 20%, and data utility preservation of 95%.