Internet of vehicles security offloading and cache optimization method based on graph neural network

By constructing a vehicle-to-everything (V2X) system model and combining graph neural networks and reinforcement learning, the problems of task and cache separation and insufficient spatiotemporal correlation in V2X were solved, achieving efficient collaborative utilization of resources and privacy security, and improving task execution efficiency and cache hit rate.

CN121099376BActive Publication Date: 2026-03-20EAST CHINA JIAOTONG UNIVERSITY
3 Cites 0 Cited by

Patent Information

Application Number
CN202511639632.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-11
Publication Date
2026-03-20
Estimated Expiration
2045-11-11

AI Technical Summary

Technical Problem

In existing vehicle networking technologies, task and cache processing are separated, and spatiotemporal correlation is not captured sufficiently, resulting in low resource utilization, insufficient task execution efficiency, and inability to achieve the optimal state of resource scheduling.

Method used

The method for optimizing task offloading and caching in vehicle-to-everything (V2X) security based on graph neural networks constructs a system model of vehicles, roadside units, and macro base stations. By combining graph neural networks and reinforcement learning, it achieves collaborative optimization of task offloading and caching. It adopts differential privacy protection and asynchronous federated learning to improve the accuracy of task popularity prediction, captures spatiotemporal correlations, and optimizes offloading targets and caching strategies.

Benefits of technology

It achieves a synergistic improvement in privacy and security, low total system latency, high cache hit rate and resource utilization in the Internet of Vehicles, adapts to dynamic topology and complex task requirements, and improves resource scheduling adaptability and efficiency.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application discloses a vehicle networking security offloading and cache optimization method based on a graph neural network, relates to the fields of intelligent transportation and edge computing, and aims to solve the problems of low resource scheduling efficiency, insufficient privacy protection and task and cache processing fragmentation in vehicle networking. The method comprises the following steps: S1, a vehicle networking system model is constructed, including a vehicle-roadside unit-macro base station architecture, a communication model of an orthogonal frequency division multiple access and a dedicated short-range communication protocol, and a calculation model including local processing and partial remote offloading; S2, Gaussian noise is introduced to realize differential privacy protection, and an asynchronous federated learning is combined with a long short-term memory network to predict task popularity; and S3, a dynamic heterogeneous graph is constructed, time and space features are extracted by using a graph neural network, an approximate policy optimization algorithm is input, and offloading targets, proportions and cache strategies are jointly optimized. The application realizes the collaborative improvement of privacy security, low system total time delay, high cache hit rate and resource utilization rate, and is suitable for the dynamic topology and complex task demand of vehicle networking.
Need to check novelty before this filing date? Find Prior Art