The invention discloses an Internet of Vehicles secure unloading and
cache optimization method based on a graph neural network, relates to the field of intelligent traffic and
edge computing, and aims to solve the problems of
low resource scheduling efficiency, insufficient
privacy protection and task and cache
processing separation of
the Internet of Vehicles. The method comprises the following steps: S1, constructing an Internet of Vehicles
system model, a communication model comprising a vehicle-roadside unit-
macro base station architecture, an
orthogonal frequency division multiple access and dedicated short-range communication protocol, and a calculation model comprising local
processing and partial remote unloading; s2,
Gaussian noise is introduced to realize
differential privacy protection, and asynchronous
federated learning is combined with a long-short-
term memory network to predict task popularity; and S3, constructing a dynamic heterogeneous graph, extracting spatio-temporal features by means of a graph neural network, inputting a near-end strategy optimization
algorithm, and jointly optimizing an unloading target, a proportion and a caching strategy. According to the method, the collaborative improvement of privacy security, low
system total time
delay, high
cache hit rate and
resource utilization rate is realized, and the method adapts to the dynamic topology and complex task requirements of
the Internet of Vehicles.