  Efficient data transmission scheduling within vehicular environments poses a
significant challenge due to the high mobility of such networks. Contemporary
research predominantly centers on crafting cooperative scheduling algorithms
tailored for vehicular networks. Notwithstanding, the intricacies of
orchestrating scheduling in vehicular social networks both effectively and
efficiently remain formidable. This paper introduces an innovative
learning-based algorithm for scheduling data transmission that prioritizes
efficiency and security within vehicular social networks. The algorithm first
uses a specifically constructed neural network to enhance data processing
capabilities. After this, it incorporates a Q-learning paradigm during the data
transmission phase to optimize the information exchange, the privacy of which
is safeguarded by differential privacy through the communication process.
Comparative experiments demonstrate the superior performance of the proposed
Q-learning enhanced scheduling algorithm relative to existing state-of-the-art
scheduling algorithms in the context of vehicular social networks.
