Towards Secure and Efficient Data Scheduling for Vehicular Social Networks
This work addresses the problem of data scheduling for users in vehicular social networks, representing an incremental improvement with a novel hybrid method.
The paper tackles the challenge of efficient and secure data transmission scheduling in vehicular social networks by introducing a learning-based algorithm that combines neural networks and Q-learning with differential privacy, demonstrating superior performance compared to existing state-of-the-art algorithms.
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.