Towards Secure and Efficient Data Scheduling for Vehicular Social Networks

TL;DR

This paper presents a novel learning-based data scheduling algorithm for vehicular social networks that enhances efficiency and security using neural networks, Q-learning, and differential privacy, outperforming existing methods.

Contribution

It introduces an innovative neural network and Q-learning based scheduling algorithm that incorporates differential privacy for secure and efficient data transmission in vehicular social networks.

Findings

Superior performance over existing algorithms

Effective privacy preservation during data exchange

Enhanced data processing capabilities

Abstract

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.