# Towards Secure and Efficient Data Scheduling for Vehicular Social Networks

arXiv: https://arxiv.org/abs/2407.00141 · HTML: https://mlsanity.com/paper/2407.00141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 61 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study](https://mlsanity.com/paper/2304.11098/) (2023)
- [Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities](https://mlsanity.com/paper/2304.11397/) (2023)
- [Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles](https://mlsanity.com/paper/2004.10927/) (2020)
- [Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks](https://mlsanity.com/paper/2010.01722/) (2020)
- [Vehicular Communications: A Network Layer Perspective](https://mlsanity.com/paper/1707.09972/) (2017)

## Abstract (arXiv)

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.