Qisong Meng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Qisong Meng has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Binyu Wang
School of City and Regional Planning, Georgia Institute of Technology, Atlanta, GA 30332, USA
1 shared publication - Jianyu Duan
School of Transportation Science and Engineering, Beihang University, Beijing, China
1 shared publication - Li Shen
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
1 shared publication - Qiang Tang
School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China
1 shared publication - Rui Xu
Changsha University of Science and Technology, Changsha 410114, China
1 shared publication - Zhen Tian
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
1 shared publication
Latest Publications
- Open Access
Integrated Blockchain and Federated Learning for Robust Security in Internet of Vehicles Networks
The Internet of Vehicles (IoV) operates in an environment characterized by asymmetric security threats, where centralized vulnerabilities create a critical imbalance that can be disproportionately exploited by attackers. This study addresses this imbalance by proposing a symmetrical security framework that integrates Blockchain and Federated Learning (FL) to restore equilibrium in the Vehicle–Road–Cloud ecosystem. The evolution toward sixth-generation (6G) technologies amplifies both the potential of vehicle-to-everything (V2X) communications and its inherent security risks. The proposed framework achieves a delicate balance between robust security and operational efficiency. By leveraging blockchain’s symmetrical and decentralized distribution of trust, the framework ensures data and model integrity. Concurrently, the privacy-preserving approach of FL balances the need for collaborative intelligence with the imperative of safeguarding sensitive vehicle data. A novel Cloud Proxy Re-Encryption Offloading (CPRE-IoV) algorithm is introduced to facilitate efficient model updates. The architecture employs a partitioned blockchain and a smart contract-driven FL pipeline to symmetrically neutralize threats from malicious nodes. Finally, extensive simulations validate the framework’s effectiveness in establishing a resilient and symmetrically secure foundation for next-generation IoV networks.
Open AccessIntegrated Blockchain and Federated Learning for Robust Security in Internet of Vehicles Networks
The Internet of Vehicles (IoV) operates in an environment characterized by asymmetric security threats, where centralized vulnerabilities create a critical imbalance that can be disproportionately exploited by attackers. This study addresses this imbalance by proposing a symmetrical security framework that integrates Blockchain and Federated Learning (FL) to restore equilibrium in the Vehicle–Road–Cloud ecosystem. The evolution toward sixth-generation (6G) technologies amplifies both the potential of vehicle-to-everything (V2X) communications and its inherent security risks. The proposed framework achieves a delicate balance between robust security and operational efficiency. By leveraging blockchain’s symmetrical and decentralized distribution of trust, the framework ensures data and model integrity. Concurrently, the privacy-preserving approach of FL balances the need for collaborative intelligence with the imperative of safeguarding sensitive vehicle data. A novel Cloud Proxy Re-Encryption Offloading (CPRE-IoV) algorithm is introduced to facilitate efficient model updates. The architecture employs a partitioned blockchain and a smart contract-driven FL pipeline to symmetrically neutralize threats from malicious nodes. Finally, extensive simulations validate the framework’s effectiveness in establishing a resilient and symmetrically secure foundation for next-generation IoV networks.