The rapid development of Internet of Vehicles (IoV) necessitates a secure and reliable infrastructure to store and share the massive data. Blockchain, a distributed and immutable ledger, is widely considered as a promising solution to ensure data security and privacy for IoV. To deal with the massive IoV data, the scalability of blockchain becomes a critical issue, which should maximize transactional throughput as well as handling the dynamics of IoV scenarios. Therefore, this paper proposes a novel deep reinforcement learning (DRL) based performance optimization framework for blockchain-enabled IoV, where transactional throughput is maximized while guaranteeing the decentralization, latency and security of the underlying blockchain system. In this framework, we first carry out the performance analysis for blockchain systems from the aspects of scalability, decentralization, latency and security. Further, DRL technique is adopted to select block producers and adjust block size and block interval to adapt to the dynamics of IoV scenarios. Simulation results show that our proposed framework can effectively improve the throughput of blockchain-enabled IoV systems without affecting other properties.
2019 IEEE International Conference on Communications, ICC 2019
School of Information Technology

Liu, M. (Mengting), Teng, Y. (Yinglei), Yu, F.R, Leung, V.C.M. (Victor C. M.), & Song, M. (Mei). (2019). Deep Reinforcement Learning Based Performance Optimization in Blockchain-Enabled Internet of Vehicle. In IEEE International Conference on Communications. doi:10.1109/ICC.2019.8761206