Blockchain-based distributed software-defined vehicular networks via deep q-learning
Nowadays, in order to support flexibility, agility, and ubiquitous accessibility among vehicles, software defined networking has been proposed to integrate with vehicular networks, known as software defined vehicular network (SDVN). Due to a variety of data, flows, and vehicles in SDVN, a distributed SDVN is necessary. However, how to reach consensus in distributed SDVN efficiently and safely is an intractable problem. In this paper, we use a permissioned blockchain approach to reach consensus in distributed SDVN. The existing permissioned blockchain has a number of drawbacks, such as low throughput. We virtualize the underlying resources (e.g., computing resources and networking resources), jointly considering the trust features of blockchain nodes to improve the throughput. Accordingly, we formulate view change, computing resources allocation, and networking resources allocation as a joint optimization problem. In order to solve this joint problem, we use a novel deep Q-learning approach. Simulation results show the effectiveness of our proposed scheme.
|Blockchain, Byzantine fault tolerance, Deep Q-learning, Software defined vehicular networks, Throughput|
|8th ACM International Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, DIVANet 2018, in conjunction with the 21st ACM International Conference on Modeling, Analysis and Simulations of Wireless and Mobile Systems, MSWiM 2018|
|Organisation||School of Information Technology|
Qiu, C. (Chao), Yu, F.R, Xu, F. (Fangmin), Yao, H. (Haipeng), & Zhao, C. (Chenglin). (2018). Blockchain-based distributed software-defined vehicular networks via deep q-learning. In DIVANet 2018 - Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (pp. 8–14). doi:10.1145/3272036.3272040