Taxonomy of an application model:Toward building large scale, connected vehicle applications

With the advent of advanced computing systems beyond personal computing, such as mobile computing, cloud computing or recently, vehicular ad-hoc network, it is crucial that we understand the application development process of each type of these systems. Better understanding of how applications are built in different environment allows us to design better application models and system supports for developers. This paper studies the taxonomy of application models and defines its consisting aspects, namely application scope, application abstraction level, application structure, communication model and programming model. With the better understanding of the application models in general, we lay out the requirements for developing a class of large scale connected vehicle applications

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