Dynamic Optimization Model for Cooperative Downloading Strategy of VANET

A key challenge of cooperative downloading of Vehicular Ad hoc NATwork (VANET) is how to manage the time and space resources of the area out of the AP coverage for improving the profit of the system. The character of mobility of VANET makes the state space and behavior space time-varying. Therefore, traditionally static optimization theory does not adapt to the situation. On the other hand, in the case of multi-requests from different users, some schemes such as random selection or FIFO method not only fail to distinguish different users' service demands, but also bring little benefit for service providers. To address the problems, this paper proposes a dynamic optimization model for cooperative downloading strategy with multi-class requests. The model is defined as the two-dimensional state space and behavior space and we propose a kind of Bellman recurrence equation, which provides a basis for choosing the users and their helpers for maximum system profit. Furthermore, the model distinguishes the users' downloading priorities according to the user-defined downloading class. Under the fairness of system operation condition, the model provides different services according to users' expectation and it also helps service providers obtain the most benefit by leveraging the repetition ratio of data and user-defined class. Simulation results indicate the benefits of the proposed scheme in terms of increasing service quality for users and benefit for services providers.