Pavan

Advances in wireless communication, storage and processing are realizing next-generation in-vehicle entertainment systems. Even if hundreds of different video or audio titles are stored among several vehicles in an area, only a subset of these titles might be available to a given vehicle depending on its current location, intended path, and the dynamics of its ad-hoc network connectivity. The vehicle's entertainment system must somehow predictively determine which titles are available either immediately or within the future d time units, so that the user can select a title to view. The available title list must seek to satisfy the user by striking a delicate balance between showing far fewer titles than can actually be accessed and showing too many titles that cannot be accessed. In addition to defining this availability problem, we make two key contributions. First, a two-tier system architecture which leverages the low-rate cellular infrastructure as a control network for the high-rate data network consisting of the vehicular ad-hoc network. Second, PAVAN as a policy framework for predicting the availability of a title. We describe several variants of PAVAN which incorporate information based on a Markov mobility model, spatio-temporal look-ahead, and title replications. Our results demonstrate that the quality of PAVAN's predictions is critically dependent on title degree of replication, as well as its relative size with respect to the trip duration. When degree of replication is below a certain threshold, PAVAN with content density information and the predictive mobility model is shown to provide the best overall performance.