Prediction of 3G network characteristics for adaptive vehicular Connectivity Maps (Poster)

Vehicular communication using cellular networks is an increasingly important issue and provides additional functionality as well as services with highly topical information. Due to the mobility of vehicles, network coverage or current utilization, frequent changes in the communication properties occur, for example spontaneous significant changes in the available bandwidth. Not all applications can handle such delays or interruptions very well. Moreover, the communication scheduling is not optimal because communication changes come without any prior warning. In many cases it would be helpful if additional knowledge about the future properties is available. In this work we show how to exploit vehicles which go around collecting data about their current communication conditions and transfer the data to a central server database. On the server side we can make predictions about the future connectivity by taking into account various influences and the network dynamic. The vehicles can simultaneously request predictions for their surrounding area and benefit from the historical network data of other vehicles. Finally, the different vehicular applications are provided by such predictions and can be used in different ways. We describe the process of establishing such a Connectivity Map including the necessary steps to collect the data and to predict network properties to support vehicular application with additional context information. Thereby, we also investigate the accuracy of our Connectivity Map in an experimental evaluation scenario.