Treffer: Context-Adaptive Vehicular Network Optimization

We propose a framework to optimize the communication performance and mobility management in vehicular networks. By having a single unified decision algorithm taking into account both stack-related and external contextual information such as GPS localization or signaling from other nodes, advice can be provided to every layer in the network stack to allow for globally optimized, faster and more accurate adaptation to the current conditions. We present how key example scenarios would benefit from such a system. We describe an instance of this framework using a constraint satisfaction problem (CSP) approach. We also describe our prototype implementation of the network data collection system and give some timing evaluation for a given constraint solver.