Towards an Optimized and Secure CASCADE for Data Aggregation in VANETs

The authors present an analysis of and security extensions to the CASCADE (Cluster-based Accurate Syntactic Compression of Aggregated Data in VANETs) data aggregation technique. CASCADE organizes known vehicles into clusters, the size of which determines both the frame size used to distribute aggregated data and the distance ahead that vehicles are aware of (local view). They determine the optimal cluster size to balance the trade-off between local view length and expected frame size. The original CASCADE description does not consider security issues, so they present their framework for a secure CASCADE by employing received signal strength and laser rangefinders for position verification.