Optimizing CASCADE data aggregation for VANETs

We present an analysis of 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). In this paper, we determine the optimal cluster size to balance the trade-off between local view length and expected frame size.