Distributed query processing is of paramount importance in next-generation distribution services, such as Internet of
Things (IoT) and cyber-physical systems. Even if several multi-attribute range queries supports have been proposed for
peer-to-peer systems, these solutions must be rethought to fully meet the requirements of new computational paradigms
for IoT, like fog computing. This paper proposes dragon, an ecient support for distributed multi-dimensional range
query processing targeting ecient query resolution on highly dynamic data. In dragon nodes at the edges of the
network collect and publish multi-dimensional data. The nodes collectively manage an aggregation tree storing data
digests which are then exploited, when resolving queries, to prune the sub-trees containing few or no relevant matches.
Multi-attribute queries are managed by linearising the attribute space through space lling curves. We extensively
analysed dierent aggregation and query resolution strategies in a wide spectrum of experimental set-ups. We show that
dragon manages eciently fast changing data values. Further, we show that dragon resolves queries by contacting a
lower number of nodes when compared to a similar approach in the state of the art
