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