Spatio-temporal range queries over Big Location Data aim to extract and analyze relevant data items generated around a given location and time. They require concurrent processing of massive and dynamic data flows. Current solutions for Big Location Data are ill-suited for continuous spatio-temporal processing because (i) most of them follow a batch processing model and (ii) they rely on spatial indexing structures maintained on a central master server. In this paper, we propose a scalable architecture for continuous spatio-temporal range queries built by coalescing multiple computing nodes on top of a Distributed Hash Table. The key component of our architecture is a distributed spatio-temporal indexing structure which exhibits low insertion and low index maintenance costs. We assess our solution with a public data set released by Yahoo! which comprises millions of geotagged multimedia files.