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Since centralized data storage and search schemes often lead to high overhead and latency, distributed data-centric storage becomes a preferable approach in large-scale wireless sensor networks (WSNs). However, most of existing distributed methods lack optimization for spatial-temporal search to query events occurred in a certain geographical area and a certain time period. Furthermore, for data search routing, most methods rely on locating systems (e.g., GPS), which consume high energy. This paper proposes a distributed spatial-temporal Similarity Data Storage (SDS) scheme. SDS provides efficient spatial-temporal and similarity data searching service, and is applicable for both static and dynamic WSNs. It disseminates event data in such a way that the distance between WSN neighborhoods represents the similarity of data stored in them. In addition, SDS carpooling routing algorithm efficiently routes messages without the aid of GPS. Theoretical and experimental results show that SDS yields significant improvements on the efficiency of data querying compared with existing approaches, and obtains stable performance in dynamic environments.