Skip to Main Content
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 WSNs. However, most of existing methods lack optimization for spatial- temporal search and similarity search for multi-attribute data. Some methods are optimized under circumstances where nodes are equipped with locating systems (e.g., GPS) which consumes high energy. This paper proposes a distributed spatial-temporal similarity data storage scheme (SDS). 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 a locating system. SDS provides efficient spatial- temporal and similarity data searching service. Experimental results show that SDS yields significant improvements on the efficiency of data querying compared with existing approaches.