Sensor Information Retrieval From Internet of Things: Representation and Indexing

Abstract

Billions of devices are connected in the Internet of Things (IoT)-based sensor networks and they continuously generate a large volume of data. In order to get access to specific data, which is crucial to enable a myriad of new intelligent applications, efficient information retrieval becomes an imminent need for IoT. However, sensor information in the physical world can be heterogeneous, high dimensional, and voluminous due to the complex and dynamic environments. In this paper, we first investigate several IoT search scenarios and propose a uniform representation model for sensor information recordings. Four query models are designed to represent all possible information query styles. With these models, we develop information retrieval architecture for IoT. In essence, an indexing mechanism called efficiency maximization and cost minimization is proposed to solve the property selection problem in the process of index construction and update. Meanwhile, a novel real-time grid R-tree structure is designed to support historical and real-time search for spatiotemporal observation data. Simulation results based on real-world IoT data sets show that storage space is considerably reduced with the sensor model. Furthermore, the proposed indexing mechanisms can improve retrieval efficiency and accuracy, and ensure scalability for large-sized data simultaneously.