Spatiotemporal indexing for moving objects in an indoor cellular space

To facilitate a variety of indoor applications, positioning technologies have been developed in indoor spaces (such as WI-FI and RFID). Thus, the requirement for the tracking and monitoring of moving objects in indoor spaces has increased considerably. The indexing of moving objects in indoor spaces is of essential importance, as these are different from outdoor spaces in many respects, such as the measurements and the positioning technologies. Therefore, in this paper, we propose a new adjacency-index structure for objects moving in indoor space which includes both spatial and temporal properties. The spatial index is based on the connectivity (adjacency) between the indoor environment cells. Moreover, we propose two temporal indexes with different methods to store the temporal data, which can support and enable efficient query processing and efficient updates for objects moving in indoor space. The proposed indexes can efficiently serve different types of spatial queries, such as KNN and indoor range, and a variety of temporal queries which are essential in an indoor environment. An empirical performance study suggests that the proposed data structures are effective, efficient, and robust.