Indexing of Spatiotemporal Objects in Indoor Environments

With rapid developments in indoor positioning technologies such as wireless communications, RFID and Bluetooth, the tracking of indoor moving objects has become easier. The indexing of moving objects in indoor spaces is different from outdoor spaces in many respects such as positioning technologies and measurements. Therefore, in this paper, we propose a new adjacency index structure for moving objects in indoor spaces that take into account both spatial and temporal properties. The index is based on the idea of connectivity (adjacency)between the indoor space cells. Furthermore, we use a non-leaf node time stamping method to store temporal data, which can enable and support the temporal queries in an indoor space. An empirical performance study suggests that the developed data structure is effective and robust.