Indexing the Trajectories of Moving Objects in Symbolic Indoor Space

Abstract

Abstract. Indoor spaces accommodate large populations of individuals. With appropriate indoor positioning, e.g., Bluetooth and RFID, in place, large amounts of trajectory data result that may serve as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. This scenario calls for the indexing of indoor trajectories. Based on an appropriate notion of indoor trajectory and definitions of pertinent types of queries, the paper proposes two R-tree based structures for indexing object trajectories in symbolic indoor space. The RTR-tree represents a trajectory as a set of line segments in a space spanned by positioning readers and time. The TP 2 R-tree applies a data transformation that yields a representation of trajectories as points with extension along the time dimension. The paper details the structure, node organization strategies, and query processing algorithms for each index. An empirical performance study suggests that the two indexes are effective, efficient, and robust. The study also elicits the circumstances under which our proposals perform the best.

Vertical Indexing for Moving Objects in Multifloor Environments

“…e earliest paper on this topic is [7]. ey proposed two types of index trees: RTR-tree and TP2R-tree.…”

“…However, the trajectories are treated as line segments in the indoor space (see Figure 3). e insertion of new index entries into an RTR-tree is carried out similar to the R-tree; however, the calculation of the MBR area will be based on the RFID readers, where the calculation of the area of an MBR in the RTR-tree is Area (readerID max -readerID min ) * (t≺ t≻) [7]. In order to deal with a range query, the search will be conducted in the same way as for the R-tree; however, instead of searching on Euclidean space, the search will be done on a set of RFID readers.…”

“…People conduct most of their day-to-day activities in indoor environments, such as entertaining, living, working, and shopping. Studies have shown that people spend around 80% of their lives indoors [2,[5][6][7][8][9].…”

Tracking the Dynamic Distribution of People in Indoor Space with Noisy Partitioning Sensors

“…Indoor has become a new data management frontier recently ( [9], [10], [11], [20], [21]). Graph model based indoor tracking was discussed in [10].…”

“…Indoor space was represented using several graphs, such as base graph and deployment graph based on placement of RFID readers. Jensen et al [11] studied the trajectory indexing of moving objects in symbolic indoor space. Assuming trajectories of moving objects in indoor space can be collected by RFID readers, they proposed two R-tree based structures to index the trajectories.…”