Scalable continuous range monitoring of moving objects in symbolic indoor space

Abstract

Indoor spaces accommodate large populations of individuals. The continuous range monitoring of such objects can be used as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. Indoor space differs from outdoor space in that symbolic locations, e.g., rooms, rather than Euclidean positions or spatial network locations are important. In addition, positioning based on presence sensing devices, rather than, e.g., GPS, is assumed. Such devices report the objects in their activation ranges. We propose an incremental, query-aware continuous range query processing technique for objects moving in this setting. A set of critical devices is determined for each query, and only the observations from those devices are used to continuously maintain the query result. Due to the limitations of the positioning devices, queries contain certain and uncertain results. A maximum-speed constraint on object movement is used to refine the latter results. A comprehensive experimental study with both synthetic and real data suggests that our proposal is efficient and scalable.

Towards Mobile Information Systems for Indoor Space

“…But the differences between indoor and outdoor space models make the researches on these queries significant. Indoor range query [11,53,66] and indoor NN query [11,12,24] have attracted many researchers to work on them. According to the different temporal predicates, distance-aware queries can be categorized into snapshot queries or continuous queries.…”

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].…”

“…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. Range monitoring of moving objects and kNN queries were studied in [20] and [21], respectively. All these works used a graph model to represent the indoor space as we do in this paper, but they do not deal with people counting.…”

“…Indoor space has become a new data management frontier recently [21], [9], [20], [10]. Keeping counts of people (people counting) in different indoor areas is a useful task in various applications including smart house with automatically controlled air-conditioning and lighting, shopping assistance allocation, and business intelligence.…”

Research on Extension of Sparql Ontology Query Language Considering the Computation of Indoor Spatial Relations

“…A lattice-based semantic location model (LSLM) for the indoor environment was proposed which can better support semantic navigation and the nearest neighbor query for indoor space (Li D et al, 2008). Also, methodologies on range query (Yang B et al, 2009) and k nearest neighbor query (Yang B et al, 2010) for indoor space were carried out. However, all models above are still not enough to support complicate semantic query of indoor space, e.g.…”