Research On Spatial Queries For Moving Objects In Indoor Space

People spend most of their time in indoor spaces such as office buildings, commercial centers, railway stations, airports, etc. At the same time, with the acceleration of urbanization, the interior space has become increasingly large and complex. As a result, more and more demand for indoor location-based services has emerged. Indoor location-based services have a wide range of applications in indoor security control, medical services, shopping promotions, emergency escape, space planning, interior navigation and many other fields. Queris for indoor moving objects, which are the basic problem of indoor location-based services, have important research value.Existing research work on spatial queries for moving objects mainly focuses on Euclidean space and road network space. While there is relatively few research for interior space. Indoor positioning technologies such as RFID, Bluetooth, etc. are all based on the principle of approximate analysis, so the location information obtained is intermittent and uncontinuous, which brings some uncertainty. At the same time, due to the complexity of the building structure and the diversity of indoor semantic entities, the traditional distance metrics and spatial models can not be applied. For these reasons, the existing query processing techniques for road network space and Euclidean space can not be directly applied to the interior space. In this paper, we study the common spatial queries for moving objects in indoor space based on the characteristics of indoor environment. The mian research work and contributions are summarized as follows:(1) Considering the uncertainty of indoor moving object data, the problem of probabilistic threshold reverse nearest neighbor queries for indoor moving objects(IPRNN) is studied. According to the topological relationship among indoor positioning equipments, we propose the device reachable graph model. And the concepts of step and busy step are used to represent indoor coarse-grained distance. On the basis of the device reachable graph model, an algorithm named MDP for indoor probabilistic threshold reverse nearest neighbor query is put forward. The algorithm consists of four parts: model pruning, indoor distance pruning, probability pruning and probability calculation. Experiments show that the MDP algorithm is effective and efficient.(2) The problem of indoor bichromatic reverse nearest neighbor queries(IBRNN) is studied. Considering the impact of the state of the door on interior distance, the concept of indoor shortest path distance is presented as the indoor distance metric. And the query processing algorithm of indoor bichromatic reverse nearest neighbor queries named Smart is proposed. The algorithm is based on the classic filter-refine framework. In the filtering step, we present path pruning, floor pruning and cell pruning strategies, which are used to reduce the search space and improve query efficiency.(3) Taking the motion direction of indoor moving objects into account, the problem of indoor direction-aware k nearest neighbor queries(IDk NN) is studied. For a given indoor location, we present the formal definition of toward and backward. Meanwhile, in order to facilitate the search and retrieval of indoor moving objects, an index IFI based on the direction semantic of indoor moving objects is put forward. On the basis of IFI, the query processing algorithm of indoor direction-aware k nearest neighbor queries named D2 is proposed. And the experimental results show that the algorithm D2 has good performance.