Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries

Abstract

Abstract:With the emergence of various types of indoor positioning technologies (e.g., radio-frequency identification, Wi-Fi, and iBeacon), how to rapidly retrieve indoor cells and moving objects has become a key factor that limits those indoor applications. Euclidean distance-based indexing techniques for outdoor moving objects cannot be used in indoor spaces due to the existence of indoor obstructions (e.g., walls). In addition, currently, the indexing of indoor moving objects is mainly based on space-related query and less frequently on semantic query. To address these two issues, the present study proposes a multi-floor adjacency cell and semantic-based index (MACSI). By integrating the indoor cellular space with the semantic space, the MACSI subdivides open cells (e.g., hallways and lobbies) using space syntax and optimizes the adjacency distances between three-dimensionally connected cells (e.g., elevators and stairs) based on the caloric cost that extends single floor indoor space to three dimensional indoor space. Moreover, based on the needs of semantic query, this study also proposes a multi-granularity indoor semantic hierarchy tree and establishes semantic trajectories. Extensive simulation and real-data experiments show that-compared with the indoor trajectories delta tree (ITD-tree) and the semantic-based index (SI)-the MACSI produces more reliable query results with significantly higher semantic query and update efficiencies; has superior semantic expansion capability; and supports multi-granularity complex semantic queries.

Spatial data analysis for intelligent buildings: Awareness of context and data uncertainty

“…However, the analytical contexts in SDAIB are complicated and diverse, involving many aspects related to the building environment and the internal entities like human beings and sensory devices. Many existing works (Yang et al, 2010;Kamkarian and Hexmoor, 2012;Lin et al, 2016;Teng et al, 2017;Li et al, 2018cLi et al, , 2020bLiu et al, 2021c) find relevant analytical contexts according to the need of the analytical task and design specific data structures for them.…”

“…However, the analytical contexts in SDAIB are complicated and diverse, involving many aspects related to the building environment and the internal entities like human beings and sensory devices. Many existing works (Yang et al, 2010 ; Kamkarian and Hexmoor, 2012 ; Lin et al, 2016 ; Teng et al, 2017 ; Li et al, 2018c , 2020b ; Liu et al, 2021c ) find relevant analytical contexts according to the need of the analytical task and design specific data structures for them. In this light, it is desirable to have a general mechanism for efficiently and effectively modeling different types of analytical contexts, in order to facilitate the data modeling and data storage for SDAIB tasks.…”

3d Geospatial Indoor Navigation for Disaster Risk Reduction and Response in Urban Environment

“…A multi-floor adjacency cell and semantic-based index (MACSI) approach integrates the indoor cellular space with the semantic space optimizes the adjacency distances between three dimensionally connected cells (e.g. elevators and stairs) based on the caloric cost (Lin et al, 2016). Another space model is the "space in a space" model, where spaces may be related while other spaces are disjoint (Schabus et al, 2015).…”

Mobile Phone Indoor Scene Recognition Location Method Based on Semantic Constraint of Building Map

“…Building maps is an effective type of information representation of interior spatial elements, in which semantic information refers to that information that enables cell phones to better understand user movement rules, perceive user scenes, plan navigation routes, and is covered in multi-level and rich dimensionality in high precision maps of buildings [25][26][27]. Semantic information in building maps can better represent the user's scene [28].…”