Towards Mobile Information Systems for Indoor Space

Zhang, Xiaoxiang; Jin, Peiquan; Yue, Lihua; Wang, Na; Li, Qianyuan

doi:10.1155/2016/9673048

Cited by 2 publications

(6 citation statements)

References 65 publications

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“…However, it is not a trivial task to detect highly influential positions from indoor moving trajectories [3]. The challenges are twofold.…”

Section: Introductionmentioning

confidence: 99%

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Cui

2020

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The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

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“…However, it is not a trivial task to detect highly influential positions from indoor moving trajectories [3]. The challenges are twofold.…”

Section: Introductionmentioning

confidence: 99%

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Cui

2020

Information

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“…furniture). According to Zhang et al (2016), geometric model, symbolic model, object feature model, and hybrid model are existing indoor space models. Geometric model focuses on the geometric represention of indoor space which is mainly for visualization, indoor navigation and Computer Aided Design (CAD).…”

Section: Introductionmentioning

confidence: 99%

“…Geometric model focuses on the geometric represention of indoor space which is mainly for visualization, indoor navigation and Computer Aided Design (CAD). Geometric model can support representation of location and direction but it is not helpful for indoor navigation queries (Zhang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Symbolic model consists of four categories including semantic-based, topology-based, lattice-based and graph-based. There are several researches based on the symbolic model (Li and Lee, 2008;Yang et al, 2009) which represents indoor connectivity but can hardly support geometrical features in indoor space (Zhang et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

“…wall, roof, floor) and contain semantic information and topology in UML classes. IndoorGML cannot represent the geometrical feature of indoor elements and cannot support indoor distance-aware queries either (Zhang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid 3D Indoor Space Model

Jamali

Rahman

Bogusławski

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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GIS integrates spatial information and spatial analysis. An important example of such integration is for emergency response which requires route planning inside and outside of a building. Route planning requires detailed information related to indoor and outdoor environment. Indoor navigation network models including Geometric Network Model (GNM), Navigable Space Model, sub-division model and regular-grid model lack indoor data sources and abstraction methods. In this paper, a hybrid indoor space model is proposed. In the proposed method, 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. This research proposes a method of indoor space modeling for the buildings which do not have proper 2D/3D geometrical models or they lack semantic or topological information. The proposed hybrid model consists of topological, geometrical and semantical space.

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Towards Mobile Information Systems for Indoor Space

Cited by 2 publications

References 65 publications

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Discovering Influential Positions in RFID-Based Indoor Tracking Data

A Hybrid 3D Indoor Space Model

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