scholarly journals Discovering Influential Positions in RFID-Based Indoor Tracking Data

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 330
Author(s):  
Ye Jin ◽  
Lizhen Cui
Keyword(s):  
Moving Objects ◽  
Indoor Localization ◽  
Rapid Development ◽  
Shopping Mall ◽  
Position Tracking ◽  
Tracking Data ◽  
H Index ◽  
Indoor Space ◽  
Indoor Tracking ◽  
Indoor Position Tracking

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.

scholarly journals Towards Mobile Information Systems for Indoor Space

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoxiang Zhang ◽  
Peiquan Jin ◽  
Lihua Yue ◽  
Na Wang ◽  
Qianyuan Li
Keyword(s):  
Information Systems ◽  
Mobile Applications ◽  
Moving Objects ◽  
Rapid Development ◽  
Information Management System ◽  
Future Research ◽  
Indoor Environments ◽  
Indoor Space ◽  
Mobile Information ◽  
Mobile Information Systems

With the rapid development of Internet of things (IOT) and indoor positioning technologies such as Wi-Fi and RFID, indoor mobile information systems have become a new research hotspot. Based on the unique features of indoor space and urgent needs on indoor mobile applications, in this paper we analyze some key issues in indoor mobile information systems, including positioning technologies in indoor environments, representation models for indoor spaces, query processing techniques for indoor moving objects, and index structures for indoor mobile applications. Then, we present an indoor mobile information management system named IndoorDB. Finally, we give some future research topics about indoor mobile information systems.

scholarly journals Location-Aware POI Recommendation for Indoor Space by Exploiting WiFi Logs

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zengwei Zheng ◽  
Yuanyi Chen ◽  
Sinong Chen ◽  
Lin Sun ◽  
Dan Chen
Keyword(s):  
Indoor Localization ◽  
User Preference ◽  
Shopping Mall ◽  
Behaviour Pattern ◽  
Indoor Space ◽  
Point Of Interest ◽  
Location Aware ◽  
Poi Recommendation ◽  
Shopping Experience ◽  
Two Phases

Indoor shopping trajectories provide us with a new approach to understanding user’s behaviour pattern in urban shopping mall, which can be derived from user-generated WiFi logs using indoor localization technology. In this paper, we propose a location-aware Point-of-Interest (POI) recommendation service in urban shopping mall that offers a user a set of indoor POIs by considering both personal interest and location preference. The POI recommendation service cannot only improve user’s shopping experience but also help the store owner better understand user’s shopping preference and intent. Specifically, the proposed method consists of two phases: offline modelling and online recommendation. The offline modelling phase is designed to learn user preference by mining his/her historical shopping trajectories. The online recommendation phase automatically produces top-k recommended POIs based on the learnt preference. To demonstrate the utility of our proposed approach, we have performed a comprehensive experiment evaluation on a real-world dataset collected by 468 users over 33 days. The experimental results show that the proposed recommendation service achieves much better recommendation performance than several existing benchmark methods.

SiFi

2021 ◽  
Vol 2 (3) ◽  
pp. 1-21
Author(s):  
Deke Guo ◽  
Xiaoqiang Teng ◽  
Yulan Guo ◽  
Xiaolei Zhou ◽  
Zhong Liu
Keyword(s):  
Rapid Development ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Location Based Services ◽  
Shopping Mall ◽  
Inference Problem ◽  
Indoor Location ◽  
Label Semantics ◽  
Geometrical Constraints ◽  
Indoor Scenes

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

Let "Ruin" into a "Home" — Create a Ruins Environment in the Contemporary Urban with the Model of Ecomuseum

2011 ◽  
Vol 368-373 ◽  
pp. 3659-3662 ◽  
Author(s):  
Xiao Min Wang ◽  
Fan Li
Keyword(s):  
Sustainable Development ◽  
Natural Environment ◽  
Rapid Development ◽  
Historical Context ◽  
Community Life ◽  
Indoor Space ◽  
Comprehensive Rehabilitation ◽  
Urban Sites ◽  
Organically Combine

Facing the current rapid development of economic and the constantly renewal of urban appearance, this article discusses the ideas about creating a ruins environment in the contemporary urban with the model of “Ecomuseum” from the point of sustainable development. Through the establishment of ecomuseum , the exhibition activities happen from indoor space to outdoor place; the exhibition content organically combine with the humanistic landscape and landmarks, natural environment and community life. This space mode not only focuses on ruins itself, but also tends to comprehensive rehabilitation and utilization about the ruins area environment. This mode play an important role for the Overall protection of the urban sites and continuous inheritance of historical context.

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

scholarly journals A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 875 ◽  
Author(s):  
Xiaochao Dang ◽  
Xiong Si ◽  
Zhanjun Hao ◽  
Yaning Huang
Keyword(s):  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Signal Interference ◽  
Indoor Environments ◽  
Phase Information ◽  
Localization Accuracy ◽  
Localization Method ◽  
Indoor Location ◽  
Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

A Study of Digitally Enhanced People–Space Interaction: A Place-Centric Perspective

2019 ◽  
pp. 120633121988135
Author(s):  
Weijia Wang
Keyword(s):  
Rapid Development ◽  
Physical Space ◽  
Shopping Mall ◽  
Future Research ◽  
City Development ◽  
Multimethod Research ◽  
Information And Communication ◽  
Future Research Directions ◽  
Digitally Enhanced ◽  
Digital Engagement

With the rapid development of information and communication technology, physical space has been increasingly enhanced by digital technology. The way people and space interact is significantly changed. In particular, an increased interest in the notion of place has emerged in examining smart city development, digital engagement, and people–technology relationships in hybrid physical–digital space. In this article, a place-centric perspective is employed to investigate digitally enhanced people–space interactions. Based on a multimethod research design that combines physical survey, field observation, and interview, this research employs a case study of a shopping mall to examine people’s everyday practices of digitally enabled placemaking. It is found that a range of digital interfaces functions as a system at various proxemic scales in hybrid space, which open up new spatialities by mediating trans-scale placemaking practices. Type-forms of digitally enabled people–space interactions are also summarized. The paper concludes that digitally enabled placemaking in this particular case is constituted within a closed mechanism. Future research directions are also raised at the end.

scholarly journals Possibilities of Using Kalman Filters in Indoor Localization

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1564
Author(s):  
Katerina Fronckova ◽  
Pavel Prazak
Keyword(s):  
Object Tracking ◽  
Stochastic Models ◽  
Kalman Filters ◽  
Indoor Environment ◽  
Moving Objects ◽  
Indoor Localization ◽  
Low Energy ◽  
Localization Error ◽  
Energy Data ◽  
The Mean

Kalman filters are a set of algorithms based on the idea of a filter described by Rudolf Emil Kalman in 1960. Kalman filters are used in various application domains, including localization, object tracking, and navigation. The text provides an overview and discussion of the possibilities of using Kalman filters in indoor localization. The problems of static localization and localization of dynamically moving objects are investigated, and corresponding stochastic models are created. Three algorithms for static localization and one algorithm for dynamic localization are described and demonstrated. All algorithms are implemented in the MATLAB software, and then their performance is tested on Bluetooth Low Energy data from a real indoor environment. The results show that by using Kalman filters, the mean localization error of two meters can be achieved, which is one meter less than in the case of using the standard fingerprinting technique. In general, the presented principles of Kalman filters are applicable in connection with various technologies and data of various nature.

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