scholarly journals Indexing the Trajectories of Moving Objects in Symbolic Indoor Space

Author(s):  
Christian S. Jensen ◽  
Hua Lu ◽  
Bin Yang
Keyword(s):  
Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 330
Author(s):  
Ye Jin ◽  
Lizhen Cui

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.


2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoxiang Zhang ◽  
Peiquan Jin ◽  
Lihua Yue ◽  
Na Wang ◽  
Qianyuan Li

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.


2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Sultan Alamri ◽  
David Taniar ◽  
Kinh Nguyen

The indexing and tracking of objects moving in indoor spaces has increasingly become an important area of research, which presents a fundamentally different challenge. There are two main reasons for why indoor should be treated as cellular space. Firstly, an indoor space has entities, such as rooms and walls, that constrain the movement of the moving objects. Secondly, the relevant notion of locations of an object is cell based rather than an exact Euclidean coordinate. As a solution, in our earlier works, we proposed a cell-based indexing structure, called the C-tree, for indexing objects moving in indoor space. In this paper, we extend the C-tree to solve another interesting problem. It can be observed that many indoor spaces (such as shopping centers) contain wings/sections. For such a space, there are queries for which the wing/section location of an object, rather than the cellular location, is the relevant answer (e.g., “the object is in the east wing”). In this paper, we propose a new index structure, called the GMI-tree (“GMI” stands for “Graph-based Multidimensional Index”). The GMI-tree is based on two notions of distance, or equivalently, two notions of adjacency: one represents horizontal adjacency and the other represents vertical adjacency.


Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6107
Author(s):  
Won-Yeol Kim ◽  
Soo-Ho Tae ◽  
Dong-Hoan Seo

Fingerprinting is the term used to describe a common indoor radio-mapping positioning technology that tracks moving objects in real time. To use this, a substantial number of measurement processes and workflows are needed to generate a radio-map. Accordingly, to minimize costs and increase the usability of such radio-maps, this study proposes an access-point (AP)-centered window (APCW) radio-map generation network (RGN). The proposed technique extracts parts of a radio-map in the form of a window based on AP floor plan coordinates to shorten the training time while enhancing radio-map prediction accuracy. To provide robustness against changes in the location of the APs and to enhance the utilization of similar structures, the proposed RGN, which employs an adversarial learning method and uses the APCW as input, learns the indoor space in partitions and combines the radio-maps of each AP to generate a complete map. By comparing four learning models that use different data structures as input based on an actual building, the proposed radio-map learning model (i.e., APCW-based RGN) obtains the highest accuracy among all models tested, yielding a root-mean-square error value of 4.01 dBm.


2009 ◽  
Author(s):  
Piers D. Howe ◽  
Michael A. Cohen ◽  
Yair Pinto ◽  
Todd S. Horowitz
Keyword(s):  

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fatima Ameen ◽  
Ziad Mohammed ◽  
Abdulrahman Siddiq

Tracking systems of moving objects provide a useful means to better control, manage and secure them. Tracking systems are used in different scales of applications such as indoors, outdoors and even used to track vehicles, ships and air planes moving over the globe. This paper presents the design and implementation of a system for tracking objects moving over a wide geographical area. The system depends on the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies without requiring the Internet service. The implemented system uses the freely available GPS service to determine the position of the moving objects. The tests of the implemented system in different regions and conditions show that the maximum uncertainty in the obtained positions is a circle with radius of about 16 m, which is an acceptable result for tracking the movement of objects in wide and open environments.


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