scholarly journals Ephemeral ID Beacon-Based Improved Indoor Positioning System

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 622 ◽  
Author(s):  
Jinsu Kang ◽  
Jeonghoon Seo ◽  
Yoojae Won
Keyword(s):  
Tracking System ◽  
Rapid Development ◽  
Communication Technologies ◽  
Location Based Services ◽  
Received Signal Strength Indicator ◽  
Indoor Location ◽  
Indoor Space ◽  
Outdoor Spaces ◽  
Physical Barriers ◽  
Positioning Algorithm

Recently, the rapid development of mobile devices and communication technologies has dramatically increased the demand for location-based services that provide users with location-oriented information and services. User location in outdoor spaces is measured with high accuracy using GPS. However, because the indoor reception of GPS signals is not smooth, this solution is not viable in indoor spaces. Many on-going studies are exploring new approaches for indoor location measurement. One popular technique involves using the received signal strength indicator (RSSI) values from the Bluetooth Low Energy (BLE) beacons to measure the distance between a mobile device and the beacons and then determining the position of the user in an indoor space by applying a positioning algorithm such as the trilateration method. However, it remains difficult to obtain accurate data because RSSI values are unstable owing to the influence of elements in the surrounding environment such as weather, humidity, physical barriers, and interference from other signals. In this paper, we propose an indoor location tracking system that improves performance by correcting unstable RSSI signals received from BLE beacons. We apply a filter algorithm based on the average filter and the Kalman filter to reduce the error range of results calculated using the RSSI values.

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.

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.

scholarly journals Smartphone-Based Indoor Pedestrian Tracking Using Geo-Magnetic Observations

2013 ◽  
Vol 9 (2) ◽  
pp. 123-137 ◽  
Author(s):  
Sungnam Lee ◽  
Yohan Chon ◽  
Hojung Cha
Keyword(s):  
Environmental Changes ◽  
Tracking System ◽  
Magnetic Sensors ◽  
Location Based Services ◽  
Location Tracking ◽  
Magnetic Fluctuations ◽  
Pedestrian Tracking ◽  
Estimation Model ◽  
Indoor Location ◽  
Extensive Evaluation

With the widespread use of smartphones, the use of location-based services (LBS) with smartphones has become an active research issue. The accurate measurement of user location is necessary to provide LBS. While outdoor locations are easily obtained with GPS, indoor location information is difficult to acquire. Previous work on indoor location tracking systems often relied on infrastructures that are influenced by environmental changes and temporal differences. Several studies have proposed infrastructure-less systems that are independent of the surroundings, but these works generally required non-trivial computation time or energy costs. In this paper, we propose an infrastructure-less pedestrian tracking system in indoor environments. The system uses accelerometers and magnetic sensors in smartphones without pre-installed infrastructure. We reduced the cumulative error of location tracking by geo-magnetic observations at corners and spots with magnetic fluctuations. In addition, we developed a robust estimation model that is tolerant to false positives, as well as a mobility model that reflects the characteristics of multiple sensors. Extensive evaluation in a real environment indicates that our system is accurate and cost-effective.

scholarly journals Development of an Omnidirectional-Image-Based Data Model through Extending the IndoorGML Concept to an Indoor Patrol Service

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Hyo-jin Jung ◽  
Jiyeong Lee
Keyword(s):  
Data Model ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Location ◽  
Indoor Space ◽  
Omnidirectional Image ◽  
Space Data ◽  
Omnidirectional Images ◽  
Indoor Spaces ◽  
Topological Data

Different indoor representation methods have been studied for their ability to provide indoor location-based services (LBS). Among them, omnidirectional imaging is one of the most typical and simple methods for representing an indoor space. However, a georeferenced omnidirectional image cannot be used for simple attribute searches, spatial queries, and spatial awareness analyses. To perform these functions, topological data are needed to define the features of and spatial relationships among spatial objects including indoor spaces as well as facilities like CCTV cameras considered in patrol service applications. Therefore, this study proposes an indoor space application data model for an indoor patrol service that can implement functions suited to linking indoor space data and service objects. In order to do this, the study presents a method for linking data between omnidirectional images representing indoor spaces and topological data on indoor spaces based on the concept of IndoorGML. Also, we conduct an experimental implementation of the integrated 3D indoor navigation model for patrol service using GIS data. Based on the results, we evaluate the benefits of using such a 3D data fusion method that integrates omnidirectional images with vector-based topological data models based on IndoorGML for providing indoor LBS in built environments.

scholarly journals A hybrid fusion of wireless signals and RGB image for indoor positioning

2018 ◽  
Vol 14 (2) ◽  
pp. 155014771875766 ◽  
Author(s):  
Jichao Jiao ◽  
Fei Li ◽  
Weihua Tang ◽  
Zhongliang Deng ◽  
Jichang Cao
Keyword(s):  
Three Dimensional ◽  
Texture Feature ◽  
Local Binary Patterns ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Coarse Grained ◽  
Wireless Data ◽  
Indoor Location ◽  
Matching Process ◽  
Positioning Algorithm

In this article, we propose a new indoor positioning algorithm using smartphones, where wireless signals and images are deeply combined together to improve the positioning performance. Our approach is based on the use of local binary patterns’ feature, which has the advantages of rotation invariance and scale invariance. Moreover, the term “uniform” are fundamental properties of local image textures and their occurrence histogram is proven to be a very powerful texture feature. Besides, the received signal strength acts as a reliable cue on a person’s identity. We first obtain a coarse-grained estimation based on the visualization of wireless signals, which are presented by a vector, making use of fingerprinting methods. Then, we perform a matching process to determine correspondences between two-dimensional pixels and three-dimensional points based on images collected by the smartphone. After being evaluated by experiments, our proposed method demonstrates that the combination of the visual and the wireless data significantly improves the positioning accuracy and robustness. It can be widely applied to smartphones to better analyze human behavior and offer high-accuracy indoor location–based services.

scholarly journals INDOORGML – A STANDARD FOR INDOOR SPATIAL MODELING

2016 ◽  
Vol XLI-B4 ◽  
pp. 701-704
Author(s):  
Ki-Joune Li
Keyword(s):  
Spatial Data ◽  
Geometric Model ◽  
Spatial Models ◽  
Cell Number ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Space ◽  
Outdoor Spaces ◽  
Core Module ◽  
Outdoor Space

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

scholarly journals INDOORGML – A STANDARD FOR INDOOR SPATIAL MODELING

2016 ◽  
Vol XLI-B4 ◽  
pp. 701-704 ◽  
Author(s):  
Ki-Joune Li
Keyword(s):  
Spatial Data ◽  
Geometric Model ◽  
Spatial Models ◽  
Cell Number ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Indoor Space ◽  
Outdoor Spaces ◽  
Core Module ◽  
Outdoor Space

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

scholarly journals A wearable device for machine learning based elderly's activity tracking and indoor location system

2021 ◽  
Vol 10 (2) ◽  
pp. 927-939
Author(s):  
Nour Eddin Tabbakha ◽  
Chee Pun Ooi ◽  
Wooi Haw Tan ◽  
Yi-Fei Tan
Keyword(s):  
Machine Learning ◽  
Tracking System ◽  
The Elderly ◽  
Physical Activities ◽  
Wearable Device ◽  
Support Vector ◽  
Activity Tracking ◽  
Received Signal Strength Indicator ◽  
Indoor Location ◽  
Location System

The number of older people is increasing in many countries. By 2030, it is estimated that 15% of the overall population will be comprised of people aged 65 and above. Hence, the monitoring and tracking of elder activities to ensure they live an active life has become a major research topic in recent years. In this work, an elderly sub-activity tracking system is developed to detect the sub-activity of the elderly based on their physical activities and indoor location. The physical activities tracking system and indoor location system is combined in this project to enhance the context of the elderly activities (i.e. sub-activities as defined in this project). An indoor location system is developed by using Bluetooth Low Energy (BLE) beacon and BLE scanners to measure the Received Signal Strength Indicator (RSSI) signal to detect the location of the elderly. The activity tracking is carried out via a waist wearable device worn by the elderly. Random forest and Support Vector Machine (SVM) are used as machine learning classifiers to predict the activity and indoor location with an accuracy of 95.03% and 86.58%, respectively. The data from activity tracking and indoor location sub-systems will then be combined to derive the sub-activity and push to an online Internet of Things (IoT) platform for remote monitoring and notification.

scholarly journals Automatic Generation of High-Accuracy Stair Paths for Straight, Spiral, and Winder Stairs Using IFC-Based Models

2020 ◽  
Vol 9 (4) ◽  
pp. 215
Author(s):  
Will Y. Lin
Keyword(s):  
Emergency Evacuation ◽  
Automatic Generation ◽  
High Accuracy ◽  
Location Based Services ◽  
Test Cases ◽  
Floor Level ◽  
Indoor Location ◽  
Indoor Space ◽  
Space Model ◽  
Novel Approach

The indoor space model is the foundation of most indoor location-based services (LBS). A complete indoor space model includes floor-level paths and non-level paths. The latter includes passages connecting different floors or elevations such as stairs, elevators, escalators, and ramps. Most related studies have merely discussed the modeling and generation of floor-level paths, while those considering non-level paths usually simplify the formation and generation of non-level paths, especially stairs, which play an important role in emergency evacuation and response. Although the algorithm proposed by i-GIT approach, which considers both floor-level and non-level paths, can automatically generate paths of straight stairs, it is not applicable to the spiral stairs and winder stairs that are common in town houses and other public buildings. This study proposes a novel approach to generate high-accuracy stair paths that can support straight, spiral, and winder stairs. To implement and verify the proposed algorithm, 54 straight and spiral stairs provided by Autodesk Revit’s official website and three self-built winder stairs are used as test cases. The test results show that the algorithm can successfully produce the stair paths of most test cases (49/50), which comprehensively extends the applicability of the proposed algorithm.

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