SmartLocService: Place Identification Method Using Space Dependent Information for Indoor Location-Based Services

2014 ◽  
Author(s):  
Tatsuro Hachiya ◽  
Masaki Bandai
Keyword(s):  
Location Based Services ◽  
Indoor Location ◽  
Identification Method ◽  
Place Identification

Geometry-Aided BLE-Based Smartphone Positioning for Indoor Location-Based Services

2020 ◽  
Author(s):  
Branislav Rudic ◽  
Maria Anneliese Klaffenbock ◽  
Markus Pichler-Scheder ◽  
Dmitry Efrosinin ◽  
Christian Kastl
Keyword(s):  
Location Based Services ◽  
Indoor Location

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 Privacy vs. Reward in Indoor Location-Based Services

2016 ◽  
Vol 2016 (4) ◽  
pp. 102-122 ◽  
Author(s):  
Kassem Fawaz ◽  
Kyu-Han Kim ◽  
Kang G. Shin
Keyword(s):  
Location Privacy ◽  
Indoor Localization ◽  
Service Providers ◽  
Location Based Services ◽  
Location Information ◽  
Privacy Concerns ◽  
Indoor Location ◽  
Location Data ◽  
Personalized Services ◽  
Privacy Risks

AbstractWith the advance of indoor localization technology, indoor location-based services (ILBS) are gaining popularity. They, however, accompany privacy concerns. ILBS providers track the users’ mobility to learn more about their behavior, and then provide them with improved and personalized services. Our survey of 200 individuals highlighted their concerns about this tracking for potential leakage of their personal/private traits, but also showed their willingness to accept reduced tracking for improved service. In this paper, we propose PR-LBS (Privacy vs. Reward for Location-Based Service), a system that addresses these seemingly conflicting requirements by balancing the users’ privacy concerns and the benefits of sharing location information in indoor location tracking environments. PR-LBS relies on a novel location-privacy criterion to quantify the privacy risks pertaining to sharing indoor location information. It also employs a repeated play model to ensure that the received service is proportionate to the privacy risk. We implement and evaluate PR-LBS extensively with various real-world user mobility traces. Results show that PR-LBS has low overhead, protects the users’ privacy, and makes a good tradeoff between the quality of service for the users and the utility of shared location data for service providers.

Indoor location-based services

2018 ◽  
Vol 10 (2) ◽  
pp. 10-17 ◽  
Author(s):  
Muhammad Aamir Cheema
Keyword(s):  
Location Based Services ◽  
Indoor Location

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.

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