research-article

Open access

Smartphone-assisted Automatic Indoor Localization of BLE-enabled Appliances Using BLE and GNSS Signals

Authors:

Masayuki Fujiwara,

Tomoya Nakatani,

Joseph Korpela,

Takuya Maekawa,

Takahiro HaraAuthors Info & Claims

BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

Pages 80 - 89

https://doi.org/10.1145/3408308.3427981

Published: 18 November 2020 Publication History

Abstract

Information about indoor locations of intelligent appliances is necessary to provide smart services by collaboration across the appliances. This study proposes a method to automatically estimate the indoor coordinates of Bluetooth Low Energy (BLE)-enabled appliances with the help of unlabeled BLE and global navigation satellite system (GNSS) data obtained from a smartphone in the environment (e.g., a smartphone carried by a person working or living in the environment). We analyze GNSS data to detect when the smartphone is located near a window, and then estimate the orientation of the outer wall into which that window is installed. By combining the rough indoor positions of the smartphone with the distances to each appliance estimated by BLE signals, we estimate the absolute coordinates of the appliances. We have evaluated the proposed method in real-world environments and achieved an average error distance of about 3 meters.

References

[1]

Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32.

Digital Library

[2]

Jonghwa Choi, Dongkyoo Shin, and Dongil Shin. 2005. Research and implementation of the context-aware middleware for controlling home appliances. IEEE Transactions on Consumer Electronics 51, 1 (2005), 301--306.

Digital Library

[3]

Masahiro Fujii and Yoshiaki Mori. 2016. A study on indoor positioning systems based on SkyPlot mask. In 2016 IEEE 5th Global Conference on Consumer Electronics. IEEE, 1--2.

[4]

Yanying Gu, Anthony Lo, and Ignas Niemegeers. 2009. A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys & Tutorials 11, 1 (2009), 13--32.

Digital Library

[5]

Taisei Hayashi, Daisuke Taniuchi, Joseph Korpela, and Takuya Maekawa. 2017. Spatio-temporal adaptive indoor positioning using an ensemble approach. Pervasive and Mobile Computing 41 (2017), 319--332.

Digital Library

[6]

Mike Hazas, James Scott, and John Krumm. 2004. Location-aware computing comes of age. Computer 37, 2 (2004), 95--97.

Digital Library

[7]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. CoRR abs/1502.01852 (2015). arXiv:1502.01852 http://arxiv.org/abs/1502.01852

[8]

Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. CoRR abs/1502.03167 (2015). arXiv:1502.03167 http://arxiv.org/abs/1502.03167

Digital Library

[9]

Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[10]

Mikkel Baun Kjærgaard, Henrik Blunck, Torben Godsk, Thomas Toftkjær, Dan Lund Christensen, and Kaj Grønbæk. 2010. Indoor positioning using GPS revisited. In International conference on pervasive computing. Springer, 38--56.

Digital Library

[11]

Quan Kong, Takuya Maekawa, Taiki Miyanishi, and Takayuki Suyama. 2016. Selecting Home Appliances with Smart Glass Based on Contextual Information. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Heidelberg, Germany) (UbiComp '16). ACM, New York, NY, USA, 97--108. https://doi.org/10.1145/2971648.2971651

Digital Library

[12]

Anthony LaMarca, Yatin Chawathe, Sunny Consolvo, Jeffrey Hightower, Ian Smith, James Scott, Timothy Sohn, James Howard, Jeff Hughes, Fred Potter, et al. 2005. Place lab: Device positioning using radio beacons in the wild. In International Conference on Pervasive Computing(Pervasive 2005). 116--133.

Digital Library

[13]

Christos Laoudias, Demetrios Zeinalipour-Yazti, and Christos G Panayiotou. 2013. Crowdsourced indoor localization for diverse devices through radiomap fusion. In 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN). 1--7.

[14]

Hui Liu, Houshang Darabi, Pat Banerjee, and Jing Liu. 2007. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37, 6 (2007), 1067--1080.

Digital Library

[15]

Takuya Maekawa, Yutaka Yanagisawa, Yasushi Sakurai, Yasue Kishino, Koji Kamei, and Takeshi Okadome. 2009. Web Searching for Daily Living. In SIGIR 2009. 27--34.

[16]

Takuya Maekawa, Yutaka Yanagisawa, Yasushi Sakurai, Yasue Kishino, Koji Kamei, and Takeshi Okadome. 2012. Context-aware Web search in ubiquitous sensor environment. ACM Transactions on Internet Technology (ACM TOIT) 11, 3 (2012), 12:1--12:23.

[17]

Tomoya Nakatani, Takuya Maekawa, Masumi Shirakawa, and Takahiro Hara. 2018. Estimating the Physical Distance Between Two Locations with Wi-Fi Received Signal Strength Information Using Obstacle-aware Approach. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article 130 (Sept. 2018), 26 pages. https://doi.org/10.1145/3264940

Digital Library

[18]

Masayuki Ochiai, Masahiro Fujii, Atsushi Ito, Yu Watanabe, and Hiroyuki Hatano. 2014. A study on indoor position estimation based on fingerprinting using GPS signals. In 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 727--728.

[19]

Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout:a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15, 1 (2014), 1929--1958.

Digital Library

[20]

Masaya Tachikawa, Takuya Maekawa, and Yasuyuki Matsushita. 2016. Predicting location semantics combining active and passive sensing with environment-independent classifier. In UbiComp 2016. 220--231.

Digital Library

[21]

Daisuke Taniuchi and Takuya Maekawa. 2014. Robust Wi-Fi based indoor positioning with ensemble learning. In IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2014). 592--597.

[22]

Andreas Teuber, Bernd Eissfeller, and Thomas Pany. 2006. A two-stage fuzzy logic approach for wireless LAN indoor positioning. In IEEE/ION Position Location Navigation Symposium, Vol. 4. 730--738.

[23]

Yapeng Wang, Xu Yang, Yutian Zhao, Yue Liu, and Laurie Cuthbert. 2013. Bluetooth positioning using RSSI and triangulation methods. In IEEE Consumer Communications and Networking Conference (CCNC 2013). 837--842.

[24]

Tatsuya Yamazaki. 2005. Ubiquitous home: real-life testbed for home context-aware service. In First International Conference on Testbeds and Research Infrastructures for the DEvelopment of NeTworks and COMmunities. IEEE, 54--59.

Digital Library

[25]

Junyang Zhou, KM-K Chu, and JK-Y Ng. 2005. Providing location services within a radio cellular network using ellipse propagation model. In 19th International Conference on Advanced Information Networking and Applications (AINA 2005), Vol. 1. 559--564.

[26]

Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal. 1997. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software (TOMS) 23, 4 (1997), 550--560.

Digital Library

Cited By

Zhou HMaekawa T(2024)Predicting Signal Reception Information from GNSS Satellites in Indoor Environments without Site Survey: Towards Opportunistic Indoor Positioning Based on GNSS FingerprintingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785548:3(1-30)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678554
Zhou HMaekawa T(2023)GPS-assisted Indoor Pedestrian Dead ReckoningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694676:4(1-36)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569467
Chandra Shit RSharma S(2022)Ray‐tracing assisted fingerprinting for localization in IoT Health 4.0Transactions on Emerging Telecommunications Technologies10.1002/ett.457333:11Online publication date: 12-Jun-2022
https://doi.org/10.1002/ett.4573

Index Terms

Smartphone-assisted Automatic Indoor Localization of BLE-enabled Appliances Using BLE and GNSS Signals
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing
      2. Ubiquitous computing

Recommendations

Improved GNSS-based indoor positioning algorithm for mobile devices

The widespread use of global navigation satellite system (GNSS) receiver in mobile devices induces the adoption of effective GNSS-based indoor positioning algorithms exploiting low-cost hardware. In a previous study, we proposed a new architecture for ...
Smartphone based intelligent indoor positioning using fuzzy logic
Abstract
Smartphones are indispensable helpers of people in their daily routine, including functions that serve the disabled or aged people finding their outdoor location. This paper presents an experimental investigation of the indoor ...
Highlights
- An experimental investigation of indoor positioning algorithms.
- A fuzzy logic ...
GPMS: Enabling Indoor GNSS Positioning using Passive Metasurfaces
ACM MobiCom '24: Proceedings of the 30th Annual International Conference on Mobile Computing and Networking

Global Navigation Satellite System (GNSS) is extensively utilized for outdoor positioning and navigation. However, achieving high-precision indoor positioning is challenging due to the significant attenuation of GNSS signals indoors. To address this ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

BuildSys '20: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 2020

361 pages

ISBN:9781450380614

DOI:10.1145/3408308

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGEnergy: ACM Special Interest Group on Energy Systems and Informatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 November 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

BuildSys '20

Sponsor:

SIGEnergy

BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 18 - 20, 2020

Virtual Event, Japan

Acceptance Rates

BuildSys '20 Paper Acceptance Rate 38 of 139 submissions, 27%;

Overall Acceptance Rate 148 of 500 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
381
Total Downloads

Downloads (Last 12 months)104
Downloads (Last 6 weeks)36

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhou HMaekawa T(2024)Predicting Signal Reception Information from GNSS Satellites in Indoor Environments without Site Survey: Towards Opportunistic Indoor Positioning Based on GNSS FingerprintingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785548:3(1-30)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678554
Zhou HMaekawa T(2023)GPS-assisted Indoor Pedestrian Dead ReckoningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694676:4(1-36)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3569467
Chandra Shit RSharma S(2022)Ray‐tracing assisted fingerprinting for localization in IoT Health 4.0Transactions on Emerging Telecommunications Technologies10.1002/ett.457333:11Online publication date: 12-Jun-2022
https://doi.org/10.1002/ett.4573

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten