Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

An Energy-efficient and Lightweight Indoor Localization System for Internet-of-Things (IoT) Environments

Published: 26 March 2018 Publication History

Abstract

Each and every spatial point in an indoor space has its own distinct and stable fingerprint, which arises owing to the distortion of the magnetic field induced by the surrounding steel and iron structures. This phenomenon makes many indoor positioning techniques rely on the magnetic field as an important source of localization. Most of the existing studies, however, have leveraged smartphones that have a relatively high computational power and many sensors. Thus, their algorithmic complexity is usually high, especially for commercial location-based services. In this paper, we present an energy-efficient and lightweight system that utilizes the magnetic field for indoor positioning in Internet of Things (IoT) environments. We propose a new hardware design of an IoT device that has a BLE interface and two sensors (magnetometer and accelerometer), with the lifetime of one year when using a coin-size battery. We further propose an augmented particle filter framework that features a robust motion model and a localization heuristic with small sensory data. The prototype-based evaluation shows that the proposed system achieves a median accuracy of 1.62 m for an office building, while exhibiting low computational complexity and high energy efficiency.

References

[1]
Michael Angermann, Martin Frassl, Marek Doniec, Brian J Julian, and Patrick Robertson. 2012. Characterization of the indoor magnetic field for applications in localization and mapping. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on. IEEE, 1--9.
[2]
Paramvir Bahl and Venkata N Padmanabhan. 2000. RADAR: An in-building RF-based user location and tracking system. In INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings., Vol. 2. IEEE, 775--784.
[3]
Stephane Beauregard and Harald Haas. 2006. Pedestrian dead reckoning: A basis for personal positioning. In Proceedings of the 3rd Workshop on Positioning, Navigation and Communication. 27--35.
[4]
Donald J Berndt and James Clifford. 1994. Using dynamic time warping to find patterns in time series. In KDD workshop, Vol. 10. Seattle, WA, 359--370.
[5]
Stephen Butterworth. 1930. On the theory of filter amplifiers. Wireless Engineer 7, 6 (1930), 536--541.
[6]
Krishna Chintalapudi, Anand Padmanabha Iyer, and Venkata N Padmanabhan. 2010. Indoor localization without the pain. In Proceedings of the sixteenth annual international conference on Mobile computing and networking. ACM, 173--184.
[7]
John Chon and Hojung Cha. 2011. Lifemap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing 10, 2 (2011), 58--67.
[8]
Jaewoo Chung, Matt Donahoe, Chris Schmandt, Ig-Jae Kim, Pedram Razavai, and Micaela Wiseman. 2011. Indoor location sensing using geo-magnetism. In Proceedings of the 9th international conference on Mobile systems, applications, and services. ACM, 141--154.
[9]
Energizer. 2017. Energizer CR2450 Specification. http://data.energizer.com/pdfs/cr2450.pdf. (2017).
[10]
Andreas Ettlinger and Günther Retscher. 2016. Positioning using ambient magnetic fields in combination with Wi-Fi and RFID. In Indoor Positioning and Indoor Navigation (IPIN), 2016 International Conference on. IEEE, 1--8.
[11]
Dominik Gusenbauer, Carsten Isert, and Jens Krösche. 2010. Self-contained indoor positioning on off-the-shelf mobile devices. In Indoor positioning and indoor navigation (IPIN), 2010 international conference on. IEEE, 1--9.
[12]
Janne Haverinen and Anssi Kemppainen. 2009. Global indoor self-localization based on the ambient magnetic field. Robotics and Autonomous Systems 57, 10 (2009), 1028--1035.
[13]
Benjamin Kempke, Pat Pannuto, and Prabal Dutta. 2015. PolyPoint: Guiding indoor quadrotors with ultra-wideband localization. In Proceedings of the 2nd International Workshop on Hot Topics in Wireless. ACM, 16--20.
[14]
Byunghun Kim, Myungchul Kwak, Jeongkeun Lee, and Ted Taekyoung Kwon. 2014. A multi-pronged approach for indoor positioning with WiFi, magnetic and cellular signals. In Indoor Positioning and Indoor Navigation (IPIN), 2014 International Conference on. IEEE, 723--726.
[15]
Seong-Eun Kim, Yong Kim, Jihyun Yoon, and Eung Sun Kim. 2012. Indoor positioning system using geomagnetic anomalies for smartphones. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on. IEEE, 1--5.
[16]
Kionix. 2016. KMX62-1031 Specification rev. 3.0. http://www.kionix.com/product/KMX62-1031. (2016).
[17]
Binghao Li, Thomas Gallagher, Andrew G Dempster, and Chris Rizos. 2012. How feasible is the use of magnetic field alone for indoor positioning?. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on. IEEE, 1--9.
[18]
Fan Li, Chunshui Zhao, Guanzhong Ding, Jian Gong, Chenxing Liu, and Feng Zhao. 2012. A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 421--430.
[19]
Zhenguang Liu, Luming Zhang, Qi Liu, Yifang Yin, Li Cheng, and Roger Zimmermann. 2017. Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective. IEEE Transactions on Multimedia 19, 4 (2017), 874--888.
[20]
Nesma Mohssen, Rana Momtaz, Heba Aly, and Moustafa Youssef. 2014. It's the human that matters: accurate user orientation estimation for mobile computing applications. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 70--79.
[21]
Nordic. 2016. nRF52832 Product Specification 1.0. https://www.nordicsemi.com/eng/Products/Bluetooth-low-energy/nRF52832. (2016).
[22]
Talat Ozyagcilar. 2012. Calibrating an ecompass in the presence of hard and soft-iron interference. Freescale Semiconductor Ltd (2012).
[23]
Karunakar Pothuganti and Anusha Chitneni. 2014. A comparative study of wireless protocols: Bluetooth, UWB, ZigBee, and Wi-Fi. Advance in Electronic and Electric Engineering 4, 6 (2014), 655--662.
[24]
Azkario Rizky Pratama, Risanuri Hidayat, et al. 2012. Smartphone-based pedestrian dead reckoning as an indoor positioning system. In System Engineering and Technology (ICSET), 2012 International Conference on. IEEE, 1--6.
[25]
Anshul Rai, Krishna Kant Chintalapudi, Venkata N Padmanabhan, and Rijurekha Sen. 2012. Zee: Zero-effort crowdsourcing for indoor localization. In Proceedings of the 18th annual international conference on Mobile computing and networking. ACM, 293--304.
[26]
Yuanchao Shu, Cheng Bo, Guobin Shen, Chunshui Zhao, Liqun Li, and Feng Zhao. 2015. Magicol: Indoor localization using pervasive magnetic field and opportunistic WiFi sensing. IEEE Journal on Selected Areas in Communications 33, 7 (2015), 1443--1457.
[27]
Yuanchao Shu, Kang G Shin, Tian He, and Jiming Chen. 2015. Last-mile navigation using smartphones. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, 512--524.
[28]
Matti Siekkinen, Markus Hiienkari, Jukka K Nurminen, and Johanna Nieminen. 2012. How low energy is bluetooth low energy? comparative measurements with zigbee/802.15.4. In Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 232--237.
[29]
Bluetooth SIG. 2014. Bluetooth Specification version 4.2. https://www.bluetooth.org/DocMan/handlers/DownloadDoc.ashx?doc_id=286439. (2014).
[30]
Bluetooth SIG. 2016. Bluetooth Specification version 5.0. https://www.bluetooth.org/DocMan/handlers/DownloadDoc.ashx?doc_id=4210438_ga=2.95164572.1071486365.1502719791-975213286.1502719791. (2016).
[31]
SKT. 1997. SK Telecom Co., Ltd. http://www.sktelecom.com. (1997).
[32]
Kalyan Pathapati Subbu, Brandon Gozick, and Ram Dantu. 2013. Locateme: Magnetic-fields-based indoor localization using smartphones. ACM Transactions on Intelligent Systems and Technology (TIST) 4, 4 (2013), 73.
[33]
S Suksakulchai, S Thongchai, DM Wilkes, and K Kawamura. 2000. Mobile robot localization using an electronic compass for corridor environment. In Systems, Man, and Cybernetics, 2000 IEEE International Conference on, Vol. 5. IEEE, 3354--3359.
[34]
Lorenzo Taponecco, AA D'amico, and Umberto Mengali. 2011. Joint TOA and AOA estimation for UWB localization applications. IEEE Transactions on Wireless Communications 10, 7 (2011), 2207--2217.
[35]
Ilari Vallivaara, Janne Haverinen, Anssi Kemppainen, and Juha Röning. 2010. Simultaneous localization and mapping using ambient magnetic field. In Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 14--19.
[36]
Xian Wang, Paula Tarrío, Eduardo Metola, Ana Bernardos, and José Casar. 2012. Gesture recognition using mobile phone's inertial sensors. Distributed Computing and Artificial Intelligence (2012), 173--184.
[37]
Hongwei Xie, Tao Gu, Xianping Tao, Haibo Ye, and Jian Lu. 2016. A reliability-augmented particle filter for magnetic fingerprinting based indoor localization on smartphone. IEEE Transactions on Mobile Computing 15, 8 (2016), 1877--1892.
[38]
Hai-Bo Ye, Tao Gu, Xian-Ping Tao, and Jian Lv. 2015. Infrastructure-free floor localization through crowdsourcing. Journal of Computer Science and Technology 30, 6 (2015), 1249--1273.
[39]
Moustafa Youssef and Ashok Agrawala. 2005. The Horus WLAN location determination system. In Proceedings of the 3rd international conference on Mobile systems, applications, and services. ACM, 205--218.
[40]
Cemin Zhang, Michael Kuhn, Brandon Merkl, Aly E Fathy, and Mohamed Mahfouz. 2006. Accurate UWB indoor localization system utilizing time difference of arrival approach. In Radio and Wireless Symposium, 2006. IEEE, 515--518.
[41]
Chi Zhang and Xinyu Zhang. 2016. LiTell: indoor localization using unmodified light fixtures. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. ACM, 481--482.
[42]
Rui Zhang, Amir Bannoura, Fabian Höflinger, Leonhard M Reindl, and Christian Schindelhauer. 2013. Indoor localization using a smart phone. In Sensors Applications Symposium (SAS), 2013. IEEE, 38--42.
[43]
Yiyang Zhao, Chen Qian, Liangyi Gong, Zhenhua Li, and Yunhao Liu. 2015. LMDD: Light-weight Magnetic-based Door Detection with Your Smartphone. In Parallel Processing (ICPP), 2015 44th International Conference on. IEEE, 919--928.
[44]
Pengfei Zhou, Mo Li, and Guobin Shen. 2014. Use it free: Instantly knowing your phone attitude. In Proceedings of the 20th annual international conference on Mobile computing and networking. ACM, 605--616.
[45]
Shilin Zhu and Xinyu Zhang. 2017. Enabling High-Precision Visible Light Localization in Today's Buildings. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 96--108.
[46]
Yuan Zhuang, Jun Yang, You Li, Longning Qi, and Naser El-Sheimy. 2016. Smartphone-based indoor localization with bluetooth low energy beacons. Sensors 16, 5 (2016), 596.
[47]
Kathryn Zickuhr. 2013. Location-based services. Pew Research (2013), 679--695.

Cited By

View all
  • (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
  • (2024)Internet of things-based robust semi-analytical over ubiquitous data for indoor positioning geomagneticMeasurement: Sensors10.1016/j.measen.2024.10110733(101107)Online publication date: Jun-2024
  • (2023)Enhancing Indoor Positioning Accuracy: A Comprehensive Study on Euclidean Distance, Trilateration, Wi-Fi RTT and FTM Protocol IntegrationProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638235(173-180)Online publication date: 25-Nov-2023
  • Show More Cited By

Index Terms

  1. An Energy-efficient and Lightweight Indoor Localization System for Internet-of-Things (IoT) Environments

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 2, Issue 1
      March 2018
      1370 pages
      EISSN:2474-9567
      DOI:10.1145/3200905
      Issue’s Table of Contents
      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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 March 2018
      Accepted: 01 January 2018
      Revised: 01 November 2017
      Received: 01 August 2017
      Published in IMWUT Volume 2, Issue 1

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. BLE
      2. Indoor Localization
      3. Internet-of-Things
      4. Magnetic Field
      5. Particle Filter
      6. Wireless Communication

      Qualifiers

      • Research-article
      • Research
      • Refereed

      Funding Sources

      • Ministry of Education of the Republic of Korea and the National Research Foundation of Korea

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)36
      • Downloads (Last 6 weeks)4
      Reflects downloads up to 10 Nov 2024

      Other Metrics

      Citations

      Cited By

      View all
      • (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
      • (2024)Internet of things-based robust semi-analytical over ubiquitous data for indoor positioning geomagneticMeasurement: Sensors10.1016/j.measen.2024.10110733(101107)Online publication date: Jun-2024
      • (2023)Enhancing Indoor Positioning Accuracy: A Comprehensive Study on Euclidean Distance, Trilateration, Wi-Fi RTT and FTM Protocol IntegrationProceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems10.1145/3638209.3638235(173-180)Online publication date: 25-Nov-2023
      • (2023)Contact Tracing for Healthcare Workers in an Intensive Care UnitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109247:3(1-23)Online publication date: 27-Sep-2023
      • (2023)GC-LocProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694956:4(1-27)Online publication date: 11-Jan-2023
      • (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
      • (2023)Machine Learning Enabled Sleep Time Estimation (MLE-STE) Architecture for Indoor Positioning in Energy-Efficient Mobile Internet of Things2023 IEEE 9th World Forum on Internet of Things (WF-IoT)10.1109/WF-IoT58464.2023.10539448(01-06)Online publication date: 12-Oct-2023
      • (2023)Indoor Geomagnetic Positioning Using Direction-Aware Multiscale Recurrent Neural NetworksIEEE Sensors Journal10.1109/JSEN.2022.322795223:3(3321-3333)Online publication date: 1-Feb-2023
      • (2022)ILLOCProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172456:1(1-26)Online publication date: 29-Mar-2022
      • (2022)Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergencePeer-to-Peer Networking and Applications10.1007/s12083-022-01301-y15:3(1370-1384)Online publication date: 25-Feb-2022
      • Show More Cited By

      View Options

      Get Access

      Login options

      Full Access

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media