Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 2/2020

14.06.2019

Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach

verfasst von: Xuyu Wang, Zhitao Yu, Shiwen Mao

Erschienen in: Mobile Networks and Applications | Ausgabe 2/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the increasing demand for location-based services, indoor localization has attracted great interest. In this paper, we present DeepML, a deep long short-term memory (LSTM) based system for indoor localization using magnetic and light sensors on smartphones. We experimentally verify the feasibility of using bimodal data from magnetic and light sensors for indoor localization for closed environments where there is no ambient light. We then design the DeepML system, which first builds bimodal images by data preprocessing, and then trains a deep LSTM network in the offline phase. Newly received magnetic field and light data are then exploited for estimating the location of the mobile device using a probabilistic method. The extensive experiments verify the effectiveness of the proposed DeepML system.
Vorheriger Artikel Design of a Practical WSN Based Fingerprint Localization System

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Khelifi F, Bradai A, Benslimane A, Rawat P, Atri M (2018) A survey of localization systems in internet of things. Springer Mobile Networks and Applications Journal, pp 1–25
2.
Kumarasiri R, Alshamaileh K, Tran NH, Devabhaktuni V (2016) An improved hybrid RSS/TDOA wireless sensors localization technique utilizing Wi-Fi networks. Springer Mobile Networks and Applications 21(2):286–295CrossRef
3.
Kan C, Ding G, Wu Q, Zhang T (2018) Robust localization with crowd sensors: a data cleansing approach. Springer Mobile Networks and Applications 23(1):108–118CrossRef
4.
Gu Y, Lo A, Niemegeers I (2009) A survey of indoor positioning systems for wireless personal networks. IEEE Commun Surveys Tuts 11(1):13–32CrossRef
5.
Wang X, Mao S, Pandey S, Agrawal P (2014) CA2T: Cooperative antenna arrays technique for pinpoint indoor localization. In: Proc MobiSPC 2014, Niagara Falls, Canada, pp 392–399
6.
Bahl P, Padmanabhan VN (2000) Radar: an in-building RF-based user location and tracking system. In: Proc IEEE INFOCOM’00, Tel Aviv, Israel, pp 775–784
7.
Youssef M, Agrawala A (2005) The Horus WLAN location determination system. In: Proc ACM MobiSys’05, Seattle, WA, pp 205–218
8.
Caso G, De Nardis L (2017) Virtual and oriented WiFi fingerprinting indoor positioning based on multi-wall multi-floor propagation models. Springer Mobile Networks and Applications 22(5):825–833CrossRef
9.
Liu H-H (2017) The quick radio fingerprint collection method for a WiFi-based indoor positioning system. Springer Mobile Networks and Applications 22(1):61–71CrossRef
10.
Xiao J, Wu K, Yi Y, Ni L (2012) FIFS: Fine-grained indoor fingerprinting system. In: Proc IEEE ICCCN’12, Munich, Germany, pp 1–7
11.
Wang X, Gao L, Mao S, Pandey S (2017) CSI-Based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66(1):763–776
12.
Chung J, Donahoe M, Schmandt C, Kim I-J, Razavai P, Wiseman M (2011) Indoor location sensing using geo-magnetism. In: Proc ACM MobiSys’11, Bethesda, MD, pp 141–154
13.
Storms W, Shockley J, Raquet J (2010) Magnetic field navigation in an indoor environment. In: Proc IEEE UPINLBS’10, Kirkkonummi, Finland, pp 1–10
14.
Gozick B, Subbu KP, Dantu R, Maeshiro T (2011) Magnetic maps for indoor navigation. IEEE Trans Instrum Meas 60(12):3883–3891CrossRef
15.
Shu Y, Bo C, Shen G, Zhao C, Li L, Zhao F (2015) Magicol: Indoor localization using pervasive magnetic field and opportunistic WiFi sensing. IEEE J Sel Areas Commun 33(7):1443–1457CrossRef
16.
Ma Y, Dou Z, Jiang Q, Hou Z (2016) Basmag: an optimized HMM-based localization system using backward sequences matching algorithm exploiting geomagnetic information. IEEE Sensors J 16(20):7472–7482CrossRef
17.
Yang Z, Wang Z, Zhang J, Huang C, Zhang Q (2015) Wearables can afford: Light-weight indoor positioning with visible light. In: Proc ACM Mobisys’15, Florence, Italy, pp 317– 330
18.
Kuo Y-S, Pannuto P, Hsiao K-J, Dutta P (2014) Luxapose: Indoor positioning with mobile phones and visible light. In: Proc ACM MobiCom’14, Maui, HI, pp 447–458
19.
Li L, Hu P, Peng C, Shen G, Zhao F (2014) Epsilon: a visible light based positioning system. In: Proc USENIX NSDI’14, Seattle, WA, pp 331–343
20.
Zhang C, Zhang X (2016) LiTell: Robust indoor localization using unmodified light fixtures. In: Proc ACM MobiCom’16, New York, NY, pp 230–242
21.
Xu Q, Zheng R, Hranilovic S (2015) IDyLL: Indoor localization using inertial and light sensors on smartphones. In: Proc ACM UbiComp’15, Osaka, Japan, pp 307–318
22.
Zhao Z, Wang J, Zhao X, Peng C, Guo Q, Wu B (2017) NaviLight: Indoor localization and navigation under arbitrary lights. In: Proc IEEE INFOCOM’17, Atlanta, GA, pp 1–9
23.
Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: Continual prediction with lstm. J Neural Comput 12(10):2451–2471CrossRef
24.
Greff K, Srivastava RK, Koutníik J, Steunebrink BR, Schmidhuber J (2017) Lstm: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232MathSciNetCrossRef
25.
Graves A, Jaitly N, Mohamed A-r (2013) Hybrid speech recognition with deep bidirectional LSTM. In: Proc IEEE ASRU’13, Olomouc, Czech Republic, pp 273–278
26.
Ordonez FJ, Roggen D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. MDPI Sensors 16(1):115CrossRef
27.
Do T-H, Yoo M (2016) An in-depth survey of visible light communication based positioning systems. MPDI Sensors 16(5):678CrossRef
28.
29.
He S, Chan S-HG (2016) Wi-fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Commun Surveys Tuts 18(1):466–490. First QuarterCrossRef
30.
Win MZ, Shen Y, Dai W (2018) A theoretical foundation of network localization and navigation. Proc IEEE 106(7):1136–1165CrossRef
31.
Sun Y, Peng M, Zhou Y, Huang Y, Mao S (2018) Application of machine learning in wireless networks: Key techniques and open issues. arXiv:1809.​08707
32.
Rapinski J, Cellmer S (2016) Analysis of range based indoor positioning techniques for personal communication networks. Springer Mobile Networks and Applications 21(3):539–549CrossRef
33.
Wang X, Wang X, Mao S (2018) RF Sensing in the Internet of Things: a general deep learning framework. IEEE Commun Mag 56(9):62–67CrossRef
34.
Wang X, Gao L, Mao S, Pandey S (2015) Deepfi: Deep learning for indoor fingerprinting using channel state information. In: Proc WCNC’15, New Orleans, LA, pp 1666–1671
35.
Wang X, Gao L, Mao S (2015) Phasefi: Phase fingerprinting for indoor localization with a deep learning approach. In: Proc GLOBECOM’15, San Diego, CA, pp 1–6
36.
Wang X, Gao L, Mao S (2016) CSI Phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet of Things J 3(6):1113–1123CrossRef
37.
Wang X, Gao L, Mao S (2017) BiLoc: Bi-modality deep learning for indoor localization with 5 GHz commodity Wi-Fi. IEEE Access J 5(1):4209–4220CrossRef
38.
Xiao C, Yang D, Chen Z, Tan G (2017) 3-D BLE indoor localization based on denoising autoencoder. IEEE Access J 5:12751–12760CrossRef
39.
Gu F, Khoshelham K, Yu C, Shang J (2018) Accurate step length estimation for pedestrian dead reckoning localization using stacked autoencoders. IEEE Trans Instrum Meas, p 1
40.
Khatab ZE, Hajihoseini A, Ghorashi SA (2018) A fingerprint method for indoor localization using autoencoder based deep extreme learning machine. IEEE Sens Lett 2(1):1–4CrossRef
41.
Abbas M, Elhamshary M, Rizk H, Torki M, Youssef M (2019) WiDeep: WiFi-based accurate and robust indoor localization system using deep learning. In: IEEE PerCom’19, Kyoto, Japan
42.
Wang X, Wang X, Mao S (2017) CiFi: Deep convolutional neural networks for indoor localization with 5GHz Wi-Fi. In: Proc IEEE ICC 2017, Paris, France, pp 1–6
43.
44.
Wang X, Wang X, Mao S (2017) ResLoc: Deep residual sharing learning for indoor localization with CSI tensors. In: Proc IEEE PIMRC 2017, Montreal, Canada, pp 1–6
45.
Wang X, Yu Z, Mao S (2018) DeepML: Deep LSTM for indoor localization with smartphone magnetic and light sensors. In: Proc IEEE ICC 2017, Kansas City, MO, pp 1–6
46.
Sahar A, Han D (2018) An LSTM-based indoor positioning method using Wi-Fi signals. In: Proc ACM international conference on vision, image and signal processing, Las Vegas, NV, p Article No 43
47.
48.
Hu Y, Xiong Y, Huang W, Li X-Y, Yang P, Zhang Y, Mao X (2018) Lightitude: Indoor positioning using uneven light intensity distribution. Proc ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2(2):Artical 67CrossRef
Metadaten
Titel
Indoor Localization Using Smartphone Magnetic and Light Sensors: a Deep LSTM Approach
verfasst von
Xuyu Wang
Zhitao Yu
Shiwen Mao
Publikationsdatum
14.06.2019
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 2/2020
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-019-01302-x

Weitere Artikel der Ausgabe 2/2020

Mobile Networks and Applications 2/2020 Zur Ausgabe

OriginalPaper

The Signal Control Optimization of Road Intersections with Slow Traffic Based on Improved PSO

EditorialNotes

Editorial: Mobile Edge Computing and Personal Networks

OriginalPaper

Energy-Efficient Virtual Machine Scheduling across Cloudlets in Wireless Metropolitan Area Networks

EditorialNotes

Editorial: ACM/Springer Mobile Networks & Applications - Special Issue on Mobile Computing and Software Engineering

OriginalPaper

Annealed Glowworm Optimization Graph Theory - Virtual Network Mapping for Home Healthcare in Cloud

OriginalPaper

Fixing Target Access Point with low load during Handoff in WLAN

Neuer Inhalt

    Bildnachweise