research-article

Enhancing wifi-based localization with visual clues

Authors:

Longfei Shangguan,

Yunhao LiuAuthors Info & Claims

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 963 - 974

https://doi.org/10.1145/2750858.2807516

Published: 07 September 2015 Publication History

Abstract

Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.

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Cited By

Li DXu JYang ZTang C(2024)Train Once, Locate Anytime for Anyone: Adversarial Learning-based Wireless LocalizationACM Transactions on Sensor Networks10.1145/361409520:2(1-21)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3614095
Xu ZHuang BJia BMao G(2024)Enhancing WiFi Fingerprinting Localization Through a Co-Teaching Approach Using Crowdsourced Sequential RSS and IMU DataIEEE Internet of Things Journal10.1109/JIOT.2023.329752111:2(3550-3562)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3297521
Liu YYang ZLiu YYang Z(2024)Automatic Fingerprint Database UpdateLocation, Localization, and Localizability10.1007/978-981-97-3176-3_9(163-185)Online publication date: 12-Jul-2024
https://doi.org/10.1007/978-981-97-3176-3_9
Show More Cited By

Index Terms

Enhancing wifi-based localization with visual clues
1. Information systems
  1. Information retrieval
  2. Information storage systems

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Published In

cover image ACM Conferences

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2015

1302 pages

ISBN:9781450335744

DOI:10.1145/2750858

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

Copyright © 2015 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]

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Yahoo! Japan: Yahoo! Japan
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
FX Palo Alto Laboratory, Inc.: FX Palo Alto Laboratory, Inc.
Google Inc.
ACM: Association for Computing Machinery
Rakuten Institute of Technology: Rakuten Institute of Technology
Microsoft: Microsoft
Bell Labs: Bell Laboratories
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
KDDI
Panasonic: Panasonic Corporation
NTT DoCoMo
Telefónica: Telefónica
ISTC-PC: Intel Science and Technology Center for Pervasive Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2015

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Research-article

Funding Sources

Microsoft
National Natural Science Foundation of China
Research Grants Council, University Grants Committee, Hong Kong
Beijing Nova Program

Conference

UbiComp '15

Sponsor:

Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

Acceptance Rates

UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Li DXu JYang ZTang C(2024)Train Once, Locate Anytime for Anyone: Adversarial Learning-based Wireless LocalizationACM Transactions on Sensor Networks10.1145/361409520:2(1-21)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3614095
Xu ZHuang BJia BMao G(2024)Enhancing WiFi Fingerprinting Localization Through a Co-Teaching Approach Using Crowdsourced Sequential RSS and IMU DataIEEE Internet of Things Journal10.1109/JIOT.2023.329752111:2(3550-3562)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3297521
Liu YYang ZLiu YYang Z(2024)Automatic Fingerprint Database UpdateLocation, Localization, and Localizability10.1007/978-981-97-3176-3_9(163-185)Online publication date: 12-Jul-2024
https://doi.org/10.1007/978-981-97-3176-3_9
Liu YYang ZLiu YYang Z(2024)Robust Indoor LocalizationLocation, Localization, and Localizability10.1007/978-981-97-3176-3_8(131-162)Online publication date: 12-Jul-2024
https://doi.org/10.1007/978-981-97-3176-3_8
Li XLi HChan HLu HJensen C(2023)Data Imputation for Sparse Radio Maps in Indoor Positioning2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00173(2235-2248)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00173
Nishio NTsubouchi KSugasaki MShimosaka M(2023)Enabling Temporal Variation Resilience for ML-Based Indoor LocalizationMachine Learning for Indoor Localization and Navigation10.1007/978-3-031-26712-3_16(379-421)Online publication date: 19-Mar-2023
https://doi.org/10.1007/978-3-031-26712-3_16
Wang PGuo BWang ZYu Z(2022)ShopSense:Customer Localization in Multi-Person Scenario With Passive RFID TagsIEEE Transactions on Mobile Computing10.1109/TMC.2020.302983321:5(1812-1828)Online publication date: 1-May-2022
https://doi.org/10.1109/TMC.2020.3029833
Zhang GHou ZLi YVucetic B(2022)Deep Learning-Based Indoor Localization Using Adjacent Received Signal Strength and Domain Knowledge2022 20th Mediterranean Communication and Computer Networking Conference (MedComNet)10.1109/MedComNet55087.2022.9810465(25-30)Online publication date: 1-Jun-2022
https://doi.org/10.1109/MedComNet55087.2022.9810465
Li YBarthelemy JSun SPerez PMoran B(2022)Urban Vehicle Localization in Public LoRaWan NetworkIEEE Internet of Things Journal10.1109/JIOT.2021.31217789:12(10283-10294)Online publication date: 15-Jun-2022
https://doi.org/10.1109/JIOT.2021.3121778
Li NAi H(2022)EfiLoc: large-scale visual indoor localization with efficient correlation between sparse features and 3D pointsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02270-838:6(2091-2106)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s00371-021-02270-8
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