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FALE: Fine-grained Device Free Localization that can Adaptively work in Different Areas with Little Effort

Authors:

Ju Wang,

Zhanyong TangAuthors Info & Claims

SIGCOMM '15: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication

Pages 601 - 602

https://doi.org/10.1145/2785956.2790020

Published: 17 August 2015 Publication History

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Abstract

Many emerging applications and the ubiquitous wireless signals have accelerated the development of Device Free localization (DFL) techniques, which can localize objects without the need to carry any wireless devices. Most traditional DFL methods have a main drawback that as the pre-obtained Received Signal Strength (RSS) measurements (i.e., fingerprint) in one area cannot be directly applied to the new area for localization, and the calibration process of each area will result in the human effort exhausting problem. In this paper, we propose FALE, a fine-grained transferring DFL method that can adaptively work in different areas with little human effort and low energy consumption. FALE employs a rigorously designed transferring function to transfer the fingerprint into a projected space, and reuse it across different areas, thus greatly reduce the human effort. On the other hand, FALE can reduce the data volume and energy consumption by taking advantage of the compressive sensing (CS) theory. Extensive real-word experimental results also illustrate the effectiveness of FALE.

References

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Cited By

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Chomu KAtanasovski VGavrilovska L(2017)Finding Near-Optimal Regularization Parameter for Indoor Device-free LocalizationWireless Personal Communications: An International Journal10.1007/s11277-016-3846-z92:1(197-220)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s11277-016-3846-z
Yang SGuo YLi NLu A(2020)PGMP: A Device-Free Moving Object Counting and Localization Approach in the Varying EnvironmentIEEE Wireless Communications Letters10.1109/LWC.2020.29886939:8(1287-1290)Online publication date: Aug-2020
https://doi.org/10.1109/LWC.2020.2988693
Yang SGuo YLi NJiang X(2020)A Potential Orthogonal Matching Pursuit Algorithm for Device Free Moving Target LocalizationIEEE Communications Letters10.1109/LCOMM.2020.299275724:8(1691-1694)Online publication date: Aug-2020
https://doi.org/10.1109/LCOMM.2020.2992757
Show More Cited By

Index Terms

FALE: Fine-grained Device Free Localization that can Adaptively work in Different Areas with Little Effort
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing design and evaluation methods

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FALE: Fine-grained Device Free Localization that can Adaptively work in Different Areas with Little Effort
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Information & Contributors

Information

Published In

SIGCOMM '15: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication

August 2015

684 pages

ISBN:9781450335423

DOI:10.1145/2785956

General Chairs:
Steve Uhlig
Queen Mary University of London, UK
,
Olaf Maennel
Tallinn U. of Technology in Estonia, Estonia
,
Program Chairs:
Brad Karp
University College London, UK
,
Jitendra Padhye
Microsoft, USA

ACM SIGCOMM Computer Communication Review Volume 45, Issue 4
SIGCOMM'15
October 2015
659 pages
ISSN:0146-4833
DOI:10.1145/2829988
Editors:
Konstantina Papagiannaki
Telefonica Research, Barcelona, Spain
,
Katerina Argyraki
EPFL, Switzerland
,
Hitesh Ballani
Microsoft Research Cambridge, UK
,
Fabián Bustamante
Northwestern University, USA
,
Joseph Camp
SMU, USA
,
Augustin Chaintreau
Columbia University, USA
,
Phillipa Gill
Stony Brook University, USA
,
Marco Mellia
Politecnico di Torino, Italy
,
Bhaskaran Raman
IIT Bombay, India
,
Joel Sommers
Colgate University, USA
,
Aline Carneiro Viana
INRIA, France
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2015

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Poster

Funding Sources

Northweste University graduate student innovation training project
Project National Key Technology R&D Program,
Project NSFC,

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SIGCOMM '15

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SIGCOMM

SIGCOMM '15: ACM SIGCOMM 2015 Conference

August 17 - 21, 2015

London, United Kingdom

Acceptance Rates

SIGCOMM '15 Paper Acceptance Rate 40 of 242 submissions, 17%;

Overall Acceptance Rate 462 of 3,389 submissions, 14%

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Citations

Cited By

View all

Chomu KAtanasovski VGavrilovska L(2017)Finding Near-Optimal Regularization Parameter for Indoor Device-free LocalizationWireless Personal Communications: An International Journal10.1007/s11277-016-3846-z92:1(197-220)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1007/s11277-016-3846-z
Yang SGuo YLi NLu A(2020)PGMP: A Device-Free Moving Object Counting and Localization Approach in the Varying EnvironmentIEEE Wireless Communications Letters10.1109/LWC.2020.29886939:8(1287-1290)Online publication date: Aug-2020
https://doi.org/10.1109/LWC.2020.2988693
Yang SGuo YLi NJiang X(2020)A Potential Orthogonal Matching Pursuit Algorithm for Device Free Moving Target LocalizationIEEE Communications Letters10.1109/LCOMM.2020.299275724:8(1691-1694)Online publication date: Aug-2020
https://doi.org/10.1109/LCOMM.2020.2992757
Guo YYang SLi NJiang X(2020)Device-Free Localization Scheme With Time-Varying Gestures Using Block Compressive SensingIEEE Access10.1109/ACCESS.2020.29935768(88951-88960)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2993576
Yang SGuo YLi NQian P(2019)Compressive Sensing Based Device-Free Multi-Target Localization Using Quantized MeasurementIEEE Access10.1109/ACCESS.2019.29204827(73172-73181)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2920482
Wang JXiong JJiang HChen XFang D(2017)D-WatchIEEE/ACM Transactions on Networking10.1109/TNET.2017.274758325:6(3559-3572)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1109/TNET.2017.2747583
Wang JFang DYang ZJiang HChen XXing TCai L(2017)E-HIPAIEEE Transactions on Mobile Computing10.1109/TMC.2016.256739616:3(716-729)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TMC.2016.2567396
Wang JXiong JJiang HChen XFang DMarkopoulou AFaloutsos MSekar VKostic D(2016)D-WatchProceedings of the 12th International on Conference on emerging Networking EXperiments and Technologies10.1145/2999572.2999589(253-266)Online publication date: 6-Dec-2016
https://dl.acm.org/doi/10.1145/2999572.2999589
Yu DGuo YLi NFang D(2016)Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM AlgorithmIEEE Access10.1109/ACCESS.2017.26495404(9743-9757)Online publication date: 2016
https://doi.org/10.1109/ACCESS.2017.2649540

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Recommendations

FALE: Fine-grained Device Free Localization that can Adaptively work in Different Areas with Little Effort

TafLoc: Time-adaptive and Fine-grained Device-free Localization with Little Cost

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