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Identifying Value in Crowdsourced Wireless Signal Measurements

Authors:

Haitao ZhengAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 607 - 616

https://doi.org/10.1145/3038912.3052563

Published: 03 April 2017 Publication History

Abstract

While crowdsourcing is an attractive approach to collect large-scale wireless measurements, understanding the quality and variance of the resulting data is difficult. Our work analyzes the quality of crowdsourced cellular signal measurements in the context of basestation localization, using large international public datasets (419M signal measurements and 1M cells) and corresponding ground truth values. Performing localization using raw received signal strength (RSS) data produces poor results and very high variance. Applying supervised learning improves results moderately, but variance remains high. Instead, we propose feature clustering, a novel application of unsupervised learning to detect hidden correlation between measurement instances, their features, and localization accuracy. Our results identify RSS standard deviation and RSS-weighted dispersion mean as key features that correlate with highly predictive measurement samples for both sparse and dense measurements respectively. Finally, we show how optimizing crowdsourcing measurements for these two features dramatically improves localization accuracy and reduces variance.

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Alimpertis EMarkopoulou AButts CBakopoulou EPsounis K(2024)A Unified Prediction Framework for Signal Maps: Not All Measurements are Created EqualIEEE Transactions on Mobile Computing10.1109/TMC.2022.322177323:1(70-89)Online publication date: Jan-2024
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Index Terms

Identifying Value in Crowdsourced Wireless Signal Measurements
1. Networks
  1. Network performance evaluation
    1. Network measurement

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Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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WWW '17

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WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Duval JHagemann SIkwunne TQuinonez DVigil-Hayes M(2024)Co-Designing Location-based Games for Broadband Data CollectionProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661502(2057-2072)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661502
Alimpertis EMarkopoulou AButts CBakopoulou EPsounis K(2024)A Unified Prediction Framework for Signal Maps: Not All Measurements are Created EqualIEEE Transactions on Mobile Computing10.1109/TMC.2022.322177323:1(70-89)Online publication date: Jan-2024
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Zhao YLiu CZhu KZhang SWu J(2023)GSMAC: GAN-Based Signal map Construction With Active CrowdsourcingIEEE Transactions on Mobile Computing10.1109/TMC.2021.312005722:4(2190-2204)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TMC.2021.3120057
He SShin K(2023)Urban Mobility-Driven Crowdsensing: Recent Advances in Machine Learning Designs and Ubiquitous ApplicationsMobile Crowdsourcing10.1007/978-3-031-32397-3_2(33-58)Online publication date: 21-Apr-2023
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Eller LRaida VSvoboda PRupp M(2022)Localizing Basestations From End-User Timing Advance MeasurementsIEEE Access10.1109/ACCESS.2022.314082510(5533-5544)Online publication date: 2022
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