research-article

Automatically characterizing places with opportunistic crowdsensing using smartphones

Authors:

Nicholas D. Lane,

Feng ZhaoAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 481 - 490

https://doi.org/10.1145/2370216.2370288

Published: 05 September 2012 Publication History

Abstract

Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present CrowdSense@Place (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place "hints" mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places into a variety of categories with an overall accuracy of 69%, outperforming currently available alternative solutions.

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Cited By

Tu CYu ZHan LGuo XHuang FGuo WWang L(2024)Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2023.334220623:7(7983-7998)Online publication date: Jul-2024
https://doi.org/10.1109/TMC.2023.3342206
Liu XZuo JWang JWang ZXu YLui J(2024)Learning Context-Aware Probabilistic Maximum Coverage Bandits: A Variance-Adaptive ApproachIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621257(2189-2198)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621257
Nagaraja G Chandan K J Amrutha S Dukandar Akash N Charitha Reddy (2023)FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing NetworksInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-9775(23-29)Online publication date: 5-May-2023
https://doi.org/10.48175/IJARSCT-9775
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Index Terms

Automatically characterizing places with opportunistic crowdsensing using smartphones
1. Applied computing
  1. Law, social and behavioral sciences
2. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Copyright © 2012 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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

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Published: 05 September 2012

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Ubicomp '12

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Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

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

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Cited By

Tu CYu ZHan LGuo XHuang FGuo WWang L(2024)Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse CrowdsensingIEEE Transactions on Mobile Computing10.1109/TMC.2023.334220623:7(7983-7998)Online publication date: Jul-2024
https://doi.org/10.1109/TMC.2023.3342206
Liu XZuo JWang JWang ZXu YLui J(2024)Learning Context-Aware Probabilistic Maximum Coverage Bandits: A Variance-Adaptive ApproachIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621257(2189-2198)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621257
Nagaraja G Chandan K J Amrutha S Dukandar Akash N Charitha Reddy (2023)FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing NetworksInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-9775(23-29)Online publication date: 5-May-2023
https://doi.org/10.48175/IJARSCT-9775
Rashid MWei NWang D(2023)A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machinesCollective Intelligence10.1177/263391372311708252:2Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1177/26339137231170825
Gao CMarsic ISarcevic AGestrich-Thompson WBurd R(2023)Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical SettingsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35807987:1(1-28)Online publication date: 28-Mar-2023
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Liu XZuo JXie HJoe-Wong CLui J(2023)Variance-Adaptive Algorithm for Probabilistic Maximum Coverage Bandits with General FeedbackIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228940(1-10)Online publication date: 17-May-2023
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Zhang ETrujillo RTempleton JPoellabauer C(2023)A Study on Mobile Crowd Sensing Systems for Healthcare ScenariosIEEE Access10.1109/ACCESS.2023.334215811(140325-140347)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3342158
Kumrai TKorpela JZhang YOhara KMurakami TAbeysekera HMaekawa T(2023)Automated construction of Wi-Fi-based indoor logical location predictor using crowd-sourced photos with Wi-Fi signalsPervasive and Mobile Computing10.1016/j.pmcj.2022.10174289:COnline publication date: 1-Feb-2023
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Sangeetha SKumari KShrinika MSujaybharath PVarsini SKumar K(2023)Ensuring Location Privacy in Crowdsensing System Using BlockchainFuturistic Communication and Network Technologies10.1007/978-981-19-9748-8_4(35-47)Online publication date: 23-Jun-2023
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Pintor LUras MColistra GAtzori L(2023)Monitoring People’s Mobility in the Cities: A Review of Advanced TechnologiesInformation and Communications Technologies for Smart Cities and Societies10.1007/978-3-031-39446-1_3(25-42)Online publication date: 10-Oct-2023
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