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10.1145/3469830.3470914acmotherconferencesArticle/Chapter ViewAbstractPublication PagessstdConference Proceedingsconference-collections
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Tell Me What Air You Breath, I Tell You Where You Are

Published: 23 August 2021 Publication History

Abstract

Wide spread use of sensors and mobile devices along with the new paradigm of Mobile Crowd-Sensing (MCS), allows monitoring air pollution in urban areas. Several measurements are collected, such as Particulate Matters, Nitrogen dioxide, and others. Mining the context of MCS data in such domains is a key factor for identifying the individuals’ exposure to air pollution, but it is challenging due to the lack or the weakness of predictors. We have previously developed a multi-view learning approach which learns the context solely from the sensor measurements. In this demonstration, we propose a visualization tool (COMIC) showing the different recognized contexts using an improved version of our algorithm. We also demonstrate the change points detected by a multi-dimensional CPD model. We leverage real data from a MCS campaign, and compare different methods.

References

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Mohammad Abboud, Hafsa El Hafyani, Jingwei Zuo, Karine Zeitouni, and Yehia Taher. 2021. Micro-environment Recognition in the context of Environmental Crowdsensing. Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference 2841 (2021).
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Stamatelopoulou Asimina, D. Chapizanis, S. Karakitsios, P. Kontoroupis, D. Asimakopoulos, T. Maggos, and D. Sarigiannis. 2018. Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies. Environmental Monitoring and Assessment 190 (2018), 1–12.
[3]
Hafsa El Hafyani. 2020. Big Data Series Analytics in the Context of Environmental Crowd Sensing. In 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE Computer Society, Los Alamitos, CA, USA, 246–247. https://doi.org/10.1109/MDM48529.2020.00056
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Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: a review. Data Mining and Knowledge Discovery 33, 4 (2019), 917–963.
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Enrique Garcia-Ceja, Carlos E. Galván-Tejada, and Ramon Brena. 2018. Multi-view stacking for activity recognition with sound and accelerometer data. Information Fusion 40 (March 2018), 45–56. https://doi.org/10.1016/j.inffus.2017.06.004
[6]
Hafsa El Hafyani, Karine Zeitouni, Yehia Taher, and Mohammad Abboud. 2020. Leveraging Change Point Detection for Activity Transition Mining in the Context of Environmental Crowdsensing. Actes de la conférence BDA 2020 1 (2020), 64.
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Fazle Karim, Somshubra Majumdar, Houshang Darabi, and Samuel Harford. 2019. Multivariate LSTM-FCNs for time series classification. Neural Networks 116(2019), 237–245.
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Baptiste Languille, Valérie Gros, Nicolas Bonnaire, Clément Pommier, Cécile Honoré, Christophe Debert, Laurent Gauvin, Salim Srairi, Isabella Annesi-Maesano, Basile Chaix, 2020. A methodology for the characterization of portable sensors for air quality measure with the goal of deployment in citizen science. Science of the Total Environment 708 (2020), 134698.
[9]
Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, and Eli Woods. 2020. Tslearn, A Machine Learning Toolkit for Time Series Data. Journal of Machine Learning Research 21, 118 (2020), 1–6. http://jmlr.org/papers/v21/20-091.html

Cited By

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  • (2024)Utility-based dual pricing incentive mechanism for multi-stakeholder in mobile crowd sensingInternet of Things10.1016/j.iot.2024.101470(101470)Online publication date: Dec-2024
  • (2022)Learning the micro-environment from rich trajectories in the context of mobile crowd sensingGeoinformatica10.1007/s10707-022-00471-428:2(177-220)Online publication date: 20-Sep-2022
  • (2021)A Microservices Based Architecture for Implementing and Automating ETL Data Pipelines for Mobile Crowdsensing Applications2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671382(5909-5911)Online publication date: 15-Dec-2021

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        cover image ACM Other conferences
        SSTD '21: Proceedings of the 17th International Symposium on Spatial and Temporal Databases
        August 2021
        173 pages
        ISBN:9781450384254
        DOI:10.1145/3469830
        Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 23 August 2021

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        Author Tags

        1. Activity Recognition
        2. Air Quality Monitoring
        3. Mobile Crowd Sensing
        4. Multi-view Learning
        5. Multivariate Time Series Classification

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        Cited By

        View all
        • (2024)Utility-based dual pricing incentive mechanism for multi-stakeholder in mobile crowd sensingInternet of Things10.1016/j.iot.2024.101470(101470)Online publication date: Dec-2024
        • (2022)Learning the micro-environment from rich trajectories in the context of mobile crowd sensingGeoinformatica10.1007/s10707-022-00471-428:2(177-220)Online publication date: 20-Sep-2022
        • (2021)A Microservices Based Architecture for Implementing and Automating ETL Data Pipelines for Mobile Crowdsensing Applications2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671382(5909-5911)Online publication date: 15-Dec-2021

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