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HAL | Université de Versailles Saint-Quentin-en-Yvelines |
Conference papers
Tell Me What Air You Breath, I Tell You Where You Are
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.
https://hal.uvsq.fr/hal-03336989
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Submitted on : Thursday, September 23, 2021 - 3:33:18 PM
Last modification on : Wednesday, October 20, 2021 - 12:24:16 AM
Long-term archiving on: : Friday, December 24, 2021 - 6:07:22 PM
Contributor : Équipe Hal Uvsq Connect in order to contact the contributor
Submitted on : Thursday, September 23, 2021 - 3:33:18 PM
Last modification on : Wednesday, October 20, 2021 - 12:24:16 AM
Long-term archiving on: : Friday, December 24, 2021 - 6:07:22 PM
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sstd21_v2.pdf
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Distributed under a Creative Commons Attribution 4.0 International License