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Learning the micro-environment from rich trajectories in the context of mobile crowd sensing

Application to air quality monitoring

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Abstract 

With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant’s exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human’s micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the exact geolocation (which is privacy invasive), or simply applying some a priori rules (which is privacy friendly). The experimental results, applied to real MCS data, not only confirm the power of MCS and air quality (AQ) data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, and during the training phase, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific hardware. However, during the application of models on new data, the deep learning algorithm fails to outperform our proposed models.

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Notes

  1. http://polluscope.uvsq.fr

  2. This value has been chosen in accordance with the resolution adopted by Airparif (the agency in charge of AQ monitoring in the Paris Region, also part of the Polluscope consortium) in their simulation models.

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Acknowledgements

This work has supported by the French National Research Agency (ANR) project Polluscope, funded under the grant agreement ANR-15-CE22-0018, by the H2020 EU GO GREEN ROUTES funded under the research and innovation programme H2020- EU.3.5.2 grant agreement No 869764, and by the DATAIA convergence institute project StreamOps, as part of the Programme d’ Investissement d’Avenir, ANR-17-CONV-0003. Part of the equipment was funded by iDEX Paris-Saclay, in the framework of the IRS project ACE-ICSEN, and by the Communauté d’agglomération Versailles Grand Parc - VGP - (www.versaillesgrandparc.fr). We are thankful to VGP (Thomas Bonhoure) for facilitating the campaign. We would like to thank all the members of the Polluscope consortia who contributed in one way or another to this work: Salim Srairi and Jean-Marc Naude (CEREMA) who conducted the campaign; Boris Dessimond and Isabella Annesi-Maesano (Sorbonne University) for their contribution to the campaign; Valerie Gros and Nicolas Bonnaire (LSCE), and Anne Kauffman and Christophe Debert (Airparif) for their contribution in the periodic qualification of the sensors and their active involvement in the project. Finally, we would like to thank the participants for their great effort in carrying the sensors, without whom this work would not be possible.

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Authors and Affiliations

  1. DAVID Lab, UVSQ-Université Paris-Saclay, Versailles, France

    Hafsa El Hafyani, Mohammad Abboud, Jingwei Zuo, Karine Zeitouni & Yehia Taher

  2. emesis Research Team, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique, Paris, France

    Basile Chaix & Limin Wang

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  1. Hafsa El Hafyani
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El Hafyani, H., Abboud, M., Zuo, J. et al. Learning the micro-environment from rich trajectories in the context of mobile crowd sensing. Geoinformatica 28, 177–220 (2024). https://doi.org/10.1007/s10707-022-00471-4

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