Conjunts de dades de producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"

  • Dades identificatives

    Identificador: PC:4128
  • Autors:

    Gonzalez, Ernesto
  • Altres:

    Matèria: Enginyeria
    Drets d'accés: info:eu-repo/semantics/openAccess
    Identificador del investigador: 0000-0002-2205-4857
    Publicat per (editora): Universitat Rovira i Virgili (URV)
    Publicacions relacionades: González, E., Casanova-Chafer, J., Romero, A., Vilanova, X., Mitrovics, J., & Llobet, E. (2020). Lora sensor network development for air quality monitoring or detecting gas leakage events. Sensors, 20(21), 6225. https://doi.org/10.3390/s20216225
    Resum: This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.
    Departament: Enginyeria Electrònica, Elèctrica i Automàtica
    DOI: 10.5281/zenodo.5946849
    Tipus de document: info:eu-repo/semantics/other
    DOI de la publicació relacionada: 10.3390/s20216225
    Data alta repositori: 2022-02-02
    Autor: Gonzalez, Ernesto
    Paraules clau: AQM, IoT, LoRa, WSN, graphene
    Grup de recerca: Metabolomics Interdisciplary Laboratory
    Any de publicació de la dataset: 2022
    Acció del programa de finançament: CP received funding from the National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. MMcC is a Wellcome Senior Investigator and an NIHR Senior Investigator. MMcC received funding from Wellcome (090532, 106130, 098381, 203141, 212259), NIDDK (U01-DK105535), and NIHR (NF-SI-0617-10090). TGMV was supported by ZonMW (TOP 40–00812–98–11010). DLC was supported by the American Diabetes Association Grant 1-17-PDF-077. SFAG is supported by the Daniel B. Burke Chair for Diabetes Research and NIH Grant R01 HD058886. NVT is funded by a pre-doctoral grant from the Agència de Gestió d’Ajuts Universitaris i de Recerca (2017 FI_B 00636), Generalitat de Catalunya – Fons Social Europeu. BK received personal funding from the European Research Council Advanced Grant META-GROWTH (ERC-2012-AdG – no. 322605). BF was supported by an Oak Foundation Fellowship. RMF and RNB are supported by Sir Henry Dale Fellowship (Wellcome Trust and Royal Society grant: WT104150). ATH is supported by a Wellcome Trust Senior Investigator award (grant number 098395/Z/12/Z). DM is supported by a Canada Research Chair. DLC was supported by the American Diabetes Association Grant 1-17-PDF-077. JTL was supported by the Finnish Cultural Foundation. DIB received a KNAW Academy Professor Award (PAH/6635). MH received PhD scholarship funding from TARGET (http://target.ku.dk), The Danish Diabetes Academy (http://danishdiabetesacademy.dk) and the Copenhagen Graduate School of Health and Medical Sciences. VWVJ received funding from the Netherlands Organization for Health Research and Development (VIDI 016.136.361) and the European Research Council (ERC-2014-CoG-648916). ISF was supported by the European Research Council, Wellcome Trust (098497/Z/12/Z), Medical Research Council (MRC_MC_UU_12012/5), the NIHR Cambridge Biomedical Research Centre, the Botnar Foundation, the Bernard Wolfe Health Neuroscience Endowment and the European Community’s Seventh Framework Programme (FP7/2007-2013) project Beta-JUDO n°279153. IB acknowledges funding from Wellcome (WT206194). ES works in a unit that receives funding from the University of Bristol and the UK Medical Research Council (MC_UU_00011/1, MC_UU_00011/3). GDS works in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_00011/1). KC received funds from the French National Agency of Research, F-CRIN/FORCE.
    Títol del conjunt de dades: Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
  • Paraules clau:

    Enginyeria
    AQM, IoT, LoRa, WSN, graphene
  • Documents:

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