Jose M. Barcelo-Ordinas

About

I graduated in Telecommunication Engineering (equivalent to BSc and MSc) at Universitat Politècnica de Catalunya (UPC) and received a PhD degree in Telecommunication Engineering from UPC in 1998. I also graduated in Mathematics (equivalent to BsC and MsC) at UNED.

I am now Full Professor at the Computer Architecture Department at UPC. I joined the Statistical Analysis of Networks and Systems (SANS) Research Group at the Computer Architecture Departament in 1993. The SANS research group is a subgroup of the CNDS (Computer Network and Distributed Systems) research group.

My current area of interest is focused on how to guarantee data quality and accuracy in IoT sensor monitoring networks made of low cost monitoring equipment by means of machine learning and deep learning techniques and in digital twins.

However, in past years, I have researched various topics, including Broadband Networks (my PhD focused on ATM traffic control), TCP/IP protocol design, Internet Topology (BGP4 and Internet taxonomy), wireless networks, VANETs, sensor networks, and communication protocols.

Employment

• Universitat Politècnica de Catalunya Full Prof.
  1999 - Present

Education

• Universidad Nacional de Educación a Distancia Lic. Mathematics (equivalent to BsC and MsC)
  ? - 2015
• Universitat Politècnica de Catalunya PhD in Telecomunicacion Engineering
  ? - 1998
• Universitat Politècnica de Catalunya Telecommunication Engineer
  ? - 1991

Projects & Funding

Projects & funding information is unavailable.

Publications (94)

• W-UDDR: A Unified Framework for Automated Drift Detection in IoT-based Air Quality Monitoring Systems
  ACM Transactions on Internet of Things 2026 DOI: 10.1145/3803419
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• Evaluating missing value imputation strategies to enhance IoT data availability in the edge
  Computer Networks 2026 DOI: 10.1016/j.comnet.2026.112355
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• Regional aerosol hygroscopicity influences radiative forcing globally
  Shravan Deshmukh, Pau Ferrer-Cid, Baseerat Romshoo +19 more
  Communications Earth & Environment 2026 DOI: 10.1038/s43247-026-03505-z
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• Optimal sizing and placement of sensor networks with coordinate error variance constraint
  Measurement 2026 DOI: 10.1016/j.measurement.2025.120123
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• ESOD: An Edge Streaming Data Outlier Detection Framework for IoT Platforms
  IEEE Internet of Things Journal 2025 DOI: 10.1109/JIOT.2025.3579057
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• A review of graph-powered data quality applications for IoT monitoring sensor networks
  Journal of Network and Computer Applications 2025 DOI: 10.1016/j.jnca.2025.104116
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• Pattern-Based Attention Recurrent Autoencoder for Anomaly Detection in Air Quality Sensor Networks
  IEEE Transactions on Network Science and Engineering 2024 DOI: 10.1109/TNSE.2024.3454459
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• Virtual sensor-based proxy for black carbon estimation in IoT platforms
  Internet of Things 2024 DOI: 10.1016/j.iot.2024.101284
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• Convex Relaxation Method for Sensor Placement in Multiclass Monitoring Networks
  IEEE Transactions on Instrumentation and Measurement 2024 DOI: 10.1109/TIM.2024.3461788
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• Leveraging Spatiotemporal Correlations With Recurrent Autoencoders for Sensor Anomaly Detection
  IEEE Internet of Things Journal 2024 DOI: 10.1109/JIOT.2024.3416525
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• Robust Proxy Sensor Model for Estimating Black Carbon Concentrations Using Low-Cost Sensors
  2023 DOI: 10.1145/3597064.3597316
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• Temporal Pattern-Based Denoising and Calibration for Low-Cost Sensors in IoT Monitoring Platforms
  IEEE Transactions on Instrumentation and Measurement 2023 DOI: 10.1109/TIM.2023.3239626
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• Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms
  IEEE Internet of Things Journal 2022 DOI: 10.1109/JIOT.2022.3196154
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• Raw data collected from NO2, O3 and NO air pollution electrochemical low-cost sensors
  Data in Brief 2022 DOI: 10.1016/j.dib.2022.108586
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• A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields
  Sensors 2022 DOI: 10.3390/s22186840
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• Non-linear models for black carbon exposure modelling using air pollution datasets
  Environmental Research 2022 DOI: 10.1016/j.envres.2022.113269
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• Volterra Graph-Based Outlier Detection for Air Pollution Sensor Networks
  IEEE Transactions on Network Science and Engineering 2022 DOI: 10.1109/TNSE.2022.3169220
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• Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors
  Sensors 2022 DOI: 10.3390/s22103964
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• Data reconstruction applications for IoT air pollution sensor networks using graph signal processing
  Journal of Network and Computer Applications 2022 DOI: 10.1016/J.JNCA.2022.103434
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• Graph Signal Reconstruction Techniques for IoT Air Pollution Monitoring Platforms
  ArXiv 2022
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