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Automating the Implementation of Unsupervised Machine Learning Processes in Smart Cities Scenarios

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Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference (DCAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 585))

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Abstract

Climate Change has become a problem for all the inhabitants of the planet and the solutions to curb it involve knowing all the data on its causes and effects. To this end, it is essential to have mechanisms capable of reading data from different media in real time. This will make it possible to solve many of the problems that arise in areas such as medicine, Smart Cities, industry, transport, etc. Analysing raw data to provide it with semantics is essential to exploit its full potential, making it possible to manage a large number of everyday tasks. All this raw data often comes from a large number of sensors and other sources, in very different types and formats. The analysis of this data read in real time and cross-referenced with information stored in heterogeneous databases, with data from simulations or with data from digital twins is a great opportunity to combat problems such as Climate Change. This work presents a successful use case by characterising the city of Salamanca in vegetation clusters, where a decarbonisation process of a communication artery that crosses the city from north to south is being carried out. The results of this study will serve to identify the most necessary areas for action in the fight against the polluting gases that cause Climate Change.

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Acknowledgements

This research has been partially supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Blockchain -based framework for IoT Security”, Reference: RTI2018-095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER).

Author information

Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, Calle Espejo 2, 37007, Salamanca, Spain

    Raúl López-Blanco, Ricardo S. Alonso & Javier Prieto

  2. AIR Institute - Deep tech lab IoT Digital Innovation Hub, Salamanca, Spain

    Ricardo S. Alonso

  3. Texas A&M University at Qatar, Doha, Qatar

    Saber Trabelsi

Authors
  1. Raúl López-Blanco
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  2. Ricardo S. Alonso
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  3. Javier Prieto
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  4. Saber Trabelsi
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Corresponding author

Correspondence to Raúl López-Blanco .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    José Manuel Machado

  2. University of Salamanca, Salamanca, Spain

    Pablo Chamoso

  3. University of Salamanca, Salamanca, Spain

    Guillermo Hernández

  4. Koszalin University of Technology, Koszalin, Poland

    Grzegorz Bocewicz

  5. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Roussanka Loukanova

  6. University of Coruña, La Coruña, Spain

    Esteban Jove

  7. Departamento de Matemática Aplicada, University of Salamanca, Salamanca, Spain

    Angel Martin del Rey

  8. University of Calabria, Arcavacata di Rende, Cosenza, Italy

    Michela Ricca

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López-Blanco, R., Alonso, R.S., Prieto, J., Trabelsi, S. (2023). Automating the Implementation of Unsupervised Machine Learning Processes in Smart Cities Scenarios. In: Machado, J.M., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-031-23210-7_7

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