2026-06-02T06:37:13Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72362023-03-17T11:23:39Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Corchado, Juan M. Chamoso, Pablo Hernández González, Guillermo San Román Gutiérrez, Agustín Rivas Camacho, Alberto González Briones, Alfonso Pinto Santos, Francisco Goyenechea, Enrique García Retuerta, David Alonso Miguel, María Bellido Hernández, Beatriz Valdeolmillos Villaverde, Diego Sánchez Verdejo, Manuel Plaza Martínez, Pablo López Pérez, Manuel Manzano García, Sergio Alonso Rincón, Ricardo S. Casado Vara, Roberto Carlos Prieto Tejedor, Javier Prieta, Fernando de la Rodríguez González, Sara Parra Domínguez, Javier Saberi Mohamad, Mohd Trabelsi, Saber Díaz Plaza, Enrique García Coria, José Alberto Yigitcanlar, Tan Novais, Paulo Omatu, Sigeru Smart cities Smart cyberphysical platform Data analysis Data visualization Edge computing Artificial intelligence Bike renting This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multifunctional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques. 2023-01-13 2023-01-13 2021-01 info:eu-repo/semantics/article http://hdl.handle.net/10259/7236 10.3390/s21010236 1424-8220 eng Sensors. 2021, V. 21, n. 1, e236 https://doi.org/10.3390/s21010236 info:eu-repo/grantAgreement/EC/H2020/777630/EU/Multi-scale Observation and Monitoring of railway Infrastructure Threats/ info:eu-repo/grantAgreement/EC/POCTEP/0677_DISRUPTIVE_2_E/EU/Intensifying the activity of Digital Innovation Hubs within the PocTep region to boost the development of disruptive and last generation ICTs through cross-border cooperation/ info:eu-repo/grantAgreement/CYTED//518RT0558//CIUDADES INTELIGENTES TOTALMENTE INTEGRALES, EFICIENTES Y SOSTENIBLES (CITIES)/ http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI