TY - JOUR
T1 - Deepint.net
T2 - A rapid deployment platform for smart territories
AU - Corchado, Juan M.
AU - Chamoso, Pablo
AU - Hernández, Guillermo
AU - Roman Gutierrez, Agustín San
AU - Camacho, Alberto Rivas
AU - González-Briones, Alfonso
AU - Pinto-Santos, Francisco
AU - Goyenechea, Enrique
AU - Garcia-Retuerta, David
AU - Alonso-Miguel, María
AU - Hernandez, Beatriz Bellido
AU - Villaverde, Diego Valdeolmillos
AU - Sanchez-Verdejo, Manuel
AU - Plaza-Martínez, Pablo
AU - López-Pérez, Manuel
AU - Manzano-García, Sergio
AU - Alonso, Ricardo S.
AU - Casado-Vara, Roberto
AU - Tejedor, Javier Prieto
AU - Prieta, Fernando de la
AU - Rodríguez-González, Sara
AU - Parra-Domínguez, Javier
AU - Mohamad, Mohd Saberi
AU - Trabelsi, Saber
AU - Díaz-Plaza, Enrique
AU - Garcia-Coria, Jose Alberto
AU - Yigitcanlar, Tan
AU - Novais, Paulo
AU - Omatu, Sigeru
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Bike renting
KW - Data analysis
KW - Data visualization
KW - Edge computing
KW - Smart cities
KW - Smart cyberphysical platform
UR - http://www.scopus.com/inward/record.url?scp=85098948613&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098948613&partnerID=8YFLogxK
U2 - 10.3390/s21010236
DO - 10.3390/s21010236
M3 - Article
C2 - 33401468
AN - SCOPUS:85098948613
SN - 1424-8220
VL - 21
SP - 1
EP - 22
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 1
M1 - 236
ER -