| Literature DB >> 33401468 |
Juan M Corchado1,2,3, Pablo Chamoso1,2, Guillermo Hernández2, Agustín San Roman Gutierrez2, Alberto Rivas Camacho1, Alfonso González-Briones4, Francisco Pinto-Santos1, Enrique Goyenechea1, David Garcia-Retuerta1, María Alonso-Miguel1, Beatriz Bellido Hernandez1, Diego Valdeolmillos Villaverde1, Manuel Sanchez-Verdejo2, Pablo Plaza-Martínez1, Manuel López-Pérez1, Sergio Manzano-García1, Ricardo S Alonso2, Roberto Casado-Vara1, Javier Prieto Tejedor1,2, Fernando de la Prieta1, Sara Rodríguez-González1, Javier Parra-Domínguez1, Mohd Saberi Mohamad5, Saber Trabelsi6, Enrique Díaz-Plaza7, Jose Alberto Garcia-Coria8, Tan Yigitcanlar9, Paulo Novais10, Sigeru Omatu11.
Abstract
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 multi-functional 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.Entities:
Keywords: artificial intelligence; bike renting; data analysis; data visualization; edge computing; smart cities; smart cyberphysical platform
Year: 2021 PMID: 33401468 PMCID: PMC7795292 DOI: 10.3390/s21010236
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576