Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices
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Título: | Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices |
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Autor/es: | Lavalle, Ana | Teruel, Miguel A. | Maté, Alejandro | Trujillo, Juan |
Grupo/s de investigación o GITE: | Lucentia |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Internet of things | Data visualization | Big data analytics | Smart city | Methodology | Artificial intelligence | Dashboards |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 11-jul-2020 |
Editor: | MDPI |
Cita bibliográfica: | Lavalle A, Teruel MA, Maté A, Trujillo J. Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices. Sustainability. 2020; 12(14):5595. https://doi.org/10.3390/su12145595 |
Resumen: | Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization. |
Patrocinador/es: | This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities and the DQIoT (INNO-20171060) project funded by the Spanish Center for Industrial Technological Development, approved with an EUREKA quality seal (E!11737DQIOT). Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company. |
URI: | http://hdl.handle.net/10045/108471 |
ISSN: | 2071-1050 |
DOI: | 10.3390/su12145595 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/su12145595 |
Aparece en las colecciones: | INV - LUCENTIA - Artículos de Revistas |
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