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Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

Author

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  • Ana Lavalle

    (Lucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig, 03690 Alicante, Spain
    Lucentia Lab, C/Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

  • Miguel A. Teruel

    (Lucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig, 03690 Alicante, Spain
    Lucentia Lab, C/Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

  • Alejandro Maté

    (Lucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig, 03690 Alicante, Spain
    Lucentia Lab, C/Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

  • Juan Trujillo

    (Lucentia Research, DLSI, University of Alicante, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig, 03690 Alicante, Spain
    Lucentia Lab, C/Pintor Pérez Gil, N-16, 03540 Alicante, Spain)

Abstract

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.

Suggested Citation

  • Ana Lavalle & Miguel A. Teruel & Alejandro Maté & Juan Trujillo, 2020. "Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5595-:d:383357
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    References listed on IDEAS

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    1. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    2. Tan Yigitcanlar & Md. Kamruzzaman, 2015. "Planning, Development and Management of Sustainable Cities: A Commentary from the Guest Editors," Sustainability, MDPI, vol. 7(11), pages 1-12, November.
    3. Chengming Li & Zhaoxin Dai & Xiaoli Liu & Wei Sun, 2020. "Evaluation System: Evaluation of Smart City Shareable Framework and Its Applications in China," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    4. Pilaiwan Phupattanasilp & Sheau-Ru Tong, 2019. "Augmented Reality in the Integrative Internet of Things (AR-IoT): Application for Precision Farming," Sustainability, MDPI, vol. 11(9), pages 1-17, May.
    5. Yung Chang Wu & Rui Sun & Yenchun Jim Wu, 2020. "Smart City Development in Taiwan: From the Perspective of the Information Security Policy," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
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    Cited by:

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    4. Carolina Narvaez Rojas & Gustavo Adolfo Alomia Peñafiel & Diego Fernando Loaiza Buitrago & Carlos Andrés Tavera Romero, 2021. "Society 5.0: A Japanese Concept for a Superintelligent Society," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
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    6. Mohammed Abdul-Rahman & Mayowa I. Adegoriola & Wilson Kodwo McWilson & Oluwole Soyinka & Yusuf A. Adenle, 2023. "Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    7. Ulfia Annette Lenfers & Nima Ahmady-Moghaddam & Daniel Glake & Florian Ocker & Jonathan Ströbele & Thomas Clemen, 2021. "Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg," Land, MDPI, vol. 10(11), pages 1-20, November.

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