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Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services

Author

Listed:
  • Francisco-Javier Ferrández-Pastor

    (Department of Computer Technology and Computation, University of Alicante, 03690 Alicante, Spain
    These authors contributed equally to this work.)

  • Higinio Mora

    (Department of Computer Technology and Computation, University of Alicante, 03690 Alicante, Spain
    These authors contributed equally to this work.)

  • Antonio Jimeno-Morenilla

    (Department of Computer Technology and Computation, University of Alicante, 03690 Alicante, Spain
    These authors contributed equally to this work.)

  • Bruno Volckaert

    (Internet Technology and Data Science Lab (IDLAB), Ghent University, 9000 Gent imec, Belgium
    These authors contributed equally to this work.)

Abstract

Advances in embedded systems, based on System-on-a-Chip (SoC) architectures, have enabled the development of many commercial devices that are powerful enough to run operating systems and complex algorithms. These devices integrate a set of different sensors with connectivity, computing capacities and cost reduction. In this context, the Internet of Things (IoT) potential increases and introduces other development possibilities: “Things” can now increase computation near the source of the data; consequently, different IoT services can be deployed on local systems. This paradigm is known as “edge computing” and it integrates IoT technologies and cloud computing systems. Edge computing reduces the communications’ bandwidth needed between sensors and the central data centre. Management of sensors, actuators, embedded devices and other resources that may not be continuously connected to a network (such as smartphones) are required for this method. This trend is very attractive for smart building designs, where different subsystems (energy, climate control, security, comfort, user services, maintenance, and operating costs) must be integrated to develop intelligent facilities. In this work, a method to design smart services based on the edge computing paradigm is analysed and proposed. This novel approach overcomes some drawbacks of existing designs related to interoperability and scalability of services. An experimental architecture based on embedded devices is described. Energy management, security system, climate control and information services are the subsystems on which new smart facilities are implemented.

Suggested Citation

  • Francisco-Javier Ferrández-Pastor & Higinio Mora & Antonio Jimeno-Morenilla & Bruno Volckaert, 2018. "Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services," Sustainability, MDPI, vol. 10(11), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3832-:d:177653
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    References listed on IDEAS

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    1. Junhu Ruan & Felix T. S. Chan & Fangwei Zhu & Xuping Wang & Jing Yang, 2016. "A Visualization Review of Cloud Computing Algorithms in the Last Decade," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    2. Gilseung Ahn & You-Jin Park & Sun Hur, 2017. "Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing," Sustainability, MDPI, vol. 9(2), pages 1-17, February.
    3. Wei, Min & Hong, Seung Ho & Alam, Musharraf, 2016. "An IoT-based energy-management platform for industrial facilities," Applied Energy, Elsevier, vol. 164(C), pages 607-619.
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    Cited by:

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    2. Mateusz Tomal, 2020. "Moving towards a Smarter Housing Market: The Example of Poland," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    3. Schaefer, Jones Luís & Mairesse Siluk, Julio Cezar & Stefan de Carvalho, Patrícia & Maria de Miranda Mota, Caroline & Pinheiro, José Renes & Nuno da Silva Faria, Pedro & Gouvea da Costa, Sergio Eduard, 2023. "A framework for diagnosis and management of development and implementation of cloud-based energy communities - Energy cloud communities," Energy, Elsevier, vol. 276(C).
    4. Jones Luís Schaefer & Julio Cezar Mairesse Siluk & Patrícia Stefan de Carvalho & José Renes Pinheiro & Paulo Smith Schneider, 2020. "Management Challenges and Opportunities for Energy Cloud Development and Diffusion," Energies, MDPI, vol. 13(16), pages 1-27, August.
    5. Qi Zhang & Hongyang Li & Xin Wan & Martin Skitmore & Hailin Sun, 2020. "An Intelligent Waste Removal System for Smarter Communities," Sustainability, MDPI, vol. 12(17), pages 1-27, August.

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