IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i11p3832-d177653.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/11/3832/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/11/3832/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mustapha Habib & Elmar Bollin & Qian Wang, 2023. "Battery Energy Management System Using Edge-Driven Fuzzy Logic," Energies, MDPI, vol. 16(8), pages 1-18, April.
    2. 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).
    3. 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.
    4. Mateusz Tomal, 2020. "Moving towards a Smarter Housing Market: The Example of Poland," Sustainability, MDPI, vol. 12(2), pages 1-25, January.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    2. Changbyung Yoon & Keeeun Lee & Byungun Yoon & Omar Toulan, 2017. "Typology and Success Factors of Collaboration for Sustainable Growth in the IT Service Industry," Sustainability, MDPI, vol. 9(11), pages 1-20, November.
    3. Youmeng Wu & Hao Sun & Hongliang Sun & Chi Xie, 2022. "Impact of Public Environmental Concerns on the Digital Transformation of Heavily Polluting Enterprises," IJERPH, MDPI, vol. 20(1), pages 1-19, December.
    4. Wenwen Zhu & Zhiqiang Wang, 2018. "The Collaborative Networks and Thematic Trends of Research on Purchasing and Supply Management for Environmental Sustainability: A Bibliometric Review," Sustainability, MDPI, vol. 10(5), pages 1-28, May.
    5. Stamatis Chrysikopoulos & Panos Chountalas, 2018. "Integrating energy and environmental management systems to enable facilities to qualify for carbon funds," Energy & Environment, , vol. 29(6), pages 938-956, September.
    6. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.
    7. Wei Song & Zhiya Chen & Xuping Wang & Qian Wang & Chenghua Shi & Wei Zhao, 2017. "Environmentally Friendly Supplier Selection Using Prospect Theory," Sustainability, MDPI, vol. 9(3), pages 1-17, March.
    8. Jiaxi Luo, 2022. "A Bibliometric Review on Artificial Intelligence for Smart Buildings," Sustainability, MDPI, vol. 14(16), pages 1-22, August.
    9. Kerstin Fritzsche & Silke Niehoff & Grischa Beier, 2018. "Industry 4.0 and Climate Change—Exploring the Science-Policy Gap," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
    10. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    12. Khaula Zeeshan & Timo Hämäläinen & Pekka Neittaanmäki, 2022. "Internet of Things for Sustainable Smart Education: An Overview," Sustainability, MDPI, vol. 14(7), pages 1-15, April.
    13. Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).
    14. Reka, S. Sofana & Dragicevic, Tomislav, 2018. "Future effectual role of energy delivery: A comprehensive review of Internet of Things and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 90-108.
    15. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    16. Fang, Shitong & Chen, Keyu & Lai, Zhihui & Zhou, Shengxi & Liao, Wei-Hsin, 2023. "Analysis and experiment of auxetic centrifugal softening impact energy harvesting from ultra-low-frequency rotational excitations," Applied Energy, Elsevier, vol. 331(C).
    17. Christian Tipantuña & Xavier Hesselbach, 2020. "NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms," Future Internet, MDPI, vol. 12(10), pages 1-31, October.
    18. Mohamed Haddouche & Adrian Ilinca, 2022. "Energy Efficiency and Industry 4.0 in Wood Industry: A Review and Comparison to Other Industries," Energies, MDPI, vol. 15(7), pages 1-25, March.
    19. Rafsanjani, Hamed Nabizadeh & Ghahramani, Ali & Nabizadeh, Amir Hossein, 2020. "iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings," Applied Energy, Elsevier, vol. 266(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3832-:d:177653. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.