IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i12p3430-d188766.html
   My bibliography  Save this article

Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings

Author

Listed:
  • Roberto Casado-Vara

    (BISITE Digital Innovation Hub, University of Salamanca. Edificio Multiusos I+D+i, 37007 Salamanca, Spain)

  • Zita Vale

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and DevelopmentInstitute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal)

  • Javier Prieto

    (BISITE Digital Innovation Hub, University of Salamanca. Edificio Multiusos I+D+i, 37007 Salamanca, Spain)

  • Juan M. Corchado

    (BISITE Digital Innovation Hub, University of Salamanca. Edificio Multiusos I+D+i, 37007 Salamanca, Spain)

Abstract

The monitoring of the Internet of things networks depends to a great extent on the availability and correct functioning of all the network nodes that collect data. This network nodes all of which must correctly satisfy their purpose to ensure the efficiency and high quality of monitoring and control of the internet of things networks. This paper focuses on the problem of fault-tolerant maintenance of a networked environment in the domain of the internet of things. Based on continuous-time Markov chains, together with a cooperative control algorithm, a novel feedback model-based predictive hybrid control algorithm is proposed to improve the maintenance and reliability of the internet of things network. Virtual sensors are substituted for the sensors that the algorithm predicts will not function properly in future time intervals; this allows for maintaining reliable monitoring and control of the internet of things network. In this way, the internet of things network improves its robustness since our fault tolerant control algorithm finds the malfunction nodes that are collecting incorrect data and self-correct this issue replacing malfunctioning sensors with new ones. In addition, the proposed model is capable of optimising sensor positioning. As a result, data collection from the environment can be kept stable. The developed continuous-time control model is applied to guarantee reliable monitoring and control of temperature in a smart supermarket. Finally, the efficiency of the presented approach is verified with the results obtained in the conducted case study.

Suggested Citation

  • Roberto Casado-Vara & Zita Vale & Javier Prieto & Juan M. Corchado, 2018. "Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings," Energies, MDPI, vol. 11(12), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3430-:d:188766
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/12/3430/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/12/3430/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammad Ali Fotouhi Ghazvini & João Soares & Hugo Morais & Rui Castro & Zita Vale, 2017. "Dynamic Pricing for Demand Response Considering Market Price Uncertainty," Energies, MDPI, vol. 10(9), pages 1-20, August.
    2. Silva, Francisco & Teixeira, Brígida & Pinto, Tiago & Santos, Gabriel & Vale, Zita & Praça, Isabel, 2016. "Generation of realistic scenarios for multi-agent simulation of electricity markets," Energy, Elsevier, vol. 116(P1), pages 128-139.
    3. Pedro Faria & João Spínola & Zita Vale, 2018. "Reschedule of Distributed Energy Resources by an Aggregator for Market Participation," Energies, MDPI, vol. 11(4), pages 1-15, March.
    4. Gabriel Santos & Tiago Pinto & Isabel Praça & Zita Vale, 2016. "An Interoperable Approach for Energy Systems Simulation: Electricity Market Participation Ontologies," Energies, MDPI, vol. 9(11), pages 1-22, October.
    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. Libor Dražan & René Križan & Miroslav Popela, 2021. "Design and Testing of a Low-Tech DEW Generator for Determining Electromagnetic Immunity of Standard Electronic Circuits," Energies, MDPI, vol. 14(11), pages 1-15, May.
    2. Roberto Casado-Vara & Angel Martín del Rey & Ricardo S. Alonso & Saber Trabelsi & Juan M. Corchado, 2020. "A New Stability Criterion for IoT Systems in Smart Buildings: Temperature Case Study," Mathematics, MDPI, vol. 8(9), pages 1-13, August.
    3. Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
    4. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    5. Khalid Haseeb & Naveed Islam & Yasir Javed & Usman Tariq, 2020. "A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network," Energies, MDPI, vol. 14(1), pages 1-14, December.
    6. Moudgil, Vipul & Hewage, Kasun & Hussain, Syed Asad & Sadiq, Rehan, 2023. "Integration of IoT in building energy infrastructure: A critical review on challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    7. Pedro Faria & Zita Vale, 2019. "Distributed Energy Resources Management 2018," Energies, MDPI, vol. 13(1), pages 1-4, December.

    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. Savelli, Iacopo & Morstyn, Thomas, 2021. "Electricity prices and tariffs to keep everyone happy: A framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Omega, Elsevier, vol. 103(C).
    2. Ferrara, Massimiliano & Violi, Antonio & Beraldi, Patrizia & Carrozzino, Gianluca & Ciano, Tiziana, 2021. "An integrated decision approach for energy procurement and tariff definition for prosumers aggregations," Energy Economics, Elsevier, vol. 97(C).
    3. Shengli Du & Mingchao Li & Shuai Han & Jonathan Shi & Heng Li, 2019. "Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data," Energies, MDPI, vol. 12(6), pages 1-20, March.
    4. Tahir, Muhammad Faizan & Chen, Haoyong & Khan, Asad & Javed, Muhammad Sufyan & Cheema, Khalid Mehmood & Laraik, Noman Ali, 2020. "Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements," Technology in Society, Elsevier, vol. 63(C).
    5. Tiago Pinto & Mohammad Ali Fotouhi Ghazvini & Joao Soares & Ricardo Faia & Juan Manuel Corchado & Rui Castro & Zita Vale, 2018. "Decision Support for Negotiations among Microgrids Using a Multiagent Architecture," Energies, MDPI, vol. 11(10), pages 1-20, September.
    6. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    7. Weiss, Olga & Bogdanov, Dmitry & Salovaara, Kaisa & Honkapuro, Samuli, 2017. "Market designs for a 100% renewable energy system: Case isolated power system of Israel," Energy, Elsevier, vol. 119(C), pages 266-277.
    8. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
    9. Giuseppe De Marco & Vincenzo Loia & Hadis Karimipour & Pierluigi Siano, 2021. "Assessing Insider Attacks and Privacy Leakage in Managed IoT Systems for Residential Prosumers," Energies, MDPI, vol. 14(9), pages 1-15, April.
    10. Iacopo Savelli & Thomas Morstyn, 2020. "Electricity prices and tariffs to keep everyone happy: a framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery," Papers 2001.04283, arXiv.org, revised Jun 2021.
    11. Zheng, Kedi & Chen, Huiyao & Wang, Yi & Chen, Qixin, 2022. "Data-driven financial transmission right scenario generation and speculation," Energy, Elsevier, vol. 238(PC).
    12. Nikos Kampelis & Elisavet Tsekeri & Dionysia Kolokotsa & Kostas Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2018. "Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions," Energies, MDPI, vol. 11(11), pages 1-22, November.
    13. Piao, Longjian & de Vries, Laurens & de Weerdt, Mathijs & Yorke-Smith, Neil, 2021. "Electricity markets for DC distribution systems: Locational pricing trumps wholesale pricing," Energy, Elsevier, vol. 214(C).
    14. Pedro Faria, 2019. "Distributed Energy Resources Management," Energies, MDPI, vol. 12(3), pages 1-3, February.
    15. Alrobaian, Abdulrahman A. & Alsagri, Ali Sulaiman, 2023. "Multi-agent-based energy management for a fully electrified residential consumption," Energy, Elsevier, vol. 282(C).
    16. Mahmood Hosseini Imani & Shaghayegh Zalzar & Amir Mosavi & Shahaboddin Shamshirband, 2018. "Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs," Energies, MDPI, vol. 11(6), pages 1-24, June.
    17. Feihu Hu & Xuan Feng & Hui Cao, 2018. "A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing," Energies, MDPI, vol. 11(12), pages 1-18, November.
    18. Gabriel Santos & Pedro Faria & Zita Vale & Tiago Pinto & Juan M. Corchado, 2020. "Constrained Generation Bids in Local Electricity Markets: A Semantic Approach," Energies, MDPI, vol. 13(15), pages 1-27, August.
    19. Kamalanathan Ganesan & João Tomé Saraiva & Ricardo J. Bessa, 2019. "On the Use of Causality Inference in Designing Tariffs to Implement More Effective Behavioral Demand Response Programs," Energies, MDPI, vol. 12(14), pages 1-20, July.
    20. Malkawi, Salaheddin & Al-Nimr, Moh'd & Azizi, Danah, 2017. "A multi-criteria optimization analysis for Jordan's energy mix," Energy, Elsevier, vol. 127(C), pages 680-696.

    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:jeners:v:11:y:2018:i:12:p:3430-:d:188766. 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.