IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v349y2023ics0306261923009030.html
   My bibliography  Save this article

An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings

Author

Listed:
  • Li, Wenzhuo
  • Tang, Rui
  • Wang, Shengwei
  • Zheng, Zhuang

Abstract

In smart buildings enabled by IoT technologies, wireless sensor networks (WSNs) are promising platforms to implement novel fully distributed optimal control approaches according to the edge computing paradigm. This requires knowledge from wireless communication and distributed computation fields where communication topologies are both critical. Communication topologies are designed considering network energy consumption and stability in wireless communication field, while considering optimization convergence speed in distributed computation field. But there is no inter-disciplinary design method considering these issues simultaneously. This study therefore proposes an optimal design method for communication topology of WSNs to implement fully distributed optimal control approaches. System control performance, network energy consumption and network stability are integrated into the objective function for the design. For a WSN consisting of n sensors, an integer programming problem with n(n − 1)/2 design variables, i.e., elements in Laplacian matrix representing the existence of communication links, is formulated and solved by the genetic algorithm (GA). The optimal topology of a WSN, on which a fully distributed optimal control approach is implemented for optimally controlling a multi-zone dedicated outdoor air system (DOAS), is designed by the proposed method. A co-simulation testbed is constructed to test and validate the proposed method by comparing the optimal topology with different topologies. The optimal topology provides satisfactory system control performance (CO2Ave = 784 ppm, CO2Max = 916 ppm, CO2 unmet hour = 1.82 h and EDOAS = 122.50 kWh), low network energy consumption (2564.12 J/Day) and high network stability (53.90 days). The proposed method facilitates the development and applications of IoT technologies in smart buildings.

Suggested Citation

  • Li, Wenzhuo & Tang, Rui & Wang, Shengwei & Zheng, Zhuang, 2023. "An optimal design method for communication topology of wireless sensor networks to implement fully distributed optimal control in IoT-enabled smart buildings," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009030
    DOI: 10.1016/j.apenergy.2023.121539
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923009030
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121539?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qi, Nanjian & Yin, Yajiang & Dai, Keren & Wu, Chengjun & Wang, Xiaofeng & You, Zheng, 2021. "Comprehensive optimized hybrid energy storage system for long-life solar-powered wireless sensor network nodes," Applied Energy, Elsevier, vol. 290(C).
    2. Li, Wenzhuo & Wang, Shengwei & Koo, Choongwan, 2021. "A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method," Applied Energy, Elsevier, vol. 287(C).
    3. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    4. Li, Wenzhuo & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Cha, Seung Hyun & Wang, Shengwei, 2020. "A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. Li, Wenzhuo & Wang, Shengwei, 2022. "A fully distributed optimal control approach for multi-zone dedicated outdoor air systems to be implemented in IoT-enabled building automation networks," Applied Energy, Elsevier, vol. 308(C).
    6. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    7. Su, Bing & Wang, Shengwei, 2020. "An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks," Applied Energy, Elsevier, vol. 274(C).
    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. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    2. Chen, Keyu & Fang, Shitong & Lai, Zhihui & Cao, Junyi & Liao, Wei-Hsin, 2024. "A plucking rotational energy harvester with tapered thickness and auxetic structures for increasing power output," Applied Energy, Elsevier, vol. 357(C).
    3. Demyan Yarmoshik & Alexander Rogozin & Alexander Gasnikov, 2024. "Decentralized optimization with affine constraints over time-varying networks," Computational Management Science, Springer, vol. 21(1), pages 1-23, June.

    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. Li, Wenzhuo & Wang, Shengwei, 2022. "A fully distributed optimal control approach for multi-zone dedicated outdoor air systems to be implemented in IoT-enabled building automation networks," Applied Energy, Elsevier, vol. 308(C).
    2. Li, Wenzhuo & Wang, Shengwei & Koo, Choongwan, 2021. "A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method," Applied Energy, Elsevier, vol. 287(C).
    3. Deng, Zhipeng & Wang, Xuezheng & Dong, Bing, 2023. "Quantum computing for future real-time building HVAC controls," Applied Energy, Elsevier, vol. 334(C).
    4. Su, Bing & Wang, Shengwei, 2021. "A delay-tolerant distributed optimal control method concerning uncertain information delays in IoT-enabled field control networks of building automation systems," Applied Energy, Elsevier, vol. 301(C).
    5. Li, Chunxiao & Cui, Can & Li, Ming, 2023. "A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency," Applied Energy, Elsevier, vol. 329(C).
    6. Su, Bing & Wang, Shengwei & Li, Wenzhuo, 2021. "Impacts of uncertain information delays on distributed real-time optimal controls for building HVAC systems deployed on IoT-enabled field control networks," Applied Energy, Elsevier, vol. 300(C).
    7. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    8. Mir M. Ali & Kheir Al-Kodmany & Paul J. Armstrong, 2023. "Energy Efficiency of Tall Buildings: A Global Snapshot of Innovative Design," Energies, MDPI, vol. 16(4), pages 1-23, February.
    9. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(C).
    10. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    11. Andrzej Ożadowicz, 2023. "Technical, Qualitative and Energy Analysis of Wireless Control Modules for Distributed Smart Home Systems," Future Internet, MDPI, vol. 15(9), pages 1-21, September.
    12. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    13. Liu, Xinzhi & Qi, Nanjian & Dai, Keren & Yin, Yajiang & Zhao, Jiahao & Wang, Xiaofeng & You, Zheng, 2022. "Sponge Supercapacitor rule-based energy management strategy for wireless sensor nodes optimized by using dynamic programing algorithm," Energy, Elsevier, vol. 239(PE).
    14. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    15. Marco Faifer & Luigi Piegari & Marco Rossi & Sergio Toscani, 2021. "An Average Model of DC–DC Step-Up Converter Considering Switching Losses and Parasitic Elements," Energies, MDPI, vol. 14(22), pages 1-18, November.
    16. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    17. Denis Artyukhov & Nikolay Gorshkov & Maria Vikulova & Nikolay Kiselev & Artem Zemtsov & Ivan Artyukhov, 2022. "Power Supply of Wireless Sensors Based on Energy Conversion of Separated Gas Flows by Thermoelectrochemical Cells," Energies, MDPI, vol. 15(4), pages 1-16, February.
    18. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
    19. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
    20. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "Multi-criteria evaluation of novel multi-objective model predictive control method for indoor thermal comfort," Energy, Elsevier, vol. 289(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:eee:appene:v:349:y:2023:i:c:s0306261923009030. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.