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Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability

Author

Listed:
  • Wen-Cheng Wang

    (College of Innovation and Entrepreneurship Education, Yango University, Fuzhou 350015, China)

  • Ngakan Ketut Acwin Dwijendra

    (Faculty of Engineering, Udayana University, Denpasar 80361, Indonesia)

  • Biju Theruvil Sayed

    (Department of Computer Science, Dhofar University, P.O. Box 2509, Salalah 211, Oman)

  • José Ricardo Nuñez Alvarez

    (Department of Energy, Universidad de la Costa, Barranquilla 080001, Colombia)

  • Mohammed Al-Bahrani

    (Chemical Engineering and Petroleum Industries Department, Al-Mustaqbal University College, Babylon 51001, Iraq)

  • Aníbal Alviz-Meza

    (Grupo de Investigación en Deterioro de Materiales, Transición Energética y Ciencia de Datos DANT3, Facultad de Ingenieria y Urbanismo, Universidad Señor de Sipán, Km 5 Via Pimentel, Chiclayo 14001, Peru)

  • Yulineth Cárdenas-Escrocia

    (Department of Energy, Universidad de la Costa, Barranquilla 080001, Colombia)

Abstract

The internal components of a smart building interact through a compatible fabric and logic. A smart building integrates systems, structure, services, management, and their interrelationships to create a dynamic and cost-efficient environment. Smart buildings reduce the amount of cooling and heating load required to cool and heat spaces, thereby lowering operating costs and energy consumption without sacrificing occupant comfort. Smart structures are an Internet of Things (IoT) concern. The Internet of Things is a global network that virtualizes commonplace objects. The Internet of Things infuses non-technical objects with technology. IoT development has led to the creation of new protocols based on architectures for wireless sensor networks. Energy conservation extends the life and improves the performance of these networks, while overcoming the limitations of IoT node batteries. This research seeks to develop a data transmission model for routing IoT data in smart buildings. Utilization of intelligent object clustering and particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), and fractional chaotic order particle swarm optimization (FCPSO) optimization methods. Using the proposed algorithm to minimize energy consumption in the IoT is possible due to the algorithm’s ability to mitigate the problem by considering the number of parameters that can have a significant impact on performance, which is the goal of many optimization approaches.

Suggested Citation

  • Wen-Cheng Wang & Ngakan Ketut Acwin Dwijendra & Biju Theruvil Sayed & José Ricardo Nuñez Alvarez & Mohammed Al-Bahrani & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6475-:d:1120734
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    References listed on IDEAS

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    1. Hernán Peraza-Vázquez & Adrián Peña-Delgado & Prakash Ranjan & Chetan Barde & Arvind Choubey & Ana Beatriz Morales-Cepeda, 2021. "A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade," Mathematics, MDPI, vol. 10(1), pages 1-32, December.
    2. Abdul Wadood & Chang-Hwan Kim & Tahir Khurshiad & Saeid Gholami Farkoush & Sang-Bong Rhee, 2018. "Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method," Energies, MDPI, vol. 11(4), pages 1-20, April.
    3. Hernán Peraza-Vázquez & Adrián F. Peña-Delgado & Gustavo Echavarría-Castillo & Ana Beatriz Morales-Cepeda & Jonás Velasco-Álvarez & Fernando Ruiz-Perez, 2021. "A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-19, September.
    4. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
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    Cited by:

    1. Alejandro Valencia-Arias & Juana Ramírez Dávila & Wilmer Londoño-Celis & Lucia Palacios-Moya & Julio Leyrer Hernández & Erica Agudelo-Ceballos & Hernán Uribe-Bedoya, 2024. "Research Trends in the Use of the Internet of Things in Sustainability Practices: A Systematic Review," Sustainability, MDPI, vol. 16(7), pages 1-23, March.
    2. Sebastian Terence & Jude Immaculate & Anishin Raj & Jeba Nadarajan, 2024. "Systematic Review on Internet of Things in Smart Livestock Management Systems," Sustainability, MDPI, vol. 16(10), pages 1-37, May.
    3. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumarai Chelvan & Hossen Teimoorinia, 2024. "Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode," Energies, MDPI, vol. 17(24), pages 1-25, December.

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