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

Author:

Wang Wen-Cheng1ORCID,Dwijendra Ngakan Ketut Acwin2,Sayed Biju Theruvil3ORCID,Alvarez José Ricardo Nuñez4ORCID,Al-Bahrani Mohammed5,Alviz-Meza Aníbal6,Cárdenas-Escrocia Yulineth4

Affiliation:

1. College of Innovation and Entrepreneurship Education, Yango University, Fuzhou 350015, China

2. Faculty of Engineering, Udayana University, Denpasar 80361, Indonesia

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

4. Department of Energy, Universidad de la Costa, Barranquilla 080001, Colombia

5. Chemical Engineering and Petroleum Industries Department, Al-Mustaqbal University College, Babylon 51001, Iraq

6. 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

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.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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