# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability
## 1 Introduction  
The term Internet of Things (IoT) refers to a vast network of actual physical objects such as machines, buildings, and other things that have sensors, network connections, and software integrated into them as a tiny system [1]. These embedded systems share information with one another as well as gathering data from the environment around the physical items. The integration of the physical world based on computer systems through the IoT improves efficiency, accuracy, and the economy. Cyber-physical systems are a broad term for sensors and actuators used in the IoT [2, 3].  
These kinds of systems can be said to contain various forms of smart cities, residences, and transportation. The IoT is made up of several components, including sensors, software, and hardware, which communicate with one another using radio waves [4]. Each of these systems communicates with the others over wired and wireless networks if the grouping of these components is employed as an embedded system [5, 6]. Establishing communication between things in the physical world enables real-time awareness ofthe physical world and an emergency response based on the data collected. The ever-increasing development of smart buildings and the rate of innovation in this field have prompted numerous researchers to implement a wide range of applications [7; 8]. A smart building is one that is equipped with a robust communication infrastructure that can continuously react to and adapt to changing environmental circumstances, and that enables the building's occupants to use the available resources more efficiently and boost their security and comfort [9]. In order to lower the expenses of the construction industry and make optimal use of technology and the application of communication and computer technologies, the energy efficiency of building management and automation systems will increase. The savings coming from the employment of these solutions can therefore quickly offset the associated expenses [10]. Intelligent control systems will have a high degree of adaptability, allowing them to be quickly adapted to the diverse optimization strategies for energy usage in smart buildings. During operation, it is also simple to adjust and optimize operations for improved management, energy cost reduction, and maintenance cost reduction. In an intelligent building, many of the behaviors that occupants display habitually and unconsciously are performed by intelligent systems, saving time and money on labor.  
In many of the proposed methods, the authors have addressed only one of these weaknesses, and the simultaneous elimination of these two weaknesses has received less attention. Various algorithms and models, such as greedy routing, opportunistic routing, data transmission methods, and clustering, have been developed in order to reduce energy consumption and extend the lifetime of wireless nodes in the IoT.  
In this study, two tools are employed concurrently to mitigate the deficiencies of the standard particle swarm optimization algorithm. We wish to determine which of chaotic particle swarm optimization (CPSO) and fractional chaotic order particle swarm optimization (FCPSO) improves the overall convergence of the particle swarm optimization (PSO) algorithm and protects it from local optima. Additionally, fractional order derivatives are employed to accelerate the convergence rate of the particles toward the optimal general response. Taking into account the significance of optimizing energy consumption in organizations and smart buildings, the proposed method is based on the clustering of intelligent objects and fuzzy systems. The proposed method aims to optimize energy consumption in smart buildings via the IoT.