# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability
## 2 Related Works  
Numerous studies have been undertaken to develop answers for the effective application of IoT-related building energy consumption. Even though great advancements have been made, the methodologies are quite diverse, and the challenges are still extremely difficult, posing an ongoing problem for the research community. In reality, equipment and appliances in a smart home can be monitored and managed in the form of objects by employing IoT technology and web-based or mobile applications based on this technology [11; 12]. In general, an IoT node has limited resources, including processing power and energy supply. If an IoT node must operate in an environment without a direct power connection, it requires a battery. To solve this issue, a significant amount of research has been undertaken on how to reduce energy consumption in wireless sensor networks (WSNs) as a battery-constrained sensing technology, and a number of techniques and approaches have been created to boost battery usage in an effective manner [13; 14]. A WSN may be utilized as a component of IoT technology to build a variety of IoT platforms. The development of various routing algorithms, such as opportunistic routing and greedy algorithms, can reduce sensor network energy usage. In addition to the development of routing protocols, clustering techniques in sensor networks are an extra method for lowering sensor energy consumption and extending network lifetime. The clustering-of-objects approach is one of the efficient ways to lower energy usage during the information transfer phase in the IoT. Each cluster in clustering has a node designated as the cluster head, which is in charge of organizing network activities and gathering data from sensor nodes. Additionally, the cluster head eliminates unnecessary and redundant data packets, reducing overheads and interference. By decreasing the size and quantity of nodes in the routing, it also lowers routing complexity [15; 16]. Additionally, the clustering approach makes sensor networks more scalable and stable. Load balancing, which divides network responsibilities between member nodes according to energy and battery memory, is another benefit of the clustering process. The primary objective of this study is to reduce energy consumption during the data transmission cycle. IoT applications are extremely time-sensitive and crucial; therefore, the majority of communication and processing operations must be accomplished in a restricted amount of time to avoid negative outcomes. Consequently, ensuring real-time support in large-scale IoT networks is one of the most significant and demanding research topics. This allows the communication center to enhance the monitoring of agents. Cloud services can provide applications such as social networks for agent monitoring, environmental data analysis, and network analysis, etc., to advanced users. Cloud computing plays a crucial role in delivering high-performance computing and supporting several operating system platforms [17]. A genetic algorithm is offered by [PERSON] et al. [18] as a solution for enhancing the energy efficiency of existing Cloud of Things energy plans. The ETCORA algorithm is contrasted with the proposed strategy, which is then subjected to extensive numerical simulations to demonstrate its utility. The results of the analysis indicate that the proposed strategy for optimizing energy use results in enhanced overall performance. A fuzzy algorithm-based method for modeling energy loss optimization scheduling with many targets is proposed by [PERSON] and [PERSON] [19]. In the IoT context, the multi-objective equipment scheduling optimization equation is created, and the fuzzy algorithm is introduced into the single-target energy loss problem. In order to reduce the overall energy consumption of device scheduling in an IoT context, the algorithm searches for idle device time and optimizes the device scheduling energy consumption model. [PERSON] et al.