# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability
## 5 Conclusions and Suggestions for Future Work  
The energy consumption model in IoT networks is dependent on the hardware, data processing, and communication interface. In this study, the cycle-based model and information transfer to the base station and between items were utilized for energy consumption and the IoT network. The most significant challenges in IoT routing are each node's power consumption, comparability, fault tolerance, and network dynamics. For effective communication and information transfer, it is necessary to evaluate a node's quality using the appropriate criteria. In this study, the energy used to transmit and receive data in the nodes, the energy available in each node, the distance between two nodes, and the data transmission rate between two nodes were evaluated. In accordance with the goal of this study, which is to optimize energy consumption in the IoT, an FCPSO algorithm was used to reduce energy consumption during information transmission. Using the FCPSO algorithm to reduce energy consumption in the IoT is possible due to the algorithm's ability to optimize the problem by considering the number of parameters that can have a significant impact on the performance of the problem, which is the goal of many optimization problems. The simulation results demonstrated that employing an FCPSO method improves and decreases energy consumption during program execution and the information transfer cycle. The most significant limitation of this study is that there are not enough data that can be relied on to construct an optimal model. Because of this, there is a greater possibility that the model will not converge. Another topic that deserves attention in this area is the degree of computational difficulty that new models of data transmission require.  
According to the outcomes of implementation and ongoing study in this subject, the following recommendations can be made for future work:  
* Practical implementation of the proposed method in a smart environment that uses the IoT, such as smart buildings.
* Using the proposed method for multicast routing, additional research can be conducted in this area, such as how to select routes.
* Utilizing alternative evolutionary algorithms and evaluating their outcomes to increase the reduction of current overheads and reach more ideal results.
* Combining the proposed method with other algorithms, such as tree-based algorithms, and presenting it within the IoT domain.  
Figure 8: Comparison of accuracy criteria with different methods.  
**Author Contributions:** Conceptualization, N.K.A.D.; Methodology, W.-C.W. and B.T.S.; Software, B.T.S. and Y.C.-E.; Validation, J.R.N.A.; Formal analysis, W.-C.W. and J.R.N.A.; Investigation, N.K.A.D., J.R.N.A., M.A.-B., A.A.-M. and Y.C.-E.; Resources, M.A.-B. and A.A.-M.; Data curation, M.A.-B. and A.A.-M.; Writing--original draft, N.K.A.D.; Writing--review and editing J.R.N.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.