# Blockchain and Machine Learning Inspired Secure Smart Home Communication Network
## 5 Conclusions  
This research paper proposed an architecture that incorporates IoT, blockchain, cloud, and artificial intelligence techniques for a secure and efficient communication network. The paper provides a detailed description of the proposed architecture and its components, including the user interface, ledger generation, data evaluation, and decision-making techniques. The contributions of this paper are the secure application of the blockchain layer and cloud-based data evaluation layer and the efficient utilization of the artificial intelligence-based algorithm. In order to incorporate efficient communication and security aspects, the paper designed an updated dragonfly algorithm that supports the training layer to pick the best samples for the three classes designed, namely \"SMART T\", \"MOD-T\", and \"AVO-T\". Specifically, for all test samples, the proposed algorithm had an average computation complexity of 3.544, while the existing algorithm had an average computation complexity of 3.944, indicating an improvement of 10.14% by the proposed algorithm. The percent improvement varied depending on the number of test samples, with the proposed algorithm showing a lower computation complexity than the existing algorithm based on blockchain and fused machine learning for all test sample sizes but with a decreasing percent improvement as the number of test samples increased. The improved performance of the proposed algorithm is attributed to its efficient data selection, which is indicated by the decreasing values of proposed computation complexity with increasing test samples. In the case of false authentication analysis for a sample size of 5000, the proposed algorithm had 173 false authentications, while the existing work of [PERSON], [PERSON], and [PERSON] had 177, 181, and 185 false authentications, respectively. This represents a maximum % improvement of 6.49% for the proposed algorithm. Furthermore, the proposed work exhibited a high prediction accuracy of 96.54% for identifying false authentications, thus proving the ability of the proposed work to handle security in smart home networks in comparison with existing works. Furthermore, this research provides future possibilities for the integration of these techniques with other technologies, such as 5G and edge computing, for more secure and efficient communication networks. Overall, the proposed architecture has potential applications in various fields, such as home care, hospitals, city services, and data marketplaces.  
Conceptualization, S.M., D.A., K., S.V., M.K., N.Z.J., R.M.G. and S.K.R.; Methodology, S.M., D.A., K., S.V., M.K., N.Z.J., R.M.G. and S.K.R.; Software, S.M., D.A., K., S.V., M.K., N.Z.J., R.M.G. and S.K.R.; Validation, S.M., D.A., K., S.V., M.K., N.Z.J. and S.K.R.; Formal analysis, S.M., D.A., K., S.V., M.K., N.Z.J. and S.K.R.; Investigation, S.M., D.A., K., S.V., M.K., N.Z.J. and R.M.G.; Resources, S.M., D.A., K., S.V., M.K. and N.Z.J.; Data curation, S.M., D.A., K., M.K. and R.M.G.; Writing--original draft, S.M.; Writing--review & editing, D.A., K., S.V., M.K., N.Z.J., R.M.G. and S.K.R.; Visualization, N.Z.J.; Supervision, S.M. and N.Z.J.; Funding acquisition, K., S.V., N.Z.J., R.M.G. and S.K.R. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Accuracy comparison.  
**Funding:** Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Acknowledgments:** We acknowledge the support from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.