# Blockchain and Machine Learning Inspired Secure Smart Home Communication Network
## 4 Results and Discussion  
The proposed model of a smart home network was designed in the Matlab Simulation and Google Colab, and the blockchain ledger was created to evaluate the effects of QoS and quantitative parameters, namely the overall computation complexity, false detection rate, precision, recall, and f-measure, along with classification accuracy. In order to do so, the proposed work performed 20,000 simulations with the following system model to collect the Throughput and BER of each simulation, which were fed to the learning mechanism. The proposed work was tested for more than 1000 iterations, and on average, the convergence of the global optimum was found to be close to the global optimum. The system specifications with reference to blockchain and cloud computing technology are summarized in Table 3. Additionally, the system and system model specifications are stated in Table 4.  
The proposed work was compared with two state-of-the-art algorithms in terms of QoS and evaluated for multiple classifiers with quantitative parameters. The training and classification model was used on the transaction record and categorized it by providing a label of Smart T, Mod T, or Avoid T. The neural network learns to recognize patterns in the data that are associated with each of the three categories and then uses this knowledge to classify new examples. The computation complexity can increase significantly as the number of devices and transactions in the network increases. Additionally, in a proof-of-work (PoW) blockchain, the process of mining can be very computationally intensive, which can be a challenge for resource-constrained IoT devices. Table 5 presents the value of the computation complexity and the comparison with other state-of-the-art techniques for similar types of environments. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**System Information**} \\ \hline RAM & 4 GB \\ \hline Processor & intel core i3 530 \\ \hline Memory & DDR3 \\ \hline HD Capacity & 500 GB \\ \hline \multicolumn{2}{c}{**System Model Information**} \\ \hline Number of Users & 50–500 \\ \hline Simulation Area & 4000 m\({}^{2}\) \\ \hline Channel of Communication & Rayleigh channel \\ \hline Interpolation measurement & Lagrange interpolation \\ \hline Channel Capacity & 96,000 symbols per second \\ \hline Channel gain & 0.0023 units \\ \hline Evaluation Parameters & Throughput, BER \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ordinal measures of system and system model. \begin{table}
\begin{tabular}{l l} \hline \hline Number of data centers & 2 \\ \hline Number of PMs to handle & 10 \\ \hline Cloud Type & Xen \\ \hline Blockchain Type & Firebase \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System specifications. The average computation complexity of the proposed algorithm across all test samples was lower than that of the other algorithms. This can be seen by comparing the average value of the 'Computation Complexity Proposed' column to the average values of the other columns. For example, the average computation complexity of the proposed algorithm for all test samples was 3.544, while the average computation complexity of the algorithm based on blockchain and fused machine learning was 3.944, indicating an improvement of 10.14% by the proposed algorithm over that in the existing study. The percent improvement of the proposed algorithm over the other algorithms varied depending on the number of test samples. For example, for 500 test samples, the proposed algorithm had a computation complexity of 0.765, which was 11.1% lower. However, for 5000 test samples, the percent improvement over the existing study dropped to 7.07%. The reason for the improved performance of the proposed algorithm is attributed to its efficient data selection, as indicated in the table by the decreasing values of proposed computation complexity with increasing test samples. The proposed work was also evaluated for the false authentication rate against the same set of algorithms.