# Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities
## 5 Results and Analysis
### Load Balancing Greedy vs. Proposed Algorithm vs. Round Robin vs. Round Robin with LJF  
Figure 4 shows the comparative results of the proposed scheme as compared to its competitors in computing nonce value for the varying number of data blocks. The number of data blocks is incremented by two data blocks from 2 to 22 data blocks simultaneously with 3 fog mining nodes. The results show that our proposed scheme outperformed compared to greedy [45], round-robin [46], and round-robin with LJF. The results show that our proposed scheme reduces the nonce computing time by 35.5%, 18.38%, and 15.94% against round-robin, greedy, and round-robin with the LJF algorithms, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Fog Mining Node coverage area & 1500 m \\ Distance between fog mining node and Nonce requesting data blocks & 50–1500 \\ Number of simultaneous data blocks for each mining node & 6–30 \\ Number of mining nodes & 2–10 \\ Downloading data rate from cloud mining server & 2 Mbps \\ Downloading data rate from fog mining node & 8 Mbps \\ Number of Nonce computing by fog mining node & 2 \\ Emergency data blocks & 2–10 \\ Medium level sensitive data blocks & 2–10 \\ Least sensitive data blocks & 2–10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
The results in Figure 5 show the performance of our proposed scheme in computing the nonce value against varying numbers of fog mining nodes when data blocks are fixed. It is evident from the results that total processing time reduces if we increase the no. of the fog mining node for a fixed number of 24 jobs with varying processing times. The results verify that with the increase in the number of mining nodes, the job processing time reduces because the same number of jobs are being distributed among multiple mining nodes. It is evident from the results that our proposed scheme reduces the processing time by up to 28 percent against the round-robin algorithm, up to 29.41 percent against the greedy algorithm, and up to 20 percent against the round-robin with the LJF algorithm.  
Figure 4: Number of computed nonce against a varying number of data block requests.  
Figure 5: Nonce computing against varying number of mining nodes.  
To evaluate the effectiveness of our proposed scheme, the results in terms of standard deviation \(\omega\) for varying numbers of mining nodes and the varying number of jobs are shown in Figures 6 and 7, respectively.  
Standard deviation is calculated as  
\[\sigma=\sqrt{\sum_{i=1}^{N}(X-Y)^{2}/N} \tag{8}\]  
where \(X\) is the mean time required to compute the nonce value of the requested blocks, \(Y\) is the time computed by an algorithm, and \(N\) is the number of instances chosen.  
Results shown in Figures 6 and 7 verify that the proposed scheme merely deviates from the mean load computed against a varying number of blocks with fixed mining nodes and for a varying number of mining nodes with fixed data blocks, respectively.  
Figure 6 shows that the standard deviation values of \(DSP_{SHS}\) are far less than the other algorithms when the number of data block requests increases from 4 to 20 in five different instances and the number of fog mining nodes is 3. It is evident from the results that \(DSP_{SHS}\) merely deviates from the mean mining fog nodes load as compared to round robin, greedy, and round robin with LJF algorithms.  
Results in Figure 7 compute the standard deviation values for a fixed number of data blocks with varying numbers of fog mining nodes. In these results, the data block requests are fixed to 20; however, fog mining nodes are increasing from 2 to 7 in six different instances. It is evident from the results that our proposed \(DSP_{SHS}\) is very close to the mean load values of the mining nodes and prominently less than the other three schemes.  
Figure 6: Standard deviation against a varying number of mining nodes.