# Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities
## 5 Results and Analysis
### Proposed Job Sequencing with the Deadline  
After the efficient distribution of data blocks to mining nodes for nonce computation, the second algorithm of our proposed scheme effectively scrutinizes them in accordance with their sensitivity levels. Varying sizes of patients' data blocks are divided into three different sensitivity levels. The number of nonces against varying numbers of data blocks is such that data requests of all three sensitivity levels of data blocks are uniform. The results are compared with the first come first serve (FCFS) algorithm by considering the same computing capacity of all fog mining nodes.  
The percentage of under-processed nonce computation against a varying number of data block requests for six and ten mining nodes, respectively, are shown in Figures 8 and 9, respectively. Both results include three sub-plots that represent three different priority levels data blocks, such as high-level sensitivity, medium-level sensitivity, and low sensitivity level patients' data blocks. In these results, we consider that there is an equal number of each type of data block present in the cache of each mining node, such as 2 data blocks in high-priority nonce computation data blocks indicating that there are 6 data blocks with 2 each in high, medium and low priority levels. The results shown in Figure 8 show that our proposed algorithm selects 100% nonce against high-level sensitive data blocks for varying numbers of data block requests when the number of high-priority data blocks in a mining node is less than data blocks. For medium-level sensitive data blocks, the proposed scheme computes all nonce when the number of requests is 2 each. However, when the number of nonce requests increases, the number of nonces computed by FCFS is more than our proposed scheme. This is because the mining node has already selected high-priority blocks, and it cannot process the other priority-level blocks. The same impact is shown for low-priority data blocks. It is evident from the results shown in Figure 9 that the proposed scheme accommodates most of the high-priority data blocks as compared to FCFS and only computes nonces for the medium- or low-priority blocks when there is no high-priority block left for computation.  
Figure 7: Standard deviation against a varying number of jobs.  
Results shown in Figure 10 are obtained against varying numbers of mining nodes when there is a fixed number of nonce requests. In these results, there is a total of 12 nonce computation requests that are equally distributed in high-, medium-, and low-priority patient data blocks. It is evident from the results that our proposed scheme entertains a maximum of the high-priority data blocks and 50% of high-priority data blocks are entertained when the number of mining nodes is only two. The nonce computation of the high-priority patients' data block is 100% when the number of mining nodes is 4 and above. However, FCFS only computes the nonce of those data blocks that arrive in their cache first. The results further show that when the number of mining nodes is more than the available high-priority data blocks, then it allows medium-priority data blocks in computing their nonce values, and nonce against low-priority data blocks are only entertained when all the high-priority data blocks are successfully entertained.  
Figure 8: Percentage of the computed nonce for 6 mining nodes with varying number of data blocks.  
Figure 9: Percentage of the computed nonce for 10 mining nodes with varying numbers of data blocks.