## 4 Proposed Frameworks with the Utilization of Competing Miners  
### The Impact of Competing Miners in a Blockchain  
As discussed in the earlier sections, having miners compete with each other on device (i.e., UAV) is expected to require much less time for solving the PoW compared to a traditional blockchain that employs only a single miner. In our proposed framework, to improve the mining speed, we employ two or more miners on device and make them compete with each other. The miners are all split into different processes, which makes it easier for a manager to have control over them. They are all trying to solve the same PoW hash, and when a miner solves the PoW hash, it gets a reward as an incentive and the other miners are stopped so that no extra computing resources are used. Our proposed improvement is compared to the conventional improvement made in [PERSON] et al. [4], where the miners are set to solve different PoW hashes but on the same block. To ensure that our research and the research with the parallel miners are fairly compared, we conducted the experiments based on the premises set in [PERSON] et al. [4]. The CPU clock speed was set to 2.2 GHz with 4 GB allocated RAM on a Windows operating system. The blockchain was made in Python and each miner was given just 10% of the total resources available. The numerical results from the experiments that were conducted are presented by Table 2 and Figure 4.  
In Table 2, we can observe how our proposed method of competing miners compares to that of parallel miners proposed in [PERSON] et al. [4]. The results are given by the average time to mine a block by each difficulty level D. By a quick glance, we can easily see that the proposed scheme of competing miners achieves a better and faster mining time than the opposition. At a low difficulty level, the proposed scheme with competing miners has an average mining speed that is more than double as fast as the parallel miners, which \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Avg Mining Time (Seconds/Difficulty** & **Single (1) Miner** & **3 Parallel Miners** & **5 Parallel Miners** & **3 Competing Miners** & **5 Competing Miners** \\ \hline D = 5 & 60 & 53 & 45 & 23 & 18 \\ D = 6 & 24 & 180 & 160 & 159 & 90 \\ D = 7 & 895 & 710 & 590 & 531 & 450 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of parallel miners vs. competing miners in average mining time per difficulty level. Figure 4: Improvement percentage of parallel and competing miners compared to a single miner. is an amazing improvement. Looking at the higher difficulty levels, we discover that the competing miners still have a better performance, but the gap seems to be closing in. This is also depicted in Figure 4, where both of the frameworks are put against each other to see how much, in percentage, they improve compared to the single miner at each difficulty level. The huge difference we see in Table 2 is confirmed by the large gap between the lines in this graph, where the framework of competing miners reaches a maximum of 70% improvement from the case of a single miner. When comparing the competing miners with the parallel miners, we find some interesting insights. To begin with, the framework of competing miners performs much better than that of parallel miners at a lower difficulty level than it does when the difficulty increases. The biggest gap is at three miners solving the PoW with difficulty level set to 5. In this parameter, the competing miners achieve a substantial 50% higher improvement than the parallel miners. However, increasing the difficulty seems to decrease the gap between the two frameworks. A possible reason behind this can be the limitations of computing resources set by the author in [PERSON] et al. [4]. This experiment was only given a limited amount of resources, which resulted in problems when scaling and wanting to solve more complex equations.