## 5 Dynamic Optimization of the Crowdsourcing Platform
### Dynamic Optimization Engine Based on Metric Indices
On the other hand, all dynamic parameter optimization approaches achieve consensus levels between 90% and 100%, with \(DQL\)-\(Act\) being the one that obtains the lowest values. Figure 9 shows the average utilization of the ISP network. The \(x\)-axis shows the name of the routing algorithm used at the beginning of the experiment. Again, at the top we show the results obtained when \(H=\) 30 at the beginning of the experiment, and at the bottom we show when \(H=\) 50. The results show that the ISP utilization tends to be higher than 40% when the experiment begins with the Baseline routing algorithm, which uses point-to-point communication between the server and the peers through the ISP. If the initial value \(H=\) 30, only the dynamic optimization approaches based on a metric index can keep the average utilization of the ISP below 40%. Meanwhile, when the initial value \(H=\) 50, the DQL-O approach drastically reduces the ISP utilization. When we use the Centralized or Distributed routing algorithms at the beginning of the experiment and \(H=\) 30, the results show that the \(DQL\)-\(o\)-\(Act\) dynamic optimization approach reports the highest utilization. All the remaining approaches tend to keep the ISP utilization below 40%. With \(H=\) 50, again the dynamic optimization approaches tend to keep the ISP utilization below 40%. Only the \(IndexLimit\) approach, initializing the execution with the Centralized algorithm, and the \(Index\) approach, using Distributed as the initial routing algorithm, report a high ISP utilization. Figure 8: Percentage of consensus reported with different dynamic optimization approaches and with no optimization (None). Figure 10 shows the time required to process the \(nQ\) images. With an initial value \(H=30\), we can reduce the processing time by 20% when applying a dynamic optimization approach. In this case, the \(IndexLimit\) approach reports the lowest processing times. With \(H=50\) and stating with the Baseline routing algorithm, we can reduce the processing time by 80% by using a dynamic optimization approach. In this case, the \(DQL\) approach presents the lowest processing time when the experiment begins with the Baseline or with the Centralized routing algorithms. The index-based approaches present competitive results. Otherwise, when starting with the Distributed routing algorithm, the \(Index\) approach presents the lowest processing time. Overall, the analytical approach reports a good percentage of consensus and low processing times, but the ISP utilization tends to be high. Regarding the metric index-based approaches, all the proposed versions report a high percentage of consensus and an ISP utilization below 40%, but the \(IndexLimite\) version reports lower processing times with \(H=30\) and the \(Index\) version with \(H=50\). Finally, the \(DQL\) approach is the one that generally obtains the best results for all the metrics among the reinforcement-learning-based approaches. Figure 10: Processing time (workTime) obtained with the different dynamic optimization approaches and with no optimization (None). Figure 9: Average ISP network utilization obtained with different dynamic optimization approaches and with no optimization (None). ### Execution Time of the Dynamic Optimization Approaches

Figure 11 shows the CPU time of the actions performed by the parameter optimization approaches with the metric index (_Index_) and with DRL (_DQL_). The intervals in which the signal is 1 correspond to the intervals where the CPU is used by the _Index_ or the _DQL_, that is, the active intervals of the optimization algorithms. In periods when the signal is 0, only the crowdsoursing platform is active. The graph shows the results obtained for 10,000 images, an initial value of \(H=50\), and applying the Distributed routing algorithm. Similar results were obtained for other configurations. The results show that the _DQL_ reports a larger number of optimization actions, but each one is very fast.