## 5 Dynamic Optimization of the Crowdsourcing Platform
### Dynamic Optimization Engine Based on Metric Indices
#### 7.4.2 Metric Index Evaluation

In this section, we analyze the impact of using different metric indices on the dynamic optimization engine. Figure 18 shows the average number of distance evaluations for a total of 1000 images. In this experiment, we use different metric space indices like the pivot index with 6 and 10 pivots and the Sparse Spatial Selection (SSS) [58] method with different values of the \(\alpha\) parameter. We also evaluate the List of Clusters [59] index with different cluster sizes. The results show that the performance of the List of Clusters (Lcluster) is highly dependent on the cluster size, reporting better results for larger cluster sizes. On the other hand, the results show that a six-pivot index--the configuration used in this work--obtains the best results. Nevertheless, if necessary, the metric index can be easily changed to a new one. Figure 8:0 Number of distance evaluations for different values for the constants \(R_{1}\), \(R_{2}\), and \(R_{3}\) in the metric indices. ## 8 Conclusions

In this paper, we present and evaluate a metric space-based approach for the dynamic optimization of parameters. The proposed approach is devised to improve the performance of a crowdsourcing platform built with a P2P network designed to operate in the context of natural disasters and to deal with unexpected bursts of incoming requests. The context requires that the deployment of crowdsourcing campaigns achieves results in the shortest possible time and without saturating the platform's resources. The proposal incorporates the concept of similarity to give the platform more adequate parameter configurations according to the characteristics of the current workload and the size of the volunteer network. The proposal uses a metric database formed of vectors with elements describing the non-controllable parameters, controllable parameters, and metrics. We used a pivot index for the evaluation of the proposal, but other metric indices can be easily incorporated. We presented two versions of the dynamic parameter optimization algorithm with metric indices and compared them with an analytical optimization approach and with different algorithms using deep reinforcement learning. We evaluated the processing time, the utilization of resources, and the voting consensus with different routing algorithms. The results show that the metric index dynamic optimization approach achieves competitive processing times, high voting consensus, and is capable of keeping the utilization below 40%. Additionally, we showed that the proposal can scale for a high number of tasks and that incorporating dynamic parameter optimization allows us to fulfill the platform's performance requirements and also reduces the processing time by 40% when the platform is under a high workload. As future work, we plan to incorporate other deep reinforcement learning algorithms to the platform. Furthermore, it would be interesting to incorporate more complex network topologies involving different communities of volunteers. The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research was funded by CONICYT Basal funds FB0001, Fondef ID15I10560. Data Availability StatementData were contained within the article. This research was supported by the supercomputing infrastructure of the NLHPC Chile. The authors declare no conflict of interest. ## References

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