## 5 Dynamic Optimization of the Crowdsourcing Platform
### Dynamic Optimization Engine Based on Metric Indices
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Params** & **Algorithm** & **Cons.** & **std** & **workT** & **std** & **ISP\_Util** & **std** \\ \hline ALG-H & IndexLimit & 0.992 & 0.007 & 111,833 & 17.429 & 0.403 & 0.254 \\ \hline ALG-H-C & Index & 0.972 & 0.023 & 104,500 & 13.531 & 0.517 & 0.245 \\ \hline ALG-H-C & IndexF & 0.993 & 0.012 & 122,333 & 31.602 & 0.414 & 0.228 \\ \hline ALG-H-C & IndexLimit & 0.980 & 0.008 & 126,833 & 24.425 & 0.277 & 0.033 \\ \hline ALG-H-TLL & Index & **0.998** & 0.001 & 102,333 & 14.264 & 0.465 & 0.208 \\ \hline ALG-H-TLL & IndexF & 0.963 & 0.091 & 180,000 & 68.230 & 0.547 & 0.241 \\ \hline ALG-H-TLL & IndexLimit & 0.994 & 0.007 & **99.167** & 8.353 & 0.623 & 0.334 \\ \hline ALG-TTL & Index & 0.169 & 0.060 & 166,333 & 18.970 & 0.630 & 0.267 \\ \hline ALG-TTL & IndexF & 0.210 & 0.087 & 179,500 & 19.087 & 0.658 & 0.314 \\ \hline ALG-TTL & IndexLimit & 0.297 & 0.145 & 188,667 & 7.711 & 0.491 & 0.098 \\ \hline ALG-TTL-C & Index & 0.187 & 0.144 & 170,333 & 19.336 & 0.690 & 0.300 \\ \hline ALG-TTL-C & IndexF & 0.285 & 0.116 & 194,333 & 20.432 & 0.522 & 0.226 \\ \hline ALG-TTL-C & IndexLimit & 0.319 & 0.143 & 195,667 & 27.259 & 0.560 & 0.223 \\ \hline All & Index & 0.964 & 0.022 & 108,667 & 14.720 & 0.535 & 0.214 \\ \hline All & IndexF & 0.960 & 0.060 & 134,000 & 29.604 & 0.349 & 0.056 \\ \hline All & IndexLimit & 0.987 & 0.009 & 123,667 & 21.417 & 0.421 & 0.144 \\ \hline C & Index & 0.490 & 0.307 & 228,667 & 33.146 & 0.578 & 0.313 \\ \hline C & IndexF & 0.496 & 0.319 & 229,667 & 33.068 & 0.575 & 0.316 \\ \hline C & IndexLimit & 0.515 & 0.317 & 228,833 & 32.443 & 0.575 & 0.316 \\ \hline H & Index & 0.997 & 0.002 & 124,167 & 47.864 & 0.477 & 0.313 \\ \hline H & IndexF & **0.998** & 0.001 & 118,667 & 42.908 & 0.471 & 0.294 \\ \hline H & IndexLimit & 0.992 & 0.005 & 109,667 & 24.080 & 0.472 & 0.326 \\ \hline H-C & Index & 0.980 & 0.019 & 136,833 & 46.893 & 0.462 & 0.301 \\ \hline H-C & IndexF & 0.976 & 0.013 & 132,167 & 42.832 & 0.455 & 0.281 \\ \hline H-C & IndexLimit & 0.987 & 0.023 & 113,833 & 25.143 & 0.429 & 0.267 \\ \hline H-TTL & Index & 0.776 & 0.331 & 114,000 & 36.006 & 0.505 & 0.279 \\ \hline H-TTL & IndexF & 0.900 & 0.148 & 113,833 & 36.191 & 0.492 & 0.279 \\ \hline H-TTL & IndexLimit & 0.993 & 0.004 & 109,167 & 23.507 & 0.482 & 0.323 \\ \hline H-TTL-C & Index & 0.774 & 0.286 & 121,000 & 25.675 & 0.502 & 0.259 \\ \hline H-TTL-C & IndexF & 0.921 & 0.142 & 122,833 & 26.739 & 0.480 & 0.258 \\ \hline H-TTL-C & IndexLimit & 0.991 & 0.013 & 114,000 & 20.986 & 0.438 & 0.269 \\ \hline TTL & Index & 0.350 & 0.339 & 194,333 & 44.148 & 0.615 & 0.291 \\ \hline TTL & IndexF & 0.384 & 0.329 & 199,500 & 24.882 & 0.582 & 0.309 \\ \hline TTL & IndexLimit & 0.384 & 0.312 & 213,000 & 37.709 & 0.586 & 0.309 \\ \hline TTL-C & Index & 0.236 & 0.105 & 189,500 & 50.919 & 0.616 & 0.292 \\ \hline TTL-C & IndexF & 0.240 & 0.116 & 190,167 & 11.548 & 0.591 & 0.297 \\ \hline TTL-C & IndexLimit & 0.379 & 0.323 & 197,833 & 27.709 & 0.602 & 0.297 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._Therefore, in Table 2 we show that controlling some parameters in isolation, like the \(TTL\) and \(C\), does not help to achieve efficient results. On the other hand, the parameter \(H\) allows one--in most cases--to achieve a high level of consensus. When controlling \(H\) and ALG at the same time, we can reduce the average utilization of the ISP. Figure 8 shows the average consensus obtained with different dynamic optimization approaches and when no optimization is applied (None). The \(x\)-axis shows the name of the routing algorithm used at the beginning of the experiment. At the top, we show the results obtained when \(H=\) 30 at the beginning of the experiment, and at the bottom we show the results obtained when \(H=\) 50. The results show that the Baseline routing algorithm obtains a 100% consensus for the case without optimization when the initial value of \(H=\) 30, but with \(H=\) 50 it reports a low consensus close to 30%.