# Building Topic-Driven Virtual IoTs in a Multiple IoTs Scenario
## 6 Experiments
### Our Approaches' Capability of Improving the Efficiency of Information Dissemination
Finally, we computed \(g_{st}=\frac{g_{st}^{\mathcal{V}}}{g_{st}^{\mathcal{V}}}\). Roughly speaking, it denotes how much the presence of the virtual IoT \(\mathcal{V}\) can contribute to require information dissemination tasks only to nodes possibly benefiting of it. A value of this coefficient higher than 1 denotes a positive contribution of \(\mathcal{V}\); the higher this value the higher the contribution. As in the previous experiment, we computed the average \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**MIoT (Size)**} & \multicolumn{6}{c}{**Average \(f_{st}\)**} \\ \cline{2-7}  & \(|\mathbf{Q}|=\mathbf{1}\) & \(|\mathbf{Q}|=\mathbf{2}\) & \(|\mathbf{Q}|=\mathbf{4}\) & \(|\mathbf{Q}|=\mathbf{6}\) & \(|\mathbf{Q}|=\mathbf{8}\) & \(|\mathbf{Q}|=\mathbf{10}\) \\ \hline \(\mathcal{M}_{1}\) (176) & 0.144 & 0.220 & 0.290 & 0.304 & 0.336 & 0.347 \\ \(\mathcal{M}_{2}\) (301) & 0.126 & 0.170 & 0.177 & 0.175 & 0.178 & 0.179 \\ \(\mathcal{M}_{3}\) (485) & 0.104 & 0.112 & 0.074 & 0.052 & 0.041 & 0.037 \\ \(\mathcal{M}_{4}\) (778) & 0.057 & 0.051 & 0.028 & 0.038 & 0.047 & 0.049 \\ \(\mathcal{M}_{5}\) (946) & 0.048 & 0.034 & 0.022 & 0.028 & 0.032 & 0.024 \\ \(\mathcal{M}_{6}\) (1256) & 0.031 & 0.015 & 0.017 & 0.011 & 0.007 & 0.007 \\ \(\mathcal{M}_{7}\) (1725) & 0.026 & 0.014 & 0.011 & 0.010 & 0.008 & 0.008 \\ \(\mathcal{M}_{8}\) (2028) & 0.016 & 0.010 & 0.009 & 0.009 & 0.009 & 0.009 \\ \(\mathcal{M}_{9}\) (3544) & 0.012 & 0.009 & 0.009 & 0.009 & 0.009 & 0.009 \\ \(\mathcal{M}_{10}\) (5024) & 0.011 & 0.008 & 0.007 & 0.007 & 0.007 & 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average values of \(f_{st}\) against the size of MIoTs and queries used to generate the virtual IoTs (supervised approach). \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**MIoT (Size)**} & **Average \(f_{st}\)** \\ \hline \(\mathcal{M}_{1}\)(176) & 0.904 \\ \(\mathcal{M}_{2}\)(301) & 0.722 \\ \(\mathcal{M}_{3}\)(485) & 0.635 \\ \(\mathcal{M}_{4}\)(778) & 0.584 \\ \(\mathcal{M}_{5}\)(946) & 0.580 \\ \(\mathcal{M}_{6}\)(1256) & 0.576 \\ \(\mathcal{M}_{7}\)(1725) & 0.516 \\ \(\mathcal{M}_{8}\)(2028) & 0.477 \\ \(\mathcal{M}_{9}\)(3544) & 0.452 \\ \(\mathcal{M}_{10}\)(5024) & 0.426 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average values of \(f_{st}\) against the size of MIoTs (unsupervised approach). values of \(g_{st}\) by operating on the ten MIoTs introduced in Section 6.2 and by performing the same tasks described therein for both the supervised and the unsupervised approaches. The obtained results are reported in Tables 12 and 13. The analysis of these tables is a further confirmation of the efficiency of our approach. Indeed, thanks to the presence of virtual IoTs, the fraction of nodes participating to the spreading of information that can also benefit from this task increases remarkably. The results of Tables 10 and 11, along with the ones of Tables 12 and 13, agree to evidence that the discovery of virtual IoTs is highly beneficial in terms of efficiency for the information dissemination task in a MIoT. In this case, the contribution of \(\mathcal{V}\) in increasing the efficiency of the spreading task, by limiting it mainly to nodes that could benefit from the information they are disseminating, is very high for the supervised approach when \(|Q|=1\) or \(|Q|=2\). When \(|Q|\) increases, this contribution decreases, even if it remains still significant. As for the unsupervised approach, the contribution of \(\mathcal{V}\) can be always observed even if it is less evident than the one characterizing the supervised approach.