# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation
### Experimental Results
#### 7.3.2 Evaluation of the Mall Scenario
To do that, a multivariate analysis has been performed considering the number of nodes having goals \(N_{g}\), as well as the number of nodes with skills \(N_{s}\) in the scenario as independent variables, while the considered dependent variables were \(d_{\text{prob}}\), \(\theta\), \(\tau\), \(\gamma\), and \(\delta\). Thus, the idea is to know the impact of each independent variable on the selected dependent variables. As a summary, the set of variables used in the statistical analysis are defined and categorized in Table 3.  
Regarding the setting of the simulations to obtain the results to be statistically analyzed, we considered the small scenario with a value of \(\omega=3600\) s, i.e., the time period between two consecutive rounds of messages creation was one hour. This interval was selected due to the fact that it allows the nodes to move along the scenario and exploit their store-carry-and-forward capabilities while no other aspects can interfere with the algorithm's functioning, e.g., an increase in the amount of traffic flowing through the network. Several tests were performed by varying the number of nodes and the initial position of each of them in the scenario. In fact, a total of 25 independent runs were carried out for each test to retrieve statistically-significant outcomes. Figure 13: Average buffer time (\(\delta\)) vs. frequency of messages generation (\(\omega\)): mall. \begin{table}
\begin{tabular}{c c c} \hline
**Variable** & **Description** & **Type** \\ \hline \(N_{g}\) & Number of nodes having goals & Independent/continuous \\ \(N_{s}\) & Number of nodes having skills & Independent/continuous \\ \(d_{\text{prob}}\) & Average delivery probability & Dependent/continuous \\ \(\theta\) & Overhead ratio & Dependent/continuous \\ \(\tau\) & Average latency & Dependent/continuous \\ \(\gamma\) & Average number of hops & Dependent/continuous \\ \(\delta\) & Average buffer time & Dependent/continuous \\ \hline \end{tabular}
\end{table} 
Table 3: Criteria and variables used in the multivariate analysis. Table 4 shows the outcomes after performing the Ordinary Least Squares (OLS) regression considering the number of nodes with goals as independent variable, achieving an average value of \(R^{2}=0.876\). First, all the variables considered in the analysis were statistically significant (\(p<0.05\)) except \(d_{\text{prob}}\), for which no conclusions can be drawn (\(p=0.265\)). Looking at unstandardized coefficients (B column), it is clear that there existed a directly proportional relationship between the number of nodes with goals, \(N_{g}\), and the overhead ratio, \(\theta\), experienced in the network. In particular, an increase in one unit in the number of nodes with goals in the scenario was associated in a statistically-significant way with an increase in 1.719 in the overhead ratio. In the same way, an increase of 7.168 s on the average latency, \(\tau\), was paid as a penalty if a new node became part of the network. The number of hops, \(\gamma_{t}\) in turn, was not highly affected by the considered independent variable, with a slight increase per added node. However, it is interesting to highlight that the only variable with negative (but also statistically significant) coefficients was the average buffer time, \(\delta\), with \(B=-1.363\). This means that buffer time was reduced with the increase in the number of nodes with goals, following a decreasing progression of 1.363 s per new node. This situation can be explained as follows. Since there are more nodes in the network to share common interests with, the probability for a node to store a message temporarily that is intended for a different destination decreases. Therefore, the average buffer time in the network also decreases. Similar results emerge by inspecting Table 5, when the number of nodes with skills, \(N_{s}\), is taken as the independent reference variable.