# 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  
A small scenario is simulated by selecting a subset of nodes as devices with skills (screens), and the rest of them are people that go to the mall with the aim of buying items (i.e., their goal is to purchase). As in Section 7.3.1, our first objective is to analyze the performance of each algorithm in this scenario. In addition, since the routing protocols proposed in this paper take into account the similarity between the goals/skills of the encountered nodes in order to forward a message (creating the OCVN), we also evaluate the impact of increasing the number of nodes with goals and the number of nodes with skills on the network performance. The first network metric we aim to analyze is the average delivery probability. The resulting values of \(d_{\text{prob}}\) as a function of \(\omega\) are reported in Figure 12. Remarkably, SACAR-OCVN again outperformed the rest of the algorithms, especially for low values of \(\omega\). In fact, the best gains were obtained when messages were continuously generated and the network traffic was high (40% of gains when \(\omega=1\) s). Although SACAR-OCVN \(d_{\text{prob}}\) slightly decreased with \(\omega\), it was almost stable and always above 20%. On the contrary, the rest of the algorithms presented an increasing trend as a function of \(\omega\), but their outcomes were worse than the ones obtained when applying the concept of OCVN. Among them, GSaR must also be highlighted because it provided better results for \(\omega\leq 1800\). In fact, for \(\omega\) = 14,400, it presented even better results than SACAR-OCVN. In a big scenario such as a mall, it is necessary to evaluate the time a message is stored in the buffer of a node. Since there are many nodes in the network and the traffic can be significantly high, an efficient usage of nodes' storage capabilities to perform store-carry-and-forward actions must be performed. Therefore, a new metric is analyzed in this scenario, \(\delta\), which is the average time a message is stored in a buffer (since the obtained results for the rest of metrics considered in Section 7.3.1 presented similar conclusions in the mall scenario, we avoid to including them in this analysis, and we only focus on \(d_{\text{prob}}\) and \(\delta\)). Figure 13 reports the values of \(\delta\) as a function of \(\omega\). From the results, we can highlight that a division of the algorithms into two groups according to their behavior respecting \(\delta\) can be done. At first, there were four solutions (DDR, SACAR-HC, GSaR, and SWR) for which the average time a message spent inside a relaying node increased with \(\omega\), reaching prohibitive results when the time period between consecutive rounds of message generation was high. On the contrary, there was no  
Figure 12: Average delivery probability (\(d_{\text{prob}}\)) vs. frequency of message generation (\(\omega\)): mall. impact on \(\delta\) when \(\omega\) was varied for the rest of algorithms. If we focus on our best solution in terms of delivery probability, i.e., SACAR-OCVN (see Figure 12), we can state that, although the logic of the algorithm restricts the number of nodes that can be used for relaying a message (there must be a goals/skills matching), and therefore messages must spend longer times in buffers, \(\delta\) results reported in Figure 13 indicate that these times are comparable with solutions where such a constraint is not taken into account and where messages can be relayed by any node found along the path. In the last analysis, our aim is to evaluate what is the impact of increasing the number of nodes with goals and the number of nodes with skills on the different considered metrics when our SACAR-OCVN solution is applied.