# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation
### Description of The Scenarios
#### 7.1.2 Mall  
In the following, we move our attention to the definition of a bigger scenario based on a mall (see Table 2 for the specification). In this second scenario, personalized information is shown to the buyers depending on their preferences, needs, previous purchases, etc. That information is shown by different screens installed at the mall's information point and throughout the shopping center. The exchange of contextual information between people and screens allows the shops to show personalized offers to the buyers and also the mall manager to get information such as the behavior of the customers, their profiles, and preferences. This case study aims at improving the buyers' satisfaction by showing them a set of personalized offers, while at the same time, the visibility of those offers is also increased. By exploiting this scenario, our aim is to evaluate the behavior of the SACAR algorithms in environments with a large number of users who do not follow a clear movement pattern. In particular, the different components of this scenario are shown in Figure 7 and described next:  
* Screens located throughout the mall with the skill of showing buyers different information depending on their needs, such as specific shops, products, and offers in which they may be interested.
* Buyers who have virtual profiles with different goals depending on their needs, preferences, past purchases, etc.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(A\) & 150 (m\({}^{2}\)) \\ \(N\) & 11 \\ \(N_{s}\) & 7 \\ \(N_{g}\) & 4 \\ \(S\) & [Temperature, Illuminance, ProductType] \\ \(G\) & [Temperature, Illuminance, ProductType] \\ \(s_{n}\) & 1 \\ \(g_{n}\) & 3 \\ \(P\) & (StationaryMovement for \(N_{s}\), MapReduceMovement for \(N_{g}\) \\ \(P\) & with working time periods in the range of \(t=[1800,7200]\) s) \\ \(T\) & 28,000 (s) \\ \(\omega\) & (1, 30, 60, 90, 1800, 3600, 14,400) (s) \\ \hline \end{tabular}
\end{table}
Table 1: Parameter setting for the smart office scenario.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(A\) & 225,000 (m\({}^{2}\)) \\ \(N\) & 41 \\ \(N_{s}\) & 3 \\ \(N_{g}\) & 38 \\ \(S\) & (Purchases) \\ \(G\) & (Purchases) \\ \(s_{n}\) & 1 \\ \(g_{n}\) & 1 \\ \(P\) & (StationaryMovement for \(N_{s}\), MapReduceMovement for \(N_{g}\) \\ \(P\) & with stationary time periods in the range of \(t=[300,500]\) s) \\ \(T\) & 28000 (s) \\ \(\omega\) & (1, 30, 60, 90, 1800, 3600, 14,400) (s) \\ \hline \end{tabular}
\end{table}
Table 2: Parameter setting for the mall scenario.  
Figure 7 shows a draft of the simulated environment and some example mobility patterns. As can be seen, two mobility flows have been considered (green and orange lines). In the figure, only two routes have been drawn in order to increase the readability of the picture. Nevertheless, for the final simulated scenario, eight different routes were evaluated. These mobility patterns have been assigned to the buyers depending on their preferences and goals, since usually people with the same preferences visit similar types of shops. Similarly, several screens have been placed at different locations inside the mall. Nevertheless, for this scenario, we also evaluated the impact of increasing or decreasing the number of nodes with skills over the delivery probability.  
The main objective of using this scenario is to evaluate the performance of the proposed solutions in an environment with a high number of nodes that can move following different mobility patterns. Moreover, we can also analyze the impact of an increase in the number of buyers (nodes with goals) on the algorithms' performance, e.g., simulating that the mall is crowded. Moreover, a trade-off analysis on the minimum number of screens (nodes with skills) that are required to reach most of the buyers can also be performed.