## 6 Experiments
Devices have been initialized at the beginning of each test whereas requests have been generated at random instants, with uniform probability distribution within the simulation time. **Small-size networks. ** Figure 10 shows test results for scenarios with 10 nodes. For loosely connected networks (RND), like values are very low (below 0.45) in the first period, as reported in Figure 10b, due to the few friendship relationships (Figure 10a). For each request, a device starts a local covering process with few available services, so it is only partially satisfied through a minimal set of useful functionalities (Figure 10d) and a large amount of data (corresponding to the uncovered part of the request) is forwarded to friends (Figure 10f). Thanks to the _Satisfaction_ and _Suggestion_ indexes described in Section 4.3, _unsatisfied_ devices (e.g., isolated nodes) promptly look for useful friendships. In the second period, it is possible to notice both a considerable increase in the like value and a reduction in the data exchanged on the network. Having more friends, each device is able to locally satisfy a request in a better way and to forward a smaller uncovered part. A similar trend can be also identified for BA and DM network configurations; in these cases, however, the network starts with a higher degree of connectivity in the first period, generating higher like values--as shown in Figure 10c--which increase more slowly with reference to the previous scenario. Nevertheless, semantic-based experience factors allow satisfactory like values, reducing at the same time the network traffic. In fact, introducing more useful friendships and removing useless ones allows social things to select the most suitable services at each hop of the cooperative covering process, converging faster to the final result. Moreover, the request rate mainly affects the behavior of social networks with few connections: device relationships are modified more frequently, leading to lower like values and less efficient cooperative covering. Few messages are forwarded to friends, as reported in Figure 10e, due to the more variable social network configuration. On the \begin{table}
\begin{tabular}{c c l} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \multirow{4}{*}{Generation algorithm} & \multirow{2}{*}{RND} & _Randomly_ generated nodes with mean number of friends set to 1. Loosely connected network with frequent isolated objects. \\ \cline{3-3}  & & Nodes generated exploiting the preferential attachment rule of the _Barabasi–Albert_ model [51]. Each node has 2 friends on average. Hierarchical topology similar to a sensor network with several edge nodes and one or more sinks. \\ \cline{2-3}  & \multirow{2}{*}{DM} & Nodes generated using the _Dorogottsev–Mendes_ algorithm [52]. Strongly connected network with nodes having 4 friends on average. \\ \hline \multirow{4}{*}{Nodes} & 10 & Small-size network. \\ \cline{2-3}  & 100 & Medium-size network. \\ \cline{1-1} \cline{2-3}  & 1000 & Large-size network. \\ \hline \multirow{4}{*}{Request rate} & 20 & 20\% of nodes identifies a new event and posts a message in each period. _Few_ device requests. \\ \cline{1-1} \cline{2-3}  & 70 & 70\% of nodes identifies a new event and posts a message in each period. _Frequent_ device requests. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network configuration parameters. contrary, social devices are able to establish stable relationships after about 4 periods for all other configurations. **Medium-size networks. ** Also in scenarios with 100 nodes (results detailed in Figure 11) loosely connected networks quickly take advantage of dynamic relationship adaptivity. Compared with small networks, mean like values are increased (Figure 11b) by virtue of a wider choice of possible friends to exploit as service providers. This also implies decreased transmitted data (Figure 11f), since the distributed discovery converges in fewer hops.