## 6 Experiments
In small-world (BA) and strongly connected (DM) network scenarios, mean like values (Figure 11c) improve their performance over the periods, while results on exchanged data are very similar to small networks. The presence of many devices facilitates the discovery of additional useful friends, as reported in Figure 11a. As a consequence, higher like scores have been obtained: all network parameters tend to steady-state values in about three (friendships and exchanged data) to five (activated services and forwarded messages) periods. Figure 10: Test results for small-size networks. Legend denotes values of parameters for each configuration (\(<\)generation algorithm\(>\)\(\_\)number of nodes\(>\)\(\_\)\(<\)request rate\(>\)). **Large-size networks. ** Finally, results for scenarios with 1000 nodes are reported in Figure 12. Large networks follow similar trends highlighted for small and medium configurations. Main differences compared to medium scenarios concern: (i) fewer friends per device (Figure 12a), as friendships are removed more frequently in large networks, so the number of friends grows more slowly; (ii) fewer activated services per request (Figure 12d) because, when nodes reach the steady-state configuration, the covering process is able to select few services which largely satisfy a request, rather than many services each covering a small part. Thanks to these optimized relationships, mean like values in large loosely connected (RND) networks (Figure 12b) increase with reference to small and medium ones; on the other hand, for BA and DM network models the is not a significant difference in like values between large (Figure 12c and medium networks, meaning in the latter case, devices are already able to find a sufficient number of friends to satisfy their requests. Moreover, devices save storage space (activated functionalities are tagged and  
Figure 11: Test results for medium-size networks. Legend denotes values of parameters for each configuration (\(<\)generation algorithm\(>\)\(\_\)\(<\)number of nodes\(>\)\(\_\)\(<\)request rate\(>\)). saved on the device wall) and keep the network overhead required to contact and activate remote services as low (Figure 12f) as in small (Figure 10f) and medium (Figure 11f) ones. This is a clear benefit of the approach, envisioning good scalability: even on large networks, nodes establish a balanced number of high-quality friends and share their resources without overloading the network. The same test suite has been exploited to compare results of the proposed dynamic approach with the static SloT relationship management model in [9]. A direct experimental comparison has been possible only with that framework, as the other state-of-the-art works reported in Section 3 have not published their code. Tests have been run using the same initial configurations, comparing the steady-state results after \(N=10\) time periods. The framework in [9] does not allow for dynamic relationship configuration; therefore, performance critically depends on the initial overlay network topology. Figure 13 highlights  
Figure 12: Test results for large-size networks. Legend denotes values of parameters for each configuration (\(<\)generation algorithm\(>_{-}\)<number of nodes\(>_{-}\)<request rate\(>\)). the deltas related to the different parameters for the 18 network configurations tested in the evaluation. The main discussion remarks are as follows:  
* Medium-size networks exhibit higher variations in the average number of friendships per node (Figure 13a). With 100 nodes, the network is large enough to facilitate multiple connections without becoming overwhelmingly complex. Medium-sized networks often see the formation of dynamic tightly knit communities or clusters, which naturally enhances the number of friendships as nodes within these clusters are more likely to be interconnected.