# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 4 Experimental Evaluation
### Scenario 2--Evaluation of the Proactive Strategy

Table 7: Comparison of the clustering results, considering the average delay (secs) of 1 MB file transfer using NDN-Chunks application. measure for each cluster: (i) the number of paths; (ii) the percentage of reactive's solution path choices per cluster out of the 15,000 interest-data exchanges; (iii) the average delay of the Interest-Data exchange (msec) carried out on the paths belonging to each cluster; (iv) the percentage of failed Interest-Data exchanges, and (v) the average delay of the file transfer, considering the chunk application. Results indicate that the proposed clustering solutions efficiently categorize the paths, i.e., the average values of delays (for both applications) and fails increase according to the clustering characterization. Based on the analysis of the scenario's 2 results, we can synthesize our conclusions in the following two points:  
* The usage of dynamic protocols over relatively stable wireless mesh networks is not always the best solution in terms of delay, i.e., the reactive solution, which is based on BATMAN routing protocol, does not always select the \"best\" NDN path. * Clustering is an efficient method for determining the \"best\" NDN paths over a stable WMN, considering both performance and reliability, for example, a clustering technique incorporating both RSSI and delay metrics may successfully locate the paths with low delay and interest data exchange failures. Concluding this Section, in Table 11, we present the major advantages/disadvantages of both approaches, derived from the experimental analysis. More precisely, the overall results provided a piece of strong evidence that there is not a one-fits-all approach for the establishment of the appropriate NDN path over WMNs. More importantly, efficient rooting strategies should consider the communication conditions of the network environment under consideration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RSSI-** & **No. of** & **Reactive Solution’s** & **Interest-Data** & **Fails (\%)** & **NDN-Chunks** \\
**Delay** & **Paths** & **Path Choices (\%)** & **Delay (msec)** & **Fails (\%)** & **Delay (secs)** \\ \hline C.1 & 2 & 5.29 & 19.33 & 2.84 & 13.02 \\ \hline C.2 & 11 & 14.66 & 30.05 & 8.62 & 13.18 \\ \hline C.3 & 7 & 77.99 & 72.91 & 13.11 & 23.53 \\ \hline C.4 & 1 & 0.19 & 144 & 21.13 & 31.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: RSSI-Delay clusters average performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RSSI** & **No. of** & **Reactive Solution’s** & **Interest-Data** & **Fails (\%)** & **NDN-Chunks** \\
**Paths** & **Path Choices (\%)** & **Delay (msec)** & **Fails (\%)** & **Delay (secs)** \\ \hline C.1 & 1 & 0.03 & 23.16 & 2.33 & 13.82 \\ \hline C.2 & 7 & 11.11 & 30.28 & 5.76 & 13.11 \\ \hline C.3 & 13 & 86.80 & 45.59 & 11.11 & 18.10 \\ \hline C.4 & 1 & 0.19 & 144.00 & 21.13 & 31.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: RSSI clusters average performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Delay** & **No. of** & **Reactive Solution’s** & **Interest-Data** & **Fails (\%)** & **NDN-Chunks** \\
**Paths** & **Path Choices (\%)** & **Delay (msec)** & **Fails (\%)** & **Delay (secs)** \\ \hline C.1 & 12 & 18.66 & 23.79 & 7.13 & 12.98 \\ \hline C.2 & 3 & 26.34 & 52.66 & 8.02 & 17.88 \\ \hline C.3 & 6 & 52.94 & 64.06 & 12.92 & 21.92 \\ \hline C.4 & 1 & 0.19 & 144.00 & 21.1 & 31.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Delay clusters average performance.