# > a blockchain scanner notifies the client about the results
## 5 Experimental Results  
In this section, we will describe the different experimental results obtained following the implementation of our application. Figure 8 presents the memory consumption evolution while changing the number of deployed nodes: a quasi-linear behavior is thus brought to light. Yet, the experiments also brought to light that the maximum number of nodes to be deployed on a Raspberry Pi 3 is 9 (an error message is obtained when trying to deploy the 10 th node). Note that this limitation is not derived from the memory: actually, after deploying the 9 th node, only about 25% from the total memory is required (241.2 M out of 1 G). The values reported in Figure 8 are obtained as average values over 10 experiments; for each reported value, the 95% confidence limits are computed with a relative error lower than 5%. Figure 8: Memory consumption depends on nodes number. Figure 7: Attacked images sampled from database. These results demonstrate that the main limitation derives from the reduced computing resources: for more than 9 nodes, the maximal time imposed by the blockchain for performing basic operations cannot be met on a Raspberry Pi 3. The Raspberry Pi stops as illustrated in Figure 9.  
In the sequel, we deployed 5 Tezos nodes, so as to reach a trade-off between functional and non-functional needs. Table 1 presents the execution time of the fingerprinting system (fingerprint extraction, matching algorithm, eventually the adding of new fingerprinting to the database) on a computer and on a Raspberry Pi, as a function of the visual document database size. By analyzing the results presented in Table 1, firstly, it can be noticed that the Raspberry Pi cannot process databases larger than 1000 documents (with 5 Tezos nodes running). When the database contains only one document, the execution time on Raspberry Pi is 37.7 times larger than the one on the PC. However, for databases containing between 10 and 1000 documents, the ratios of the Raspberry Pi execution time to the PC execution time vary between 7.5 and 9.8, with an average value of 9.13. For the two processing structures, these values also show the different weights of the initialization operations in the overall processing time. The values reported in Table 1 are obtained as average values over 10 experiments. The underlying 95% confidence limits can be computed with relative errors lower than 10% for the case of a single visual document in the database and with relative errors lower than 2.5% for the rest of the cases. This difference can be explained by the fact that in the case of \begin{table}
\begin{tabular}{c c c} \hline \hline  & **PC** & **Raspberry Pi 3** \\ \hline
1 & 7 ms & 264 ms \\
10 & 42.2 ms & 320 ms \\
50 & 69.1 ms & 637 ms \\
100 & 103 ms & 960 ms \\
500 & 375 ms & 3.65 s \\
1000 & 704 ms & 6.92 s \\
5000 & 3.41 s & — \\
10,000 & 6.78 s & — \\
11,557 & 7.77 s & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall execution time. Figure 9: System crashes with more than nine nodes. a single document in the visual database, the random effect of the initialization conditions is higher than in the other cases. Table 2 presents the execution time of Tezos Blockchain operations (Bake, Coin transfert, Typecheck, and Smart contract deployment) when considering 5 Tezos nodes running, on a PC and on a Raspberry Pi, respectively. The values reported in Table 2 are obtained as an average over 10 successive experiments; their corresponding 95% confidence limits can be computed with relative errors lower than 2.5%. For the four types of operations, the ratios of the Raspberry Pi execution time to the PC execution time is 2.23, 3.63, 6.27 and 3.95, respectively. It can be noticed that the largest ratio corresponds to the execution of the Typecheck operation: this result can be explained by the inner complexity of such a command related to the formal verification of the Smart contract.