## 7 Conclusions  
In this work, a security system was proposed for deployment in IoMTs to ensure the confidentiality of patient information and verify the originality of the bio-signal recordings included in the EPF. First, our system was developed using [PERSON]'s discrete orthogonal moments, 2 DSCLM, and QR code. Then, our scheme was implemented on the Raspberry Pi 4 model B hardware of low cost and low power consumption. A parallel implementation of the feature vector extraction and QR code encryption phases was also presented with the proposed system to fully exploit the available resources of Raspberry Pi hardware. This implementation considerably reduced the total execution time of our scheme compared to its sequential implementation by an ETIR \(>\) 65%. The achieved simulation and comparison outcomes showed, on the one hand, the high-security level of our scheme and its good robustness against different types of attacks, and on the other hand, its fast execution on the Raspberry Pi board. The latter guarantees the power efficiency of our system in IoMTs. In the future, we will improve our scheme so that it can be applied to various types of biomedical multimedia, such as 2D and 3D medical images, and we will exploit the parallel computing on a cluster of Raspberry Pi boards to provide a fast execution time of our scheme when it is applied to large databases.  
Conceptualization, A.D. and M.Y.; methodology, A.D. and M.Y.; software, A.D. and M.Y.; validation, A.D, M.Y., M.S. and H.Q.; formal analysis, M.S., O.J., W.E.-S. and H.Q.; investigation, M.S. and H.Q.; resources, A.D., W.E.-S. and N.F.S.; data curation, A.D. and M.Y.; writing, A.D., M.Y., M.S. and H.Q.; original draft preparation, A.D., M.Y., H.K. and S.M.; writing--review and editing, A.D., M.Y., W.E.-S., H.K., S.M., M.H.A., A.D.A., M.S. and H.Q.; visualization, S.M., O.J., A.D.A., M.H.A. and S.M.; supervision, M.S. and H.Q.; project administration, M.S.; funding acquisition, W.E.-S., A.D. and N.F.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors would like to acknowledge the Princess Nourah bint Abdulrahman University Re-searchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Length of the** **Feature Vector**} & \multirow{2}{*}{**QR Code Size**} & **Total Execution** & **Total Execution** \\  & & **Time in (s) by** & **Time in (s) by** \\  & & **the Sequential** & **the Parallel** \\  & & **Method** & **Method** \\ \hline L = 256 & 260 \(\times\) 260 & 71.5690 & 24.1107 & 66.3258 \\ \hline L = 1024 & 564 \(\times\) 564 & 150.0288 & 49.3229 & 67.1244 \\ \hline L = 4096 & 1036 \(\times\) 1036 & 241.0704 & 82.0035 & 65.9836 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison in terms of the average execution time of the proposed scheme using the sequential and parallel methods for feature vector extraction and encryption.