## 7 Conclusions and Future Works  
This study presented an M/M/c/K queueing model to depict and assess a health monitoring scenario (ambient assisted living) for the elderly. IoT sensors and cloud/fog  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**\# Requests** & **\# Sample** & **MRT Mean (ms)–Experiment** & **MRT Mean (ms)–Model** & **Confidence Interval** & **Std. Deviation** & **\(p\)-Value** \\ \hline
3 & 30 & 111.5 & 112.1 & [104.8–118.1] & 122.1 & 0.676 \\
6 & 30 & 243.8 & 251.2 & [223.4–263.2] & 360 & 0.488 \\
9 & 30 & 439.3 & 445.4 & [416.5–462.2] & 415 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation Results.  
Figure 8: Practical experiment outline.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Configurations** & **Name** & **Functionality** \\ \hline Intel Core i3 1.7 Ghz and 8 GB & Request generator & Generate requests to be processed on remote servers \\ Intel Core i3 3.7 Ghz and 4 GB & Gateway & Receive requests and forward to router \\ Intel Core i3 3.7 Ghz and 4 GB & Router & Retransmit the data \\ Intel Core i3 3.7 Ghz and 8 GB & Cloud & Receive requests and process them \\ Intel Core i3 3.7 Ghz and 8 GB & Fog & Receive requests and process them \\ \hline \hline \end{tabular}
\end{table}
Table 3: Machines’ configuration.  
-distant resources were included in the analyzed design. The model may be used to calculate various measures, including mean reaction time, utilization level, drop rate, and the link between throughput and mean response time. The numerical analysis looked at four different possibilities. Three of them experimented with different fog/cloud resource capabilities, while one examined different routing tactics. We observed the association between the arrival rate and cloud/fog capacity fluctuation from various viewpoints thanks to the numerical analysis. Furthermore, according to the study, even if a fog was closer to the sensors, the cloud was more efficient for remote processing. The established results also show that the mean response time was highly dependent on message queuing in the components. Depending on the component's position in the system, the drop rate and utilization level may grow or decrease. The random and fixed probability strategies were the most efficient when it came to routing techniques. However, because the model has 21 parameters, it may be used for various additional analyses.  
The model will be extended in the future to investigate alternative methods of communication between the components. More components, such as more than one cloud, can be added (considering public, private, and hybrid). Furthermore, the nature of queues can be altered, with or without limits. We also want to look at the security implications of such a system.  
Conceptualization, L.R. and F.A.S.; Data curation, A.d.B.S.; Funding acquisition, L.R. and F.A.S.; Investigation, A.d.B.S.; Methodology, J.J.P.C.R.; Project administration, F.A.S.; Resources, J.J.P.C.R.; Software, L.R. and A.d.B.S.; Supervision, F.A.S.; Validation, L.R., J.J.P.C.R. and F.A.S.; Visualization, J.J.P.C.R. and A.d.B.S.; Writing--original draft, L.R.; Writing--review & editing, J.J.P.C.R., A.d.B.S. and F.A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable, the study does not report any data.  
This work is partially supported by FCT/MCTES through national funds and, when applicable co-funded EU funds under the Project UIDB/50008/2020; and by Brazilian National Council for Scientific and Technological Development - CNPq, via Grant No. 313036/2020-9.  
The authors declare no conflict of interest.