## 6 Conclusions and Future Work  
As modern systems are increasingly integrating sensors and actuators with built-in network connectivity, they are adopting architectures that distribute computing workloads to remote resources. The subsequent emergence of new paradigms such as the IoT and edge/fog computing motivates the implementation of advanced orchestration of these decentralized computing resources across heterogeneous environments, thus blending IoT, edge, fog, and cloud computing into a cohesive computing continuum [74]. The COGNIFOG framework aligns with this vision by integrating advanced cognitive functions into fog computing architectures. It aims to ensure the efficient operation of next-generation applications that require autonomous data processing and robust computing capabilities along the IoT-edge-cloud continuum. This paper presents the architecture and the key building blocks of the COGNIFOG framework, which is still under development as part of the three-year EU-funded research project in which it is embedded. Special emphasis is placed on the connectivity modules, which, due to their advanced level of development, have enabled the design of a PoC that validates the core functionality of BW management and paves the way for the concept of cognitive connectivity. A pilot version of the COGNIFOG framework has been implemented and evaluated in the context of an e-Health application, where it handles data acquisition from IoT devices and manages processing tasks across heterogeneous computing resources. Although this preliminary version only includes some building blocks, it is capable of not only deploying and managing all the necessary services for a typical IoT application across the available resources of the IoT-edge-cloud continuum but also providing improved performance in terms of connectivity, even in situations of network congestion or instability. Looking ahead, the short-term future work within COGNIFOG entails the integration of the remaining building blocks, significantly boosting the framework with advanced capabilities for real-time adaptability and security. Moreover, the framework will undergo further evaluations not only to verify its effectiveness in the proposed e-Health application with fully functional smartwatches but also to extend its applicability to two additional use cases in the areas of Smart Cities and Industry 4.0, broadening its impact and utility in dynamic real-world environments. But beyond the scope of the COGNIFOG framework, further challenges persist in the domain of the IoT-edge-cloud continuum. For instance, extending the presented orchestration/management capabilities to highly constrained hardware platforms is a significant \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**ID \#167590**} & \multicolumn{4}{c}{**ID \#167 DEC**} \\ \cline{3-8}  & & **Stage 1** & **Stage 2** & **Stage 3 *** & **Stage 1** & **Stage 2** & **Stage 3 * \\ \hline \multirow{4}{*}{**L (ms)**} & \(\overline{L}\) & 452.78 & 16,192.96 & 438.87 & 1167.00 & 17,061.18 & 1165.20 \\ \cline{2-8}  & \(L_{min}\) & 422.33 & 911.29 & 410.65 & 1117.33 & 1965.48 & 1118.77 \\ \cline{2-8}  & \(L_{max}\) & 740.25 & 27,719.03 & 853.83 & 1634.17 & 28,446.14 & 1790.57 \\ \cline{2-8}  & \(\sigma_{L}\) & 40.93 & 7594.71 & 47.63 & 87.27 & 7536.50 & 83.89 \\ \hline \multirow{4}{*}{**D (ms)**} & \(\overline{D}\) & 1998.00 & 2236.19 & 2000.01 & 2008.49 & 2246.74 & 1999.50 \\ \cline{2-8}  & \(D_{min}\) & 1664.95 & 513.48 & 1588.44 & 1698.59 & 703.07 & 1348.73 \\ \cline{2-8}  & \(D_{max}\) & 2277.76 & 3559.76 & 2439.89 & 2516.58 & 4259.41 & 2606.50 \\ \hline
**J (ms)** & 65.73 & 505.63 & 72.17 & 117.32 & 546.33 & 119.32 \\ \hline
**PDR (\%)** & 100\% & 89.6\% & 100\% & 100\% & 88.8\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metrics of interest of the performed test for the two analyzed IoT devices. (* Statistics from Stage 3 refer to its non-transient period: that is, from 12:06:30 to 12:10:00.)challenge.