# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 5 Discussion--Open Issues
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Survey Paper** & **Year** & **Contributions** & **Methodology** & **Limitations—Open Issues** \\ \hline
[106] & 2019 & IEC in e-health applications &
\begin{tabular}{c} Lightweight security \\ mechanism based on trusted \\ nodes \\ \end{tabular} & Scalability \\ \hline
[107] & 2023 & IEC in e-health applications & \
\begin{tabular}{c} FL for data transfer reduction \\ D2D communications \\ \end{tabular} &
\begin{tabular}{c} Single cell wireless sensor \\ network scenario with one \\ MEC server \\ \end{tabular} \\ \hline
[108] & 2023 & IEC in e-health applications & \
\begin{tabular}{c} Blockchain and smart contacts \\ \end{tabular} &
\begin{tabular}{c} Efficient consensus algorithms \\ \end{tabular} \\ \hline
[109] & 2023 & \
\begin{tabular}{c} Energy efficiency in IEC \\ systems \\ \end{tabular} &
\begin{tabular}{c} Problem formulation, iterative \\ optimization and comparison to \\ baseline approaches \\ \end{tabular} & Energy prediction algorithms \\ \hline
[110] & 2023 & \begin{tabular}{c} Anomaly detection in IEC \\ systems \\ \end{tabular} & \
\begin{tabular}{c} Interpolation and marching \\ squares algorithm \\ \end{tabular} &
\begin{tabular}{c} Extension in additional real \\ world scenarios \\ \end{tabular} \\ \hline
[111] & 2019 & \begin{tabular}{c} Energy-efficient task \\ offloading \\ \end{tabular} & \
\begin{tabular}{c} Deep reinforcement learning in \\ an IEC smart city scenario \\ \end{tabular} &
\begin{tabular}{c} Extension in additional real \\ world scenarios \\ \end{tabular} \\ \hline
[112] & 2021 & Load forecasting in & \
\begin{tabular}{c} Edge servers in microgrids, \\ data collection, ML for load \\ forecasting \\ \end{tabular} &
\begin{tabular}{c} Federated learning, transfer \\ learning, insertion of historical \\ data via deep learning \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._be in a position to collect and aggregate a vast amount of heterogeneous data from diverse sources. Moreover, the developed ML models should be applicable to multiple scenarios in the IEC framework. * Although hardware and computational complexity along with latency reduction during task execution and efficient task offloading are the main KPIs under evaluation in an IEC ecosystem, the design and development of <<green>> networks are extremely important in the new era of 6G communications. In this context, the mass number of interconnected devices may have a severe impact on the energy footprint, which should be taken into account during IEC design as well. In addition, ML algorithms consume more energy compared to regular algorithms due to the increasing complexity of running and training models. Therefore, the design and implementation of less complex algorithms, while maintaining the accuracy rate, is also a key challenge towards green-based architectures. * While some recent ML research works address intelligent orchestration and control of different edge-cloud systems, there is still a lack of integrated AI solutions to optimize the edge-cloud continuum. Therefore, it is essential to integrate ML techniques at different layers of the architecture while dynamically optimizing the edge-cloud continuum software, data, and resource orchestration as a whole. A range of techniques such as federated learning for privacy, transfer learning for model reuse at the device, and different deep reinforcement learning architectures can be applicable to various optimization goals. * The vast majority of related works consider performance evaluation of the proposed approach per case in limited orientations (e.g., laboratory evaluation or a moderate number of participating nodes). Therefore, large-scale evaluation is important not only to examine scalability issues but also to identify potential limitations when full deployment takes place.