# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 4 Recent Works in IoT-Edge-Cloud Continuum Architectures
To this end, potential nodes that can be used in the orientation are acceptable only in the case that can verify their trustiness. In this case, they render some services to the network, without fully being part of it. Their full admittance is based on the bootstrapping factor value. Therefore, attack attempts can be minimized. In [107], FL is used in order to reduce the amount of data sent to MEC servers for the processing and training of ML models. In this case, considering wearable devices on end users, local models can be derived based on the corresponding data sets. After the local FL training is completed, the e-health wearable device sends the corresponding local FL system model results to the MEC node, which aggregates the local FL system model (i.e., the global FL model) and broadcasts the updates to all e-health wearable users. In this context, D2D communications are also exploited. In [108], blockchain technology with smart contracts has been deployed to leverage data integrity and transaction fairness in e-health applications. According to the presented results, the proposed approach can resist adaptive chosen keyword attacks. In [109], the problem of energy efficiency has been investigated in IEC orientations. In this context, the mathematical formulation leads to a convex optimization problem that has been solved with the help of a proposed iterative technique. To this end, the proposed approach outperforms other baseline technologies according to the resource allocation policies that have been considered, such as an equal computational load to all involved servers, a popularity-based and workload-aware assignment, as well as a communication-based assignment. In [110], the authors deal with anomaly detection in the IEC continuum. In particular, the inverse distance weighted algorithm and marching squares algorithm are adopted to generate the boundary of an anomaly in terms of isopleths. Afterwards, an appropriate filtering mechanism is employed at the edge networks, and related data are transmitted to the cloud for further analysis only if necessary. The performance of the proposed approach was evaluated with the help of a sensor telemetry data set, and the results showed that it can outperform other benchmark approaches. In [111], an IoT energy management system has been designed for smart city environments, leveraging DRL for energy-efficient calculations. In this context, two distinct approaches are evaluated: In the first case, IoT devices offload energy scheduling tasks to an edge server. In the second case, the edge server offloads NN calculations in the cloud domain to reduce overall computational times. Results were presented for specific communities with smart homes and edge servers. According to the presented results, the proposed schemes can achieve low energy costs while causing lower delay, compared to traditional schemes. Finally, in [112], an IEC architecture is proposed and evaluated that integrates ML algorithms for optimum energy management in microgrids that employ distributed energy sources. In this context, edge devices are located in the boundaries of microgrids and collect various types of energy management data. Edge devices send telemetry data to a cloud-based IoT platform that is responsible for data monitoring, visualization, storage, and sharing for future planning purposes. The implementation is based on open-source software, and performance evaluation results address scalability for hundreds of prosumers. In particular, two forecasting algorithms were considered. As part of their future work, the authors have identified, among others, the employment of FL approaches, as well as the insertion of historical data in the considered algorithms via deep learning.