# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 1 Introduction
### Related Works  
In this subsection, indicative recent survey papers are presented in the context of IEC systems and related fields. In [25], for example, the authors present all recent advances on edge computing-driven IoT (ECDriven IoT), which have been summarized in six main aspects: architecture of edge computing-driven IoT, operating systems, communication protocols, computing, security and privacy, as well as use cases and applications. In [26], the most important security and privacy approaches in the context of edge computing (EC)-based IoT systems are discussed. In particular, these include user-centric, device-centric, and end-to-end (E2E) security. Apart from the architectural approach per case,  
Figure 2: Survey structure.  
additional issues such as firewalls, intrusion detection systems, authentication and authorization mechanisms, and privacy-preserving designs are discussed as well. Moving a step forward, the work in [27] discusses the major classifications of attacks in IEC continuum systems and also provides possible solutions and countermeasures along with the related research efforts.  
In [28], a survey of edge artificial intelligence (AI) is provided, where AI algorithms and models are deployed on edge devices. In this context, due to the resource-constraint nature of the edge devices, various technical challenges are presented and analyzed. In [29], a comprehensive survey on mobile edge computing nodes (ECNs) is presented along with related challenges and limitations. In particular, mobile ECNs are classified into four major categories, namely, aerial, ground vehicular, spatial, and maritime nodes. For each category, the different types of nodes are introduced, along with transmission and mobility limitations. A key outcome of this work is the need for an integrated architecture that takes into consideration all the aforementioned different types of nodes and diverse technical characteristics. The work in [30] is mainly focused on distributed ML approaches in the IEC continuum. In this context, the main libraries and frameworks for ML and deep learning (DL) inference, centralized training, and distributed training with a focus on the edge and cloud are presented and analyzed, along with limitations and open issues. In [31], in the context of integrating edge computing with emerging technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) to support Internet of Energy (IoE) applications, the authors present an up-to-date survey of edge computing research. The research in [32] is focused on service orchestration and resource management for edge computing, including task offloading, content caching, and virtual network embedding (VNE). Finally, in [33], all recent technological advances in edge computing, especially from the perspective of architectures and models, key technologies, and directions, are presented and discussed.  
The aforementioned indicative surveys are also summarized in Table 1, where the main contributions per case are highlighted. In the same context, the key contributions of our work are mentioned as well, which will be analyzed in the following subsection.