# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 1 Introduction
* The continuum needs to be effectively managed to optimally meet the application demands during service execution, taking into account multiple constraints, such as the location of the involved nodes (edge or IoT), their transmission and processing capabilities, as well as their energy footprint. Optimum resource allocation in multi-node heterogeneous environments might lead to highly non-convex problems. In this context, machine learning (ML) algorithms have emerged as a promising approach that can solve various optimization problems providing near-optimal solutions [14; 15]. In traditional centralized ML approaches, all collected data are sent to a high-performance computing node for proper model training and inference. However, on one hand, the collection of heterogeneous data from all involved nodes of the continuum might increase the pre-processing load, and on the other hand, centralized ML training might jeopardize latency requirements in critical applications. Therefore, as will also be described in Section 2, the support of distributed and decentralized ML approaches is a key concept in IEC systems [16; 17; 18]. * Due to the distributed and dynamic nature of the continuum, with plenty of devices from different owners and provenance, the application of reliability and trustiness becomes fundamentally challenging. Secure mechanisms for accessing the distributed nodes, preserving data privacy, and providing open and transparent operation are fundamental to enhancing trustworthiness [19; 20]. * As it was previously mentioned, the continuum puts together a broad and diverse space with multiple heterogeneous devices and protocols. Although there are several standards, open-source projects, and foundations that focus on global communication and management protocols, the envisioned continuum must also consider that some constrained devices will not support any specific tool. Therefore, contributing to an open ecosystem favors interoperability with existing and emerging frameworks, which is a key challenge for next-generation broadband wireless networks [21]. Hence, as it becomes apparent from the above, the optimum design of an IEC infrastructure should address various technical challenges, such as: (i) distributed data management; (ii) continuum infrastructure virtualization and diverse network connectivity; (iii) optimized and scalable service execution and performance; (iv) guaranteed trust, security, and privacy; (v) reliability and trustworthiness; and (vi) support of scalability, extensibility, and adoption of open-source frameworks [22]. These challenges are also depicted in Figure 1. The envisioned effects of edge computing in a wide range of potential use cases, from smart environmental monitoring to future fifth-generation (5G) advanced applications (such as e-health, autonomous driving, smart manufacturing, etc. ), have fueled several initiatives aimed at addressing the different challenges posed by the full deployment of an IEC continuum. These challenges become even more important as the discussions on sixth-generation (6G) networks have already started taking place [23; 24]. Therefore, the goal of this survey paper is to analyze all recent technological developments in the field of IEC continuum systems. Emphasis will be given on the addressed challenges per case, according to the previous description. In the same context, open issues  
Figure 1: Challenges in the IoT-edge-cloud continuum. and limitations will be identified as well. Moreover, potential deployment scenarios based on IEC continuum systems will be also presented. The rest of this survey paper is organized as follows: In Section 1.1 of Section 1, indicative recent survey papers are presented, while the main contributions of our work are highlighted in Section 1.2. In Section 2, the most important supporting technologies in IEC systems are presented.