# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 4 Recent Works in IoT-Edge-Cloud Continuum Architectures
In [93], an architectural approach is presented for the IEC continuum able to be federated so as to support cross-domain services that use different cloud-IoT resources. In this context, various aspects are taken into consideration, such as content virtualization and cognitive management of services. In [94], optimum application deployment takes into consideration both required latency as well as overall power consumption. To this end, simulations were conducted with the help of the iFogSim [95] simulator, which demonstrates that application service quality is significantly improved and system power consumption is greatly reduced when compared with other baseline approaches. In [96], the authors propose a new Internet of Things Edge-Cloud Federation (IoTEF) architecture for multi-cluster IoT applications. This new architecture has four layers: (i) application isolation; (ii) data transport; (iii) distributed operating system (OS); and (iv) unified federated management layer. This approach provides a common framework for data management to both edge and cloud, reduced latency, since data processing is closer to the edge, as well as a unified federated management approach for managing several clusters from a single management interface. In the same context, experimental results were provided as well, taking into consideration a smart building scenario. In [97], quality of service (QoS) is considered to be the main performance metric to solve the problem of optimum cluster usage in edge-cloud environments. In [98], a model-based approach to automatically assign multiple software deployment plans to hundreds of edge gateways and connected IoT devices implemented in collaboration with a smart healthcare application provider is described. In [99], a precision agriculture (PA) case that covers extreme PA requirements by using automation, IoT technologies, and edge and cloud computing through virtualization is dealt with. In this context, a three-layered architectural approach has been considered, where IoT devices are located in greenhouse facilities and tasks can be offloaded to virtualized edge servers, while the cloud infrastructure deals with non-latency-critical high complexity calculations. In [100], an edge-fog-cloud architectural approach for IoT-based smart agricultural applications is introduced. In this context, an optimization problem is formulated using mixed-integer linear programming aiming to improve various KPIs, such as energy consumption, CO\({}_{2}\) emission, and network traffic. In [101], an architectural approach to edge computing in IoT-based manufacturing applications is described. In this context, the role of edge computing is analyzed from four perspectives, in particular the following: edge equipment, network communication, information fusion, and cooperation with the cloud. In the same context, the architectural approach of [102] leverages edge-fog-cloud cooperation in a smart manufacturing environment. In [103], the architecture of an IoT big data ecosystem is presented and analyzed, based on a three-layer approach: edge layer, cloud layer, and application layer. In this context, an efficient task execution is carried out between the cloud and the edge layer, in the context of predictive maintenance. In [104] edge computing is combined with blockchain technology in a smart manufacturing environment. In [105], a hybrid task offloading model is introduced, including the collaboration of cloud computing and MEC, in the context of smart cities. To this end, a distributed deep learning-driven task offloading algorithm is proposed to generate near-optimal offloading decisions over mobile devices, edge-cloud servers, and central cloud servers. According to the presented results, the proposed approach can significantly reduce the overall computational burden, when compared with other offloading schemes. In [106], a lightweight mechanism for security provision in IoT-based e-health applications is proposed and evaluated.