# 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 this context, performance evaluation in a mobile robot environment has taken place, where a proof-of-concept system based on Ethereum and KubeEdge was designed. Results demonstrated that more flexible IoT devices can be supported, while at the same time software development is improved as well. In [82], an architectural approach is proposed and evaluated, which combines IoT, cloud, and edge computing for failure analysis and prediction. According to the presented results, the proposed model can reduce the number of failed tasks for cloud-IoT applications. In [83], a low-complexity and secure task offloading algorithm was implemented and evaluated in IEC environments. Results indicate that the proposed approach can provide a significant reduction in the overall execution times compared to other baseline approaches. In [84], the IoT microservice deployment problem is investigated in heterogeneous edge-cloud environments. In this context, microservices leverage programming flexibility, as each application can be deployed as a collection of loosely coupled services [85]. Optimum microservice deployment should take into consideration multiple applications whose execution is based on highly heterogeneous infrastructures. To this end, a deep reinforcement learning (DRL) methodology was presented in [84] that was compared with a random and a genetic algorithm. According to the presented results, the proposed approach has improved performance compared to the other benchmark approaches even when scaling up the requests. In [86], a deep Q-learning (DQL) framework is proposed and evaluated for efficient task offloading from IoT devices to either edge or cloud servers. To this end, blockchain is used during task offloading to ensure security and privacy protection. A key novelty of this work is that it considers the energy status of each device during offloading calculations,while at the same time, an energy harvesting process is adopted per device. In [87], an IEC computing continuum solution is investigated in the context of SAR operations. These scenarios can be highly demanding both in terms of throughput and latency since they involve multiple and diverse operations, such as dynamic multi-robot mapping and fleet management, computer vision for feature extraction, data processing, device management, orchestration of software components, as well as low latency communications. The proposed approach, based on the NEPHELE project [88], leverages the virtualization of IoT devices at the edge part of the infrastructure and supports openness and interoperability aspects in a device-independent way. In the same context, an orchestration framework is supported for coordination between cloud and edge computing orchestration platforms, also considering ML and security protection. In [89], reference architectures are discussed for industrial IoT, Internet of Vehicles (IoV) as well as IoT-based smart homes. In [90], a novel approach is introduced that adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand, and delay sensitivity) as well as resource utilization and resource heterogeneity. The presented results indicate that this approach improves overall service time when compared to other benchmark approaches. In [91], an industrial edge-cloud collaborative computing platform is presented, namely, the Sophon Edge, which also makes use of AI-enabled operations. Sophon Edge adopts a pipeline-based computing model for streaming data from IoT devices. Moreover, this platform supports an iterative way for model evolution and updating to enable agile and data-driven IoT applications. In [92], an edge-cloud collaboration in the context of AI-assisted approaches is investigated. To this end, various aspects are discussed, such as privacy enhancement via federated learning, ML model training in hardware constraint devices, model compression, inference policies, etc.