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The concept of Edge-to-Cloud Continuum aims to significantly reduce overall traffic to the cloud by enabling IoT data processing as close as possible to the data sources, either on near- or far-edge devices. In this highly dynamic environment, where IoT devices and edge nodes are constantly changing their state and location, services running on edge nodes have to be scheduled, deployed and managed to ensure high service availability with appropriate Quality of Service (QoS) parameters. However, once services are deployed in the edge-to-cloud continuum, the question arises how to ensure continuous data delivery from IoT devices to the appropriate services for further processing, either on edge devices or in the cloud. In this paper, we propose a general architecture for adaptive data-driven routing in the edge-to-cloud continuum and introduce an implementation of this architecture using a content-based publish/subscribe approach. We evaluate the given implementation against a real-world use case scenario for federated learning in an edge-to-cloud environment hosting digital twins. The performance evaluation of this scenario shows that our implementation efficiently adapts to service failures and reconfigures the edge-to-cloud environment with minimal latency and without data loss, while preserving data privacy and security. In addition, the experiments show that our solution is stable in an environment with a large number of IoT data sources generating data at high frequency. |
