The successful deployment of the Semantic Web of Things (SWoT) requires the adaptation of the Semantic Web principles and technologies to the constraints of the IoT domain, which is the challenging research direction we address here. In this context we promote distributed reasoning approaches in IoT systems by implementing a hybrid deployment of reasoning rules relying on the complementarity of Cloud and Fog computing. Our solution benefits from the complementarity between Cloud and Fog infrastructures. Indeed, remote powerful Cloud computation resources are essential to the deployment of scalable IoT applications, and locally distributed constrained Fog resources, close to data producers, enable low-latency decision making. Moreover, as IoT networks are open and evolutive, the computation should be dynamically distributed across Fog nodes according to the transformation of the network topology. For this purpose, we propose the Emergent Distributed Reasoning (EDR) approach, implementing a dynamic distributed deployment of reasoning rules in a Cloud–Fog IoT architecture. We elaborated mechanisms enabling the genericity and the dynamicity of EDR. We evaluated its scalability and applicability in a simulated smart factory use-case. The complementarity between Fog and Cloud in this context is assessed based on the experimentation conducted.

中文翻译：

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EDR：在Cloud-Fog连续体中分布基于规则的推理的通用方法

成功部署语义网（SWoT）需要使语义网原理和技术适应IoT领域的约束，这是我们在此提出的具有挑战性的研究方向。在这种情况下，我们通过依赖于云计算和雾计算的互补性实施推理规则的混合部署，在物联网系统中推广分布式推理方法。我们的解决方案受益于Cloud和Fog基础架构之间的互补性。确实，强大的远程云计算资源对于可扩展的IoT应用程序的部署至关重要，而本地分布式受约束的Fog资源（靠近数据生产者）可实现低延迟决策。此外，由于物联网网络是开放且不断发展的，计算应根据网络拓扑的变换动态分布在Fog节点上。为此，我们提出了紧急分布式推理（EDR）方法，在Cloud-Fog IoT架构中实施推理规则的动态分布式部署。我们阐述了使EDR具有通用性和动态性的机制。我们在模拟的智能工厂用例中评估了它的可伸缩性和适用性。在此背景下，雾和云之间的互补性是根据进行的实验进行评估的。我们阐述了使EDR具有通用性和动态性的机制。我们在模拟的智能工厂用例中评估了它的可伸缩性和适用性。在此背景下，雾和云之间的互补性是根据进行的实验进行评估的。我们阐述了使EDR具有通用性和动态性的机制。我们在模拟的智能工厂用例中评估了它的可伸缩性和适用性。在此背景下，雾和云之间的互补性是根据进行的实验进行评估的。