German Research Center for Artificial Intelligence, Saarland Informatics Campus, Stuhlsatzenhausweg 3, 66123 Saarbruecken, Germany.
Sensors (Basel). 2021 Nov 12;21(22):7509. doi: 10.3390/s21227509.
The concept of the cloud-to-thing continuum addresses advancements made possible by the widespread adoption of cloud, edge, and IoT resources. It opens the possibility of combining classical symbolic AI with advanced machine learning approaches in a meaningful way. In this paper, we present a thing registry and an agent-based orchestration framework, which we combine to support semantic orchestration of IoT use cases across several federated cloud environments. We use the concept of based on machine learning (ML) services as abstraction, mediating between the instance level and the semantic level. We present examples of virtual sensors based on ML models for activity recognition and describe an approach to remedy the problem of missing or scarce training data. We illustrate the approach with a use case from an assisted living scenario.
云到物连续体的概念解决了广泛采用云、边缘和物联网资源所带来的挑战。它为将经典的符号人工智能与先进的机器学习方法以有意义的方式结合起来提供了可能。在本文中，我们提出了一个物注册表和一个基于代理的编排框架，我们将它们结合起来，以支持跨几个联邦云环境的物联网用例的语义编排。我们使用基于机器学习（ML）服务的概念作为抽象，在实例级别和语义级别之间进行调解。我们提供了基于 ML 模型的虚拟传感器的示例，用于活动识别，并描述了一种解决训练数据缺失或稀缺问题的方法。我们通过辅助生活场景中的一个用例来说明该方法。
