Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations. Various sensors and field devices play a central role, as they generate a vast amount of real-time data that can provide insights into manufacturing. The synergy of complex event processing (CEP) and machine learning (ML) has been developed actively in the last years in IIoT to identify patterns in heterogeneous data streams and fuse raw data into tangible facts. In a traditional compute-centric paradigm, the raw field data are continuously sent to the cloud and processed centrally. As IIoT devices become increasingly pervasive and ubiquitous, concerns are raised since transmitting such amount of data is energy-intensive, vulnerable to be intercepted, and subjected to high latency. The data-centric paradigm can essentially solve these problems by empowering IIoT to perform decentralized on-device ML and CEP, keeping data primarily on edge devices and minimizing communications. However, this is no mean feat because most IIoT edge devices are designed to be computationally constrained with low power consumption. This paper proposes a framework that exploits ML and CEP's synergy at the edge in distributed sensor networks. By leveraging tiny ML and micro CEP, we shift the computation from the cloud to the power-constrained IIoT devices and allow users to adapt the on-device ML model and the CEP reasoning logic flexibly on the fly without requiring to reupload the whole program. Lastly, we evaluate the proposed solution and show its effectiveness and feasibility using an industrial use case of machine safety monitoring.

中文翻译：

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工业物联网中复杂事件处理与微型机器学习的协同作用

工业物联网（IIoT）专注于综合网络，大数据和人工智能，可提高工厂运营的效率和健壮性。各种传感器和现场设备起着核心作用，因为它们生成大量的实时数据，可以提供有关制造方面的见解。复杂事件处理（CEP）和机器学习（ML）的协同作用在IIoT中已在过去几年中得到了积极发展，以识别异构数据流中的模式并将原始数据融合为有形事实。在传统的以计算为中心的范例中，原始现场数据连续发送到云中并进行集中处理。随着IIoT设备的普及和无处不在，由于传输如此大量的数据会消耗大量能源，容易被拦截，并承受高延迟。以数据为中心的范式可以通过授权IIoT执行分散的设备上ML和CEP，将数据主要保留在边缘设备上并最大程度地减少通信来实质上解决这些问题。但是，这绝非易事，因为大多数IIoT边缘设备都设计为在计算上受到低功耗的约束。本文提出了一个框架，该框架在分布式传感器网络的边缘利用ML和CEP的协同作用。通过利用微型ML和微型CEP，我们将计算从云转移到功耗受限的IIoT设备，并允许用户即时灵活地调整设备上ML模型和CEP推理逻辑，而无需重新上传整个程序。最后，