Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services. This survey moves beyond existing ML algorithms and cloud-driven design to investigate the less-explored systems, scaling and socio-technical aspects for consolidating ML and IoT. It covers the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. With a multi-layered framework to classify and illuminate system design choices, this survey exposes fundamental concerns of developing and deploying ML systems in the rising cloud-edge-device continuum in terms of functionality, stakeholder alignment and trustworthiness.
翻译:在物业互联网(IoT)中,正在出现机器学习技术,以提供智能服务,这项调查超越了现有的ML算法和云驱动设计,以调查整合ML和IoT的探索较少的系统、规模扩大和社会技术方面。它涵盖在云层、边缘和IoT装置之间扩大和传播ML的最新动态(截至2020年)。通过一个多层次框架对系统设计选择进行分类和说明,这项调查揭示了在功能、利益攸关方调整和信任性方面不断上升的云层-顶端装置连续体中开发和部署ML系统的根本关切。