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.
@article{arxiv.2006.04950,
title = {Machine Learning Systems for Intelligent Services in the IoT: A Survey},
author = {Wiebke Toussaint and Aaron Yi Ding},
journal= {arXiv preprint arXiv:2006.04950},
year = {2020}
}