Machine Learning Systems for Intelligent Services in the IoT: A Survey

TL;DR

This survey reviews recent developments in machine learning systems for IoT, focusing on system design, scaling, deployment across cloud, edge, and devices, and addressing socio-technical challenges.

Contribution

It provides a comprehensive framework for classifying system design choices and highlights key concerns in deploying ML in the IoT cloud-edge-device continuum.

Findings

Explores scaling and distribution of ML in IoT environments.

Identifies socio-technical challenges in ML-IoT system deployment.

Provides a multi-layered classification framework for system design.

Abstract

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.