Reliable Fleet Analytics for Edge IoT Solutions

TL;DR

This paper introduces a comprehensive framework for deploying and managing machine learning models at the edge in IoT environments, enabling scalable and robust fleet analytics for real-time decision making.

Contribution

It presents a novel architecture with services and tools for continuous deployment, monitoring, and management of edge ML models in AIoT applications.

Findings

Successful deployment of multivariate time series forecasting models at edge devices

Demonstrated efficiency and robustness of the fleet analytics framework

Validated real-time air quality prediction in campus IoT rooms

Abstract

In recent years we have witnessed a boom in Internet of Things (IoT) device deployments, which has resulted in big data and demand for low-latency communication. This shift in the demand for infrastructure is also enabling real-time decision making using artificial intelligence for IoT applications. Artificial Intelligence of Things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the IoT infrastructure to provide robust and efficient operations and decision making. Edge computing is emerging to enable AIoT applications. Edge computing enables generating insights and making decisions at or near the data source, reducing the amount of data sent to the cloud or a central repository. In this paper, we propose a framework for facilitating machine learning at the edge for AIoT applications, to enable continuous delivery, deployment, and monitoring of machine learning models at the edge (Edge MLOps). The contribution is an architecture that includes services, tools, and methods for delivering fleet analytics at scale. We present a preliminary validation of the framework by performing experiments with IoT devices on a university campus's rooms. For the machine learning experiments, we forecast multivariate time series for predicting air quality in the respective rooms by using the models deployed in respective edge devices. By these experiments, we validate the proposed fleet analytics framework for efficiency and robustness.