Supporting AI Engineering on the IoT Edge through Model-Driven TinyML

TL;DR

This paper introduces a model-driven TinyML approach for IoT edge AI, enhancing service quality, privacy, and performance in resource-constrained environments through domain-specific modeling and validation with a case study.

Contribution

It presents a novel model-driven methodology for deploying ML on heterogeneous, resource-limited IoT edge devices, integrating platform-independent and platform-specific models.

Findings

Improved ML service availability and performance at the edge.

Effective delegation of ML tasks to microcontrollers with minimal resources.

Successful validation through a predictive maintenance case study.

Abstract

Software engineering of network-centric Artificial Intelligence (AI) and Internet of Things (IoT) enabled Cyber-Physical Systems (CPS) and services, involves complex design and validation challenges. In this paper, we propose a novel approach, based on the model-driven software engineering paradigm, in particular the domain-specific modeling methodology. We focus on a sub-discipline of AI, namely Machine Learning (ML) and propose the delegation of data analytics and ML to the IoT edge. This way, we may increase the service quality of ML, for example, its availability and performance, regardless of the network conditions, as well as maintaining the privacy, security and sustainability. We let practitioners assign ML tasks to heterogeneous edge devices, including highly resource-constrained embedded microcontrollers with main memories in the order of Kilobytes, and energy consumption in the order of milliwatts. This is known as TinyML. Furthermore, we show how software models with different levels of abstraction, namely platform-independent and platform-specific models can be used in the software development process. Finally, we validate the proposed approach using a case study addressing the predictive maintenance of a hydraulics system with various networked sensors and actuators.