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

arXiv: https://arxiv.org/abs/2107.02690 · HTML: https://mlsanity.com/paper/2107.02690/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)](https://mlsanity.com/paper/2009.10632/) (2020)
- [ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning](https://mlsanity.com/paper/2009.10633/) (2020)
- [GreyCat: Efficient What-If Analytics for Data in Motion at Scale](https://mlsanity.com/paper/1803.09627/) (2018)
- [Kevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use](https://mlsanity.com/paper/1405.6817/) (2014)

## Built on by (most cited first, 3 of 3)

- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [TinyML for Speech Recognition](https://mlsanity.com/paper/2504.16213/) (2025)
- [MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat](https://mlsanity.com/paper/2209.07282/) (2022)

## Abstract (arXiv)

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.