Supporting AI Engineering on the IoT Edge through Model-Driven TinyML
This addresses software engineering complexities for AI in IoT systems, enabling TinyML on devices with kilobytes of memory and milliwatts of energy, though it appears incremental in applying existing methodologies to this domain.
The paper tackles the challenge of deploying machine learning on resource-constrained IoT edge devices by proposing a model-driven software engineering approach, validated through a predictive maintenance case study for a hydraulics system.
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.