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Supporting AI engineering on the IoT edge through model-driven TinyML

Moin, A., Challenger, M., Badii, A. and Günnemann, S. (2022) Supporting AI engineering on the IoT edge through model-driven TinyML. In: 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 27 June 2022 - 01 July 2022, Los Alamitos, CA, pp. 884-893, (ISSN: 0730-3157, ISBN 978166548810-5)

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To link to this item DOI: 10.1109/COMPSAC54236.2022.00140


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 Tiny ML. 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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:108395


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