TY - GEN
T1 - Supporting AI Engineering on the IoT Edge through Model-Driven TinyML
AU - Moin, Armin
AU - Challenger, Moharram
AU - Badii, Atta
AU - Gunnemann, Stephan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - domain-specific modeling
KW - edge analytics
KW - internet of things
KW - machine learning
KW - model-driven software engineering
KW - tinyml
UR - http://www.scopus.com/inward/record.url?scp=85136997057&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC54236.2022.00140
DO - 10.1109/COMPSAC54236.2022.00140
M3 - Conference contribution
AN - SCOPUS:85136997057
T3 - Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022
SP - 884
EP - 893
BT - Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022
A2 - Va Leong, Hong
A2 - Sarvestani, Sahra Sedigh
A2 - Teranishi, Yuuichi
A2 - Cuzzocrea, Alfredo
A2 - Kashiwazaki, Hiroki
A2 - Towey, Dave
A2 - Yang, Ji-Jiang
A2 - Shahriar, Hossain
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022
Y2 - 27 June 2022 through 1 July 2022
ER -
