TY - GEN
T1 - A Lifecycle for Engineering IoT Neural Network-based Systems
AU - Nascimento, Nathalia
AU - Alencar, Paulo
AU - Cowan, Donald
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Internet of Things (IoT) applications have been deployed in several domains, including health care, smart cities, and agriculture. Because of the complex static and dynamic variability of the environment in which these applications are deployed, machine learning-based approaches have been used to support the design of IoT applications. In particular, an emergent approach involves using neural networks to enable IoT devices to learn to adapt their behavior based on the dynamics of the environment. Designing IoT systems is already challenging because of the autonomy and concurrency inherent in distributed physical systems. Moreover, neural networks systems have particular characteristics, such as dynamism, adaptability, and generalization, that make it necessary to adapt the traditional software development lifecycle to satisfy the requirements of these systems. In this paper, we describe our proposed approach to support the engineering of IoT neural network-based systems. Our approach considers a lifecycle supporting the integration of IoT system development tasks with particular ANN tasks, as model requirements and feature engineering. In addition, the paper includes the provision of the application of the approach to a case study and conclusive remarks.
AB - Internet of Things (IoT) applications have been deployed in several domains, including health care, smart cities, and agriculture. Because of the complex static and dynamic variability of the environment in which these applications are deployed, machine learning-based approaches have been used to support the design of IoT applications. In particular, an emergent approach involves using neural networks to enable IoT devices to learn to adapt their behavior based on the dynamics of the environment. Designing IoT systems is already challenging because of the autonomy and concurrency inherent in distributed physical systems. Moreover, neural networks systems have particular characteristics, such as dynamism, adaptability, and generalization, that make it necessary to adapt the traditional software development lifecycle to satisfy the requirements of these systems. In this paper, we describe our proposed approach to support the engineering of IoT neural network-based systems. Our approach considers a lifecycle supporting the integration of IoT system development tasks with particular ANN tasks, as model requirements and feature engineering. In addition, the paper includes the provision of the application of the approach to a case study and conclusive remarks.
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U2 - 10.1109/BigData52589.2021.9671413
DO - 10.1109/BigData52589.2021.9671413
M3 - Conference contribution
AN - SCOPUS:85125307904
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 2427
EP - 2433
BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
Y2 - 15 December 2021 through 18 December 2021
ER -