A Lifecycle for Engineering IoT Neural Network-based Systems

Nathalia Nascimento, Paulo Alencar, Donald Cowan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    Abstract

    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.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
    EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2427-2433
    Number of pages7
    ISBN (Electronic)9781665439022
    DOIs
    StatePublished - 2021
    Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
    Duration: Dec 15 2021Dec 18 2021

    Publication series

    NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

    Conference

    Conference2021 IEEE International Conference on Big Data, Big Data 2021
    Country/TerritoryUnited States
    CityVirtual, Online
    Period12/15/2112/18/21

    All Science Journal Classification (ASJC) codes

    • Information Systems and Management
    • Artificial Intelligence
    • Computer Vision and Pattern Recognition
    • Information Systems

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