skip to main content
10.1145/3401895.3402055acmotherconferencesArticle/Chapter ViewAbstractPublication Pageseatis-orgConference Proceedingsconference-collections
research-article

Computational architecture of IoT data analytics for connected home based on deep learning

Published: 29 January 2021 Publication History

Abstract

The internet of things (IoT) is a computing paradigm that expands every day along with the number of devices connected to the network, that's why transmit information safely and be able to use all the computational capacity of the devices that compose it to analyze the generated data is one of the great challenges that it is tried to solve under the computational architecture proposed in the present article.

References

[1]
Stolpe, M. (2016). The Internet of Things: Opportunities and Challenges for Distributed Data Analysis. ACM SIGKDD Explorations Newsletter, 18(1), 15--34.
[2]
Carbonell, J. G., Michalski, R. S., & Mitchell, T. M. (1983). An overview of machine learning. In Machine learning (pp. 3--23). Morgan Kaufmann
[3]
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436--444.
[4]
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012, August). Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13--16). ACM.
[5]
Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78, 964--975.
[6]
Renart, E. G., Veith, A. D. S., Balouek-Thomert, D., de Assuncao, M. D., Lefèvre, L., & Parashar, M. (2019, May). Distributed Operator Placement for IoT Data Analytics Across Edge and Cloud Resources.
[7]
Yuan, X., He, P., Zhu, Q., & Li, X. (2019). Adversarial examples: Attacks and defenses for deep learning. IEEE transactions on neural networks and learning systems.
[8]
Jackson, K. R., Ramakrishnan, L., Muriki, K., Canon, S., Cholia, S., Shalf, J., ... & Wright, N. J. (2010, November). Performance analysis of high performance computing applications on the amazon web services cloud. In 2010 IEEE second international conference on cloud computing technology and science (pp. 159--168). IEEE.
[9]
Krishnan, S. P. T., & Gonzalez, J. L. U. (2015). Building your next big thing with google cloud platform: A guide for developers and enterprise architects. Apress.
[10]
Barga, R., Fontama, V., Tok, W. H., & Cabrera-Cordon, L. (2015). Predictive analytics with Microsoft Azure machine learning. Berkely, CA: Apress.
[11]
Naik, N. (2017, October). Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP. In 2017 IEEE international systems engineering symposium (ISSE) (pp. 1--7). IEEE.

Cited By

View all
  • (2022)Integrated Industrial Reference Architecture for Smart Healthcare in Internet of Things: A Systematic InvestigationAlgorithms10.3390/a1509030915:9(309)Online publication date: 29-Aug-2022

Index Terms

  1. Computational architecture of IoT data analytics for connected home based on deep learning

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    EATIS '20: Proceedings of the 10th Euro-American Conference on Telematics and Information Systems
    November 2020
    388 pages
    ISBN:9781450377119
    DOI:10.1145/3401895
    © 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 January 2021

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. IoT architecture
    2. deep learning
    3. fog computing
    4. internet of things (IoT)
    5. machine learning

    Qualifiers

    • Research-article

    Conference

    EATIS 2020

    Acceptance Rates

    Overall Acceptance Rate 17 of 64 submissions, 27%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)16
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 07 Mar 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Integrated Industrial Reference Architecture for Smart Healthcare in Internet of Things: A Systematic InvestigationAlgorithms10.3390/a1509030915:9(309)Online publication date: 29-Aug-2022

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media