IDEAS home Printed from https://ideas.repec.org/h/spr/prochp/978-3-031-56576-2_23.html
   My bibliography  Save this book chapter

A Machine Learning-Based IoT Environmental Monitoring Platform for Data Centers

In: Artificial Intelligence Tools and Applications in Embedded and Mobile Systems

Author

Listed:
  • Wayne Steven Okello

    (School of Computational and Communication Science and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST))

  • Silas Mirau

    (School of Computational and Communication Science and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST))

  • Michael Kisangiri

    (School of Computational and Communication Science and Engineering (CoCSE), Nelson Mandela African Institution of Science and Technology (NM-AIST))

  • Andrew Katumba

    (Makerere University)

  • Edwin Mugume

    (Makerere University)

Abstract

Data centers are a crucial part of many organizations in the world today consisting of expensive assets that store and process critical business data as well as applications responsible for their daily operations. Unconducive environmental conditions can lead to a decline in performance, sporadic failures, and total damage of equipment in the data centers which can consequently lead to data loss as well as disruption of the continuity of business operations. This paper describes an environmental monitoring system that employs IoT and machine learning to monitor and predict important environmental parameters within a data center setting. The system comprises a Wireless Sensor Network (WSN) of four (4) sensor nodes and a sink node. The sensor nodes measure environmental parameters of temperature, humidity, smoke, water, voltage, and current. The readings captured from the sensor nodes are sent wirelessly to a database on a Raspberry Pi 4 for local storage as well as the ThingSpeak platform for cloud data logging and real-time visualization. An audio alarm is triggered, and email, Short Message Service (SMS), as well as WhatsApp alert notifications are sent to the data center administrators in case any undesirable environmental condition is detected. Time series forecasting machine learning models were developed to predict future temperature and humidity trends. The models were trained using Facebook Prophet, Auto-Regressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ES) algorithms. Facebook Prophet manifested the best performance with a Mean Absolute Percentage Error (MAPE) of 5.77% and 8.98% for the temperature and humidity models, respectively.

Suggested Citation

  • Wayne Steven Okello & Silas Mirau & Michael Kisangiri & Andrew Katumba & Edwin Mugume, 2024. "A Machine Learning-Based IoT Environmental Monitoring Platform for Data Centers," Progress in IS, in: Jorge Marx Gómez & Anael Elikana Sam & Devotha Godfrey Nyambo (ed.), Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, pages 261-271, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-56576-2_23
    DOI: 10.1007/978-3-031-56576-2_23
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prochp:978-3-031-56576-2_23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.