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

Wayne Steven Okello (), Silas Mirau (), Michael Kisangiri (), Andrew Katumba () and Edwin Mugume ()
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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

A chapter in Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, 2024, pp 261-271 from Springer

Abstract: 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.

Keywords: Environmental monitoring; Data centers; IoT; Machine learning; Time series forecasting; Wireless sensor network (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-56576-2_23

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DOI: 10.1007/978-3-031-56576-2_23

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