[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127638-en":3,"doc-seo-127638-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127638,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A smart environmental monitoring system for data centres using IoT and machine learning - Project report","Data centres rely on expensive infrastructure to store and process critical information, making their environmental stability essential for continuous service. Poor conditions can degrade performance, trigger sporadic failures, and cause irreversible equipment damage with potential data loss. This project develops an IoT- and machine-learning-based monitoring system to measure key parameters (temperature, humidity, smoke, water, voltage, and current), transmit data via a wireless sensor network to local storage on Raspberry Pi 4 and to ThingSpeak, and issue alerts through audio, email, SMS, and WhatsApp. Time-series forecasting using Prophet, ARIMA, and exponential smoothing predicts temperature and humidity trends, with Prophet achieving the lowest errors.","The Nelson Mandela AFrican Institution of Science and Technology  \nNM-AIST Repository [https://dspace.mm-aist.ac.tz](https://dspace.mm-aist.ac.tz)  \nComputational and Communication Science Engineering Masters Theses and Dissertations [CoCSE]  \n2023-08  \nA smart environmental monitoring system for data centres using IOT and machine learning  \nOkello, Wayne  \nNM-AIST  \n[https://doi.org/10.58694/20.500.12479/2144](https://doi.org/10.58694/20.500.12479/2144)  \nProvided with love from The Nelson Mandela African Institution of Science and Technology  \nA SMART ENVIRONMENTAL MONITORING SYSTEM FOR DATA CENTRES USING IoT AND MACHINE LEARNING  \nWayne Steven Okello  \nA Project Report Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Embedded and Mobile Systems of the Nelson Mandela African  \nInstitution of Science and Technology  \nArusha, Tanzania  \nABSTRACT  \nData centres are a crucial part of many organizations in the world today consisting ofexpensive assets that store and process critical business data as well as applications responsible for their daily operations. Unconducive environmental conditions can lead to 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. The objective of this project was to develop an environmental monitoring system that employs Internet of Things (IoT) and machine learning to monitor and predict important environmental parameters within a data centre setting. The system comprises of 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 asthe 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 centre 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, AutoRegressive 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. In conclusion, the developed environmental monitoring system for data centers surpasses existing alternatives by integrating forecasting capabilities, monitoring several critical parameters, and offering scalability for improved efficiency and reliability. The study recommendations include exploring a Web of Things (WoT) approach and incorporating instant corrective measures for improved performance.  \nI, Wayne Okello, declare of  \nInstitution  \n\n| Name of Candidate | Signature | Date |\n| --- | --- | --- |\n|  | Signature | Date |\n\nName of Supervisor Signature  \nCOPYRIGHT  \nThis project report is copyright material protected under the Berne Convention, the Copyright Act of 1999, and other international and national enactments, on behalf, of intellectual property. It must not be produced by any means, in full or in part, except for shorts extracts in fair dealing, for researcher private study, critical scholarly review, or discourse with an acknowledgment, without the written permission of the office of Deputy Vice-Chancellor for Academic, Research, and Innovation on behalf of the author and the Nelson Mandela African Institution of Science and Technology.  \nThe undersigned certify that they have read and hereby recommend for acceptance by the  \nNelson Mandela African Institution of Science and Technology, a project report titled “A  \nSmart Enviro","cbCaimpimEVblorh","https://ap.wps.com/l/cbCaimpimEVblorh","pdf",3644465,2,1,109,"English","en",105,"# Abstract\n# Declaration\n# Copyright\n# Acknowledgements\n# Dedication\n# Table of Contents","[{\"question\":\"What environmental parameters does the monitoring system measure in the data centre?\",\"answer\":\"It measures temperature, humidity, smoke, water, voltage, and current using a wireless sensor network with four sensor nodes and a sink node.\"},{\"question\":\"How are sensor readings delivered and stored by the system?\",\"answer\":\"Readings are sent wirelessly to a Raspberry Pi 4 database for local storage and also uploaded to the ThingSpeak platform for cloud logging and real-time visualization.\"},{\"question\":\"Which machine learning models are used for forecasting, and which performed best?\",\"answer\":\"The project trains time-series forecasting models using Facebook Prophet, ARIMA, and Exponential Smoothing, with Facebook Prophet achieving the best performance (MAPE 5.77% for temperature and 8.98% for humidity).\"}]","A smart environmental monitoring system for data centres using IoT and machine learning - 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