Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees. (3rd November 2021)
- Record Type:
- Journal Article
- Title:
- Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees. (3rd November 2021)
- Main Title:
- Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees
- Authors:
- Uppal, Mudita
Gupta, Deepali
Juneja, Sapna
Dhiman, Gaurav
Kautish, Sandeep - Other Names:
- Hum Yan Chai Academic Editor.
- Abstract:
- Abstract : The novel paradigm of Internet of Things (IoT) is gaining recognition in the numerous scenarios promoting the pervasive presence of smart things around us through its application in various areas of society, which includes transportation, healthcare, industries, and agriculture. One more such application is in the smart office to monitor the health of devices via machine learning (ML) that makes the equipment more efficient by allowing real-time monitoring of their health. It guarantees indoor comfort as per the user's satisfaction as it emphasizes on fault prediction in real-life devices. Early identification of various types of faults in IoT devices is the key requirement in smart offices. IoT devices are becoming ubiquitous and provide an assistant to supervise an office that is regulated by ML and data received from sensors is stored in cloud. A recommender system facilitates the selection of an appropriate solution for faults in IoT-enabled devices to mitigate faults. The architecture proposed in this paper is used to monitor each and every office appliance connected via IoT technology using ML technique, and recommender system is used to recommend solutions for fault patterns without much human intervention. The ultrasonic motion sensor is used to fetch the information of employee availability in cubicles and data is sent to the cloud through the WiFi module. ATmega8 is used to control electrical appliances in the office environment. The significance of thisAbstract : The novel paradigm of Internet of Things (IoT) is gaining recognition in the numerous scenarios promoting the pervasive presence of smart things around us through its application in various areas of society, which includes transportation, healthcare, industries, and agriculture. One more such application is in the smart office to monitor the health of devices via machine learning (ML) that makes the equipment more efficient by allowing real-time monitoring of their health. It guarantees indoor comfort as per the user's satisfaction as it emphasizes on fault prediction in real-life devices. Early identification of various types of faults in IoT devices is the key requirement in smart offices. IoT devices are becoming ubiquitous and provide an assistant to supervise an office that is regulated by ML and data received from sensors is stored in cloud. A recommender system facilitates the selection of an appropriate solution for faults in IoT-enabled devices to mitigate faults. The architecture proposed in this paper is used to monitor each and every office appliance connected via IoT technology using ML technique, and recommender system is used to recommend solutions for fault patterns without much human intervention. The ultrasonic motion sensor is used to fetch the information of employee availability in cubicles and data is sent to the cloud through the WiFi module. ATmega8 is used to control electrical appliances in the office environment. The significance of this work is to forecast the faults in IoT appliances which will have an impact on life and reliability of IoT appliances. The main objective is to design a prototype of a smart office using IoT that can control and automate workplace devices and forecast whether the device needs repairing or replacing, thus reducing the overall burden on the employee and helping out in increasing physical as well as mental health of the person. … (more)
- Is Part Of:
- Journal of healthcare engineering. Volume 2021(2021)
- Journal:
- Journal of healthcare engineering
- Issue:
- Volume 2021(2021)
- Issue Display:
- Volume 2021, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 2021
- Issue:
- 2021
- Issue Sort Value:
- 2021-2021-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-11-03
- Subjects:
- Hospital buildings -- Environmental engineering -- Periodicals
Medical technology -- Periodicals
Medical informatics -- Periodicals
610.28 - Journal URLs:
- http://www.hindawi.com/journals/jhe/ ↗
http://multi-science.metapress.com/content/r03085752427/?p=bacc87ee7c194c1aa6a045ab293b1f0f&pi=2 ↗ - DOI:
- 10.1155/2021/8106467 ↗
- Languages:
- English
- ISSNs:
- 2040-2295
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 19894.xml