Indoor Localization-Based Office Automation System Using IOT Devices

Meena, Bhagwan Sahay; Laskar, Ramin Uddin; Hemachandran, K.

doi:10.1007/978-981-15-2780-7_24

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Meena et al [ 53 ] proposed a system that localizes a person and controls the devices. An indoor localization technique was applied to localize a person.…”

Section: Literature Surveymentioning

confidence: 99%

Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees

Uppal

Gupta

Juneja

et al. 2021

Journal of Healthcare Engineering

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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.

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“…Meena et al [ 53 ] proposed a system that localizes a person and controls the devices. An indoor localization technique was applied to localize a person.…”

Section: Literature Surveymentioning

confidence: 99%

Cloud-Based Fault Prediction Using IoT in Office Automation for Improvisation of Health of Employees

Uppal

Gupta

Juneja

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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“…A large amount of data is generated by different devices and many applications can be developed by this generated data [3]. The IoT creates a doorway for several applications like as surveillance [4], smart cities [5], home automation [6] and healthcare [7]-object localization [8] etc.…”

Section: Internet Of Thingsmentioning

confidence: 99%

Device based Multi-User Tracking System using Internet of Things

Meena

2022

IJRITCC

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In Light Dependent Resistor (LDR) sensor-based user is localized based on the event and the intensity of the room light when a user enters inside a room and switch ON the lights, the intensity goes high, an entry is noti?ed. An exit is noti?ed when a user switches OFF the light and exit the room. Moreover, the model remains prone to more error in multi user localization because multiple users may enter inside same room at same time and the lights of many rooms remain ON which makes more difficult to localize a user. In order to overcome this ambiguity of light sensors, two passive infrared (PIR) sensor with radio frequency identi?cation (RFID) tag-based model has been proposed, where every user has a tag. In this system, 10 PIR sensors and 5 RFID readers were attached to house room (10.0 m * 6.0m). An entry is noti?ed if the following pattern form, the outer PIR detects a motion and waits for few seconds, next the RFID reader reads the tag given to the user and ?nally the inner PIR detects a motion within the given time delay. An exit of a user is noti?ed only if the pattern from inner PIR to outer PIR is followed with the given time delay. The RFID tag is used to identify which user has entered a room at a particular time and also ensures unauthorized entry. The LDR based system gives accuracy nearby 20% but the multi-person tracking in a binary infrared sensor network-based system gives accuracy near about 90%. In this paper, the proposed PIR sensor along with RFID based indoor navigation system gives accuracy near about 94%.

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