An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications

In the applications of the Internet of Things (IoT), sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime since there is additional data transmissions that must be supported by the board...

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Main Author:	MOhammed Alduais, Nayef Abdulwahab
Format:	Thesis
Language:	English English English
Published:	2019
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.uthm.edu.my/98/1/24p%20NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS.pdf http://eprints.uthm.edu.my/98/2/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/98/3/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20WATERMARK.pdf
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spelling	my-uthm-ep.982021-06-22T07:55:50Z An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications 2019-05 MOhammed Alduais, Nayef Abdulwahab QA75 Electronic computers. Computer science In the applications of the Internet of Things (IoT), sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime since there is additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this thesis is to increase the battery life of the IoT sensor node. To do so, a number of proposals are presented. First, an updating data strategy denoted as an efficient data collection and dissemination (EDCD) is proposed. EDCD aims to save the energy consumption of the IoT sensor board with multiple sensors by means of reducing the number of transmission packets, if no significant change is reported by the payload sensing block; second is proposed a validity of the measuring sensor reading at node level (VSNL) algorithm. VSNL aims to avoid transmitting any incorrect data, which will help in saving the energy consumption; third, an adaptive threshold and new metric for multivariate data reduction models such as principal component analysis – based (PCA-B) and multiple linear regression – based (MLR-B) have been proposed. In addition, proposed a payload data reduction algorithm (APRS). APRS aims to reduce the transmitted packet size for each sensed payload, which that will help in saving the energy of the IoT sensor board. This work provides an extensive analysis for the design and performance evaluation of real-time data collection model for multivariate sensors in IoT applications. Finally, an efficient real-time data collection model for multivariate sensors in IoT applications (RDCM). RDCM integrated EDCD, VSNL, PCA-B/MLR-B and APRS and the ability to prolong sensor board battery lifetime, which that satisfied by reducing number of transmissions and payload packet size, and also increase the accuracy of data validation. Performance of the proposed algorithms was evaluated through simulation by utilising various real-time datasets. The average of the total percentage of energy saved by applied RDCM to real-time data sets injected with various percentage of errors for all nodes is 98%. 2019-05 Thesis http://eprints.uthm.edu.my/98/ http://eprints.uthm.edu.my/98/1/24p%20NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS.pdf text en public http://eprints.uthm.edu.my/98/2/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/98/3/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science MOhammed Alduais, Nayef Abdulwahab An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications
description	In the applications of the Internet of Things (IoT), sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime since there is additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this thesis is to increase the battery life of the IoT sensor node. To do so, a number of proposals are presented. First, an updating data strategy denoted as an efficient data collection and dissemination (EDCD) is proposed. EDCD aims to save the energy consumption of the IoT sensor board with multiple sensors by means of reducing the number of transmission packets, if no significant change is reported by the payload sensing block; second is proposed a validity of the measuring sensor reading at node level (VSNL) algorithm. VSNL aims to avoid transmitting any incorrect data, which will help in saving the energy consumption; third, an adaptive threshold and new metric for multivariate data reduction models such as principal component analysis – based (PCA-B) and multiple linear regression – based (MLR-B) have been proposed. In addition, proposed a payload data reduction algorithm (APRS). APRS aims to reduce the transmitted packet size for each sensed payload, which that will help in saving the energy of the IoT sensor board. This work provides an extensive analysis for the design and performance evaluation of real-time data collection model for multivariate sensors in IoT applications. Finally, an efficient real-time data collection model for multivariate sensors in IoT applications (RDCM). RDCM integrated EDCD, VSNL, PCA-B/MLR-B and APRS and the ability to prolong sensor board battery lifetime, which that satisfied by reducing number of transmissions and payload packet size, and also increase the accuracy of data validation. Performance of the proposed algorithms was evaluated through simulation by utilising various real-time datasets. The average of the total percentage of energy saved by applied RDCM to real-time data sets injected with various percentage of errors for all nodes is 98%.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	MOhammed Alduais, Nayef Abdulwahab
author_facet	MOhammed Alduais, Nayef Abdulwahab
author_sort	MOhammed Alduais, Nayef Abdulwahab
title	An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications
title_short	An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications
title_full	An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications
title_fullStr	An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications
title_full_unstemmed	An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications
title_sort	efficient real-time data collection model for multivariate sensors in internet of things (iot) applications
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2019
url	http://eprints.uthm.edu.my/98/1/24p%20NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS.pdf http://eprints.uthm.edu.my/98/2/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/98/3/NAYEF%20ABDULWAHAB%20MOHAMMED%20ALDUAIS%20WATERMARK.pdf
_version_	1747830529491730432

An efficient real-time data collection model for multivariate sensors in internet of things (IOT) applications

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