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Studying Water Pollution Based on Wi...

Alnahash, Nahed.

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Studying Water Pollution Based on Wireless Sensor Networks and Stochastic Approximation Method.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Studying Water Pollution Based on Wireless Sensor Networks and Stochastic Approximation Method./
Author:	Alnahash, Nahed.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	99 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Water Resource Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13809477
ISBN:	9781392244609

Studying Water Pollution Based on Wireless Sensor Networks and Stochastic Approximation Method.
Alnahash, Nahed.

Studying Water Pollution Based on Wireless Sensor Networks and Stochastic Approximation Method. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 99 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--Oakland University, 2019.

This item must not be sold to any third party vendors.

Water is essential to human life and a healthy environment, it is important that the water is in a good, usable condition. Water pollution has a significant impact on the environment, and it should be monitored over time. A conventional method requires personnel to physically go to the location, take the samples from different places, and bring them back to the lab for processing. The results take a long time and are not accurate because of human error and also cost more money. While we are living in the technology evolution world especially in the speed world it is important to use some of these technologies to do that. One of that technology is wireless sensors. This technique facilitates the process of gathering information and getting results in a short time, with minimal effort, and low cost. A novel monitoring system architecture based on a wireless sensor network is proposed in this research. In this research, a wireless network was built using MatLab software, and the low-energy adaptive clustering hierarchy (LEACH) is considered the best algorithm in the construction of clusters.This research focuses on water quality monitoring and water pollution, using the Flint water crisis as a case study. (1) With regard to water quality monitoring, only three predetermined water parameters were tested: potential hydrogen (pH), dissolved oxygen (DO), and turbidity. After testing, the results were compared with those in the World Health Organization (WHO) report. (2) Regarding the Flint water crisis, the research focused on lead and copper then after processing compared the results with data collected from Michigan's state website. A stochastic approximation method is considered to measure the collected sensor's data. In this research, develops the simple stochastic approximation, by applying three different methods; the normal distribution method, Weibull distribution method, and uniform distribution method. After all, processing, the comparison between the three methods applied to figure out which one we can suggest using according to the period of time in which we got accurate results. While this research uses a wireless sensor network, the cluster is a good method to use to save the life of the sensors for a long time. Each cluster collects any data within its range and sends its sensor data to the base station, where all sensors' data noise is reduced via a linear Kalman filter.

ISBN: 9781392244609Subjects--Topical Terms:

1669219
Water Resource Management.

Studying Water Pollution Based on Wireless Sensor Networks and Stochastic Approximation Method.
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300 $a 99 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Zohdy, Mohamed A.
502 $a Thesis (Ph.D.)--Oakland University, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Water is essential to human life and a healthy environment, it is important that the water is in a good, usable condition. Water pollution has a significant impact on the environment, and it should be monitored over time. A conventional method requires personnel to physically go to the location, take the samples from different places, and bring them back to the lab for processing. The results take a long time and are not accurate because of human error and also cost more money. While we are living in the technology evolution world especially in the speed world it is important to use some of these technologies to do that. One of that technology is wireless sensors. This technique facilitates the process of gathering information and getting results in a short time, with minimal effort, and low cost. A novel monitoring system architecture based on a wireless sensor network is proposed in this research. In this research, a wireless network was built using MatLab software, and the low-energy adaptive clustering hierarchy (LEACH) is considered the best algorithm in the construction of clusters.This research focuses on water quality monitoring and water pollution, using the Flint water crisis as a case study. (1) With regard to water quality monitoring, only three predetermined water parameters were tested: potential hydrogen (pH), dissolved oxygen (DO), and turbidity. After testing, the results were compared with those in the World Health Organization (WHO) report. (2) Regarding the Flint water crisis, the research focused on lead and copper then after processing compared the results with data collected from Michigan's state website. A stochastic approximation method is considered to measure the collected sensor's data. In this research, develops the simple stochastic approximation, by applying three different methods; the normal distribution method, Weibull distribution method, and uniform distribution method. After all, processing, the comparison between the three methods applied to figure out which one we can suggest using according to the period of time in which we got accurate results. While this research uses a wireless sensor network, the cluster is a good method to use to save the life of the sensors for a long time. Each cluster collects any data within its range and sends its sensor data to the base station, where all sensors' data noise is reduced via a linear Kalman filter.
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