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IntelliSensorNet: A Positioning Technique Integrating Wireless Sensor Networks and Artificial Neural Networks for Critical Construction Resource Tracking Open Access

Descriptions

Other title
Subject/Keyword
Construction Resource Tracking
Artificial Neural Network
Wireless Sensor Network
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Soleimanifar, Meimanat
Supervisor and department
Lu, Ming (Civil and Environmental Engineering)
Abourizk, Simaan (Civil and Environmental Engineering)
Examining committee member and department
Nikolaidis, Ioanis (Computing Science)
Qiu, Tony Z. , (Civil and Environmental Engineering)
Department
Department of Civil and Environmental Engineering
Specialization

Date accepted
2011-09-28T20:51:17Z
Graduation date
2011-11
Degree
Master of Science
Degree level
Master's
Abstract
The increasing needs for safety and productivity improvement in the field of construction engineering and project management have stimulated research interests in developing cost-effective resource tracking and positioning solutions for challenging indoor or partially covered site environments. This thesis has proposed a robust positioning architecture called IntelliSensorNet that relies on an integrated environment of Wireless Sensor Networks and Artificial Neural Networks for construction resource localization. The wireless sensor network (WSN) based component of the architecture determines the location of mobile sensor nodes (“tags”) by evaluating radio signal strengths (RSS) received by stationary sensor nodes (“pegs”). Only a limited quantity of reference points with known locations and pre-calibrated RSS in relation to the pegs are used to determine the most likely coordinates of a tag. Moreover, to effectively reduce uncertainty and improve accuracy, an on-line error correction approach based on a Radial Basis Function Neural Network (RBF NN) model is embedded in the proposed architecture. In short, this localization technique produces a cost-effective solution to positioning and tracking critical construction resources such as laborers and equipment for challenging indoor environments or partially covered site environments in construction, thus lending itself well to potential deployment in real-world construction sites.
Language
English
Rights
License granted by Meimanat Soleimanifar (meimanat@ualberta.ca) on 2011-09-23T17:28:01Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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