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Impact of neighborhood discovering and adaptive sampling in wireless sensor networks
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Impact of neighborhood discovering and adaptive sampling in wireless sensor networks
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ETD_1408
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051027
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eng
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theses
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Electrical and Computer Engineering
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Topic
Wireless sensor networks
Abstract
Wireless Sensor Networks (WSNs) are networks characterized by a dense deployment of sensor nodes. Because of the dense deployment, sensors can make interference when exchanging data messages. Besides these data messages, in location-based routing that uses geographical positions to route messages, there is a Neighborhood Discovery Protocol (NDP). It should periodically broadcast "Hello" packets to discover neighboring nodes and maintain routing tables updated. This is due to the uncertainty of the wireless environment such as varying radio interference and mobility. Due to the overhead caused by these periodic broadcasts from many nodes in certain radio range, however, NDP may heavily impact on the performance of the routing scheme itself, which in turn could affect end-to-end performance. Although this is an important and challenging problem in WSNs, this impact and the associated tradeoffs have not been fully explored in the literature. Hence, in the first half of this thesis, an analytical and experimental study is conducted to determine how parameters such as power and transmission frequency of neighborhood discovery packets affect the communication process in static and mobile environments.
In addition, WSNs are used to monitor and reliably estimate a phenomenon from the collective information provided by its constituent sensor nodes. Due to the high density of the sensor nodes, the data obtained from them are usually correlated in both space and time. Adaptive sampling is a method that employs this spatio-temporal correlation inherent in WSNs to obtain an energy-efficient estimate of the field. In the second half of this thesis, a distributed, hierarchical, cluster-based adaptive sampling framework is proposed using multiple manifestations for field estimation in three-dimensional environment. Nodes sensing highly correlated values in space are grouped to form clusters and these clusters are modified based on variation in sensor data over time. Energy efficiency is achieved through minimization of communication costs by restricting data communication to the local domain (within clusters) and by applying sleep mode. Moreover, a phenomenon is more reliably captured by using multiple manifestations than by using a single manifestation. It ensures joint optimization by adaptively varying the sampling rates in both space and time domains.
In addition, WSNs are used to monitor and reliably estimate a phenomenon from the collective information provided by its constituent sensor nodes. Due to the high density of the sensor nodes, the data obtained from them are usually correlated in both space and time. Adaptive sampling is a method that employs this spatio-temporal correlation inherent in WSNs to obtain an energy-efficient estimate of the field. In the second half of this thesis, a distributed, hierarchical, cluster-based adaptive sampling framework is proposed using multiple manifestations for field estimation in three-dimensional environment. Nodes sensing highly correlated values in space are grouped to form clusters and these clusters are modified based on variation in sensor data over time. Energy efficiency is achieved through minimization of communication costs by restricting data communication to the local domain (within clusters) and by applying sleep mode. Moreover, a phenomenon is more reliably captured by using multiple manifestations than by using a single manifestation. It ensures joint optimization by adaptively varying the sampling rates in both space and time domains.
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electronic resource
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xii, 64 p. : ill.
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M.S.
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Includes bibliographical references (p. 62-64)
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by Eun Kyung Lee
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Lee
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Eun Kyung
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Eun Kyung Lee
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Dario
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Advisory Committee
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Dario Pompili
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Gajic
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Zoran
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Zoran Gajic
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Silver
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Deborah
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Deborah Silver
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Rutgers University
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Graduate School - New Brunswick
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2009
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2009-01
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Rutgers University Electronic Theses and Dissertations
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ETD
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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doi:10.7282/T36Q1XHG
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ETD graduate
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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