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Permanent link (DOI): https://doi.org/10.7939/R3SH39

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Extending the Lifetime of Wireless Sensor Networks with Spatial Data Aggregation Open Access

Descriptions

Other title
Subject/Keyword
Sensor Network, Lifetime, Spatial Data Aggregation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Zou, Shoudong
Supervisor and department
Nikolaidis, Ioanis (Computing Science)
Harms, Janelle (Computing Science)
Examining committee member and department
Kunz, Thomas (Systems and Computer Engineering, Carleton University)
Nascimento, Mario (Computing Science)
Sander, Joerg (Computing Science)
Kurgan, Lukasz (Electrical and Computer Engineering)
Department
Department of Computing Science
Specialization

Date accepted
2009-08-27T15:57:25Z
Graduation date
2009-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
In this thesis, we propose mechanisms to extend the lifetime of wireless sensor networks. In-network data aggregation is considered on both tree-based and flow-based routing protocols during the process of data collection to reduce redundant transmissions. In the flow-based data collection design, we introduce the concept of flow loss multiplier to express the impact of data aggregation over correlated data. The application has the freedom to set the flow loss multiplier to reflect its specific knowledge of correlation. We also introduce traffic balancing as a complementary technique to data aggregation. It helps avoid exhausting the energy of any sensor node while leaving large amounts of energy at other nodes. In tree-based data collection schemes, we adjust the tree structure judiciously to balance energy consumption before any node's failure due to total residual energy depletion. In flow-based schemes, after aggregation, data flows are split and the fragments are spread to increase network lifetime. We investigate the impact of performing greedily data aggregation at the "best" aggregation site regardless of its location, the results of our analysis show that only applying 2-way data aggregation may limit the ability to explore more complex aggregation possibilities. To address this problem, we propose an aggressive data aggregation for a specified application, contour map reconstruction. Based on the simulation results, our aggregation scheme is shown to be able to eliminate large volume of contour data and retain satisfying data accuracy.
Language
English
DOI
doi:10.7939/R3SH39
Rights
License granted by Shoudong Zou (szou@ualberta.ca) on 2009-08-26T16:44:09Z (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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