Localization is very important for self-organizing wireless networks. The localization
process involves two main steps: ranging, i.e., estimating the distance
between an unlocalized node and the anchor nodes within its range, and the localization
algorithm to compute the location of the unlocalized nodes using the
anchor coordinates and the estimated ranges. To be able to estimate the distance,
the receiver needs to detect the arrival time of the received signals precisely. Thus,
the first part of this research is related to time synchronization.
We propose two new symbol timing offset estimation (STO) algorithms that can
detect the start of an orthogonal frequency division multiplexing (OFDM) symbol
more accurately than others in a Rayleigh fading channel. OFDM is used to perform
timing synchronization because it is incorporated inmany current and future
wireless systems such as 802.11, WiMAX, wireless USB, and WiMedia. The first
proposed algorithm uses a metric that is calculated recursively. Two estimation
methods are considered: one using the average of the metric results, and the other
using the median. The second approach uses a preamble designed to have a maximum
timing metric for the correct location and very small values otherwise. These
algorithms are shown to outperform recent algorithms in the literature.
In the second part of this dissertation we explore the second step of the localization
problem. There are two kinds of localization: range-free and range-based.
A new distributed range-free localization algorithm is proposed where every unlocalized
node forms two sets of anchors. The first set contains one-hop anchors
from the unlocalized node. The second set contains two-hop and three-hop anchors
away from the unlocalized node. Each unlocalized node uses the intersections
between the ranging radii of these anchors to estimate its position.
Four different range-based localization algorithms are proposed. These algorithms
use techniques from data mining to process the intersection points between
an unlocalized node and nearby anchors. The first proposed scheme is based on
decision tree classification to select a group of intersection points. The second is
based on the decision tree classification and K-means clustering algorithms applied
to the selected intersection points by the decision trees. The third is based on
decision tree classification and the density-based spatial clustering of applications
with noise (DBSCAN) algorithm applied to the intersection points selected by decision
trees. The last approach uses the density-based outlier detection (DBOD)
algorithm. DBOD assigns density values to each point being used in the location
estimation. The mean of these densities is calculated and those points having a
density larger than the mean are kept as candidate points. These proposed approaches
are shown to outperform recent algorithms in the literature. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3637 |
| Date | 19 October 2011 |
| Creators | Almuzaini, Khalid |
| Contributors | Gulliver, T. Aaron |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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