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Presenter: Khalid Almuzaini
Supervisor: Dr. T. Aaron Gulliver

Date: Wed, March 30, 2011
Time: 09:30:00 - 10:30:00
Place: EOW 430

ABSTRACT

Abstract:

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.

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 target. Each unlocalized node uses the intersections between the ranging radii of these anchors to estimate its position.

Two 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 the decision tree classification and K-means clustering algorithms applied to the selected intersection points by the decision tress. The second one 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 locations of the selected points by the two different clustering algorithms are very close to the actual location of the unlocalized node.

These proposed approaches are shown to outperform recent algorithms in the literature.