Author : Khalid Almuzaini
Release : 2011
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Time Synchronization and Localization in Wireless Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Time Synchronization and Localization in Wireless Networks write by Khalid Almuzaini. This book was released on 2011. Time Synchronization and Localization in Wireless Networks available in PDF, EPUB and Kindle. 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 in many 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.