This book presents the latest research results in the area of secure localization for both wireless mobile ad hoc networks and wireless sensor networks. It is suitable as a text for computer science courses in wireless systems and security. It includes implementation studies with mica2 mote sensors. Due to the open spectrum nature of wireless communication, it is subject to attacks and intrusions. Hence the wireless network synchronization needs to be both robust and secure. Furthermore, issues such as energy constraints and mobility make the localization process even more challenging. The book will also interest developers of secure wireless systems.

Modern day networking and computing systems rely increasingly on knowing the location of the user. These include state-of-the art technologies such as navigation of vehicles and robots, traffic planning, or light control in smart home environments. In the future, even more services will appear. The predominant localization technology currently uses satellite signals but this only works outdoors, is expensive, and consumes considerable power. Alternative localization technologies have been recently developed using optical, ultra-sound, or radar techniques. All of these require additional hardware components to work effectively. Radio-frequency (RF) localization is a technique that uses communication signals to perform the task without the need for any extra hardware. This monograph addresses the role of synchronization in radio localization and provides a comprehensive overview of recent developments suitable for current and future practical implementations. The material is intended for both theoreticians and practitioners and is written to be accessible to novices while covering state-of-the-art topics of interest to advanced researchers of localization and synchronization systems.

This book presents the latest research results in the area of secure localization for both wireless mobile ad hoc networks and wireless sensor networks. It is suitable as a text for computer science courses in wireless systems and security. It includes implementation studies with mica2 mote sensors. Due to the open spectrum nature of wireless communication, it is subject to attacks and intrusions. Hence the wireless network synchronization needs to be both robust and secure. Furthermore, issues such as energy constraints and mobility make the localization process even more challenging. The book will also interest developers of secure wireless systems.

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.

In a computational tour-de-force, this volume wipes away a host of problems related to location discovery in wireless ad-hoc sensor networks. WASNs have recognized potential in many applications that are location-dependent, yet are heavily constrained by factors such as cost and energy consumption. Their “ad-hoc” nature, with direct rather than mediated connections between a network of wireless devices, adds another layer of difficulty. Basing this work entirely on data-driven, coordinated algorithms, the author’s aim is to present location discovery techniques that are highly accurate—and which fit user criteria. The research deploys nonparametric statistical methods and relies on the concept of joint probability to construct error (including location error) models and environmental field models. It also addresses system issues such as the broadcast and scheduling of the beacon. Reporting an impressive accuracy gain of almost 17 percent, and organized in a clear, sequential manner, this book represents a stride forward in wireless localization.

Wireless sensor networks (WSNs) play an important role in applications such as environmental monitoring, source tracking, and health care,... In WSN, sensors have the ability to perform data sampling, distributed computing and information fusion. To perform such complex tasks, clock synchronization and localization are two fundamental and essential algorithms. WSNs have been widely studied in the past years, and the scientific literature reports many outcomes that make them applicable for some applications. For some others, research still needs to find solutions to some of the challenges posed by battery limitation, dynamicity, and low computing clock rate. With the aim of contributing to the research on WSN, this thesis proposes new algorithms for both clock synchronization and localization. For clock synchronization, sensors converge their local physical clock to perform data fusion. By applying the clock synchronization algorithm, sensors converge the time difference and therefore work at the same rate. In view of dynamicity, low computing and sparsity of WSN, a new pulse-coupled decentralized synchronization algorithm is proposed to improve the precision of the synchronization. The benefit of this kind of algorithm is that sensors only exchange zero-bit pulse instead of packets, so not only the communication is efficient but also robust to any failure of the sensors in the network. Localization of sensors has been widely studied. However, the quality and the accuracy of the localization still have a large room to improve. This thesis apply Leave-out Sign-dominant Correlated Regions (LSCR) algorithm to localization problem. With LSCR, one evaluates the accurate estimates of confidence regions with prescribed confidence levels, which provide not only the location but also the confidence of the estimation. In this thesis, several localization approaches are implemented and compared. The simulation result shows under mild assumptions, LSCR obtains competitive results compared to other methods.