23 августа 2009 | Автор: Admin | Рубрика: Компьютерная литература » Програм-ние и разработка » Программирование | Комментариев: 0
Guoqiang Mao, Baris Fidan "Localization Algorithms and Strategies for Wireless Sensor Networks"
Information Science Reference | English | 2009-05-15 | ISBN: 1605663964 | 372 pages | PDF | 15,7 MB
Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks.
Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.
Table of Contents:
Chapter I: Introduction to Wireless Sensor Network Localization
Chapter I is an introductory chapter that covers the basic principles of techniques involved in the design and implementation of wireless sensor network localization systems. A focus of the chapter is on explaining how the other chapters are related to each other and how topics covered in each chapter fit into the architecture of this book and the big picture of wireless sensor network localization.
Chapter II: Measurements Used in Wireless Sensor Networks Localization
Chapter II introduces a common framework for analysing the information content of various measurements, which can be used to derive localization bounds for integration of any combination of measurements in the network.
Chapter III: Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking
Chapter III discusses challenges in time-of-arrival measurement techniques and methods to overcome these challenges. A focus of the chapter is on the identification of non-line-of-sight conditions in timeof- arrival measurements and the corresponding mitigation techniques.
Chapter IV: RF Ranging Methods and Performance Limits for Sensor Localization
Chapter IV gives a detailed discussion on the impact of various factors, that is, noise, clock synchronization, signal bandwidth and multipath, on the accuracy of signal propagation time measurements.
Chapter V: Calibration and Measurement of Signal Strength for Sensor Localization
Chapter V features a thorough discussion on a number of practical issues involved in the use of received signal strength (RSS) measurements. In particular, it focuses on the device calibration problem and its impact on localization.
Chapter VI: Graph Theoretic Techniques in the Analysis of Uniquely Localizable Sensor Networks
Chapter VI gives a detailed overview of various tools in graph theory and combinatorial rigidity, many of which are just recently developed, to characterize uniquely localizable networks. A network is said to be uniquely localizable if there is a unique set of locations consistent with the given data, that is, location information of a few specific sensors and inter-sensor measurements.
Chapter VII: Sequential Localization with Inaccurate Measurements
Chapter VII presents a class of computationally efficient sequential algorithms based on graph theory for estimating sensor locations using inaccurate distance measurements.
Chapter VIII: MDS-Based Localization
Chapter VIII presents several centralized and distributed localization algorithms based on multidimensional scaling techniques for implementation in regular and irregular networks.
Chapter IX: Statistical Location Detection
Chapter IX focuses on localization in indoor wireless local area network (WLAN) environments and presents a RSS-based localization system for indoor WLAN environments. The localization problem is formulated as a multi-hypothesis testing problem and an algorithm is developed using this algorithm to identify in which region the sensor resides. A solid theoretical discussion of the problem is provided, backed by experimental validations.
Chapter X: Theory and Practice of Signal Strength-Based Localization in Indoor Environments
Chapter X first presents an analytical framework for ascertaining the attainable accuracy of RSS-based localization techniques. It then summarizes the issues that may affect the design and deployment of RSS-based localization systems, including deployment ease, management simplicity, adaptability and cost of ownership and maintenance. With this insight, the authors present the “LEASE” architecture for localization that allows easy adaptability of localization models.
Chapter XI: On a Class of Localization Algorithms Using Received Signal Strength
Chapter XI surveys and compares several RSS-based localization techniques from two broad categories: point-based and area-based. It is demonstrated that there are fundamental limitations for indoor localization performance that cannot be transcended without using qualitatively more complex models of the indoor environment, e.g., modelling every wall, desk or shelf, or without adding extra hardware in the sensor node other than those required for communication, e.g., very high frequency clocks to measure the time of arrival.
Chapter XII: Machine Learning Based Localization
Chapter XII presents a machine learning approach to localization. The applicability of two learning methods, the classification method and the regression model, to RSS-based localization is discussed.
Chapter XIII: Robust Localization Using Identifying Codes
Chapter XIII presents another paradigm for robust localization based on the use of identifying codes, a concept borrowed from the information theory literature with links to covering and superimposed codes. The approach is reported to be robust and suitable for implementation in harsh environments.
Chapter XIV: Evaluation of Localization Algorithms
Chapter XIV introduces a methodological approach to the evaluation of localization algorithms. The authors argue that algorithms should be simulated, emulated (on test beds or with empirical data sets) and subsequently implemented in hardware, in a realistic WSN deployment environment, as a complete test of their performance.
Chapter XV: Accuracy Bounds for Wireless Localization Methods
Chapter XV looks at evaluation of localization algorithms from a different perspective and takes an analytical approach to performance evaluation. In particular, the authors advocate the use of the Weinstein- Weiss and extended Ziv-Zakai lower bounds for evaluating localization error, which overcome the problem in the widely used Cramer-Rao bound that the Cramer-Rao bound relies on some idealizing assumptions not necessarily satisfied in real systems.
Chapter XVI: Experiences in Data Processing and Bayesian Filtering Applied to Localization and Tracking in Wireless Sensor Networks
Chapter XVI discusses algorithms and solutions for signal processing and filtering for localization and tracking applications. The authors explain some practical issues for engineers interested in implementing tracking solutions and their experiences gained from implementation and deployment of several such systems.
Chapter XVII: A Wireless Mesh Network Platform for Vehicle Positioning and Location Tracking
Chapter XVII presents an experimental study on the integration of Wi-Fi based wireless mesh networks and Bluetooth technologies for detecting and tracking travelling cars and measuring their speeds for road traffic monitoring in intelligent transportation systems.
Chapter XVIII: Beyond Localization: Communicating Using Virtual Coordinates
Chapter XVIII discusses an interesting aspect of the geographic routing problem. The authors propose the use of virtual coordinates, instead of physical coordinates, of sensors for improved geographic routing performance. This chapter motivates us to think beyond the horizon of localization and invent smarter ways to label sensors and measurement data from sensors to facilitate applications that do not rely on the knowledge of physical locations of sensors.
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