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 and List of Contributors
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Ahmed A. Ahmed, Xiaoli Li, Yi Shang, Hongchi Shi
The authors present several network node localization methods that are based on multidimensional scaling (MDS) techniques. Four algorithms are...
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Saikat Ray, Wei Lai, Dong Guo, Ioannis Ch. Paschalidis
The authors present a unified stochastic localization approach that allows a wireless sensor network to determine the physical locations of its...
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Duc A. Tran, XuanLong Nguyen, Thinh Nguyen
A vast majority of localization techniques proposed for sensor networks are based on triangulation methods in Euclidean geometry. They utilize the...
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Moshe Laifenfeld, Ari Trachtenberg, David Starobinski
Various real-life environments are exceptionally harsh for signal propagation, rendering well-known trilateration techniques (e.g. GPS) unsuitable...
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Michael Allen, Sebnem Baydere, Elena Gaura, Gurhan Kucuk
This chapter introduces a methodological approach to the evaluation of localization algorithms. The chapter contains a discussion of evaluation...
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Michael L. McGuire, Konstantinos N. Plataniotis
Node localization is an important issue for wireless sensor networks to provide context for collected sensory data. Sensor network designers need to...
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Reviews and Testimonials
This book covers the major techniques that have been widely used for wireless sensor network localization and capture the most recent developments in the area.
– Guoqiang Mao, University of Sydney, Australia
- Evaluation of localization algorithms
- Global rigidity and localizability
- Localization algorithm development cycle
- Localization in wireless networks
- Location awareness
- Machine learning based localization
- Range and location accuracy
- Robust localization using identifying codes
- Sensor network localization theory and algorithms
- Signal strength for sensor localization
- Statistical location detection
- Wireless sensor network localization
Distributed sensor networks have been discussed for more than 30 years, but the vision of wireless sensor
networks has been brought into reality only by the recent advances in wireless communications and
electronics, which have enabled the development of low-cost, low-power and multi-functional sensors that
are small in size and communicate over short distances. Today, cheap, smart sensors, networked through
wireless links and deployed in large numbers, provide unprecedented opportunities for monitoring and
controlling homes, cities, and the environment. In addition, networked sensors have a broad spectrum
of applications in the defence area, generating new capabilities for reconnaissance and surveillance as
well as other tactical applications.
Localization (location estimation) capability is essential in most wireless sensor network applications.
In environmental monitoring applications such as animal habitat monitoring, bush fire surveillance, water
quality monitoring and precision agriculture, the measurement data are meaningless without an accurate
knowledge of the location from where the data are obtained. Moreover, the availability of location
information may enable a myriad of applications such as inventory management, intrusion detection,
road traffic monitoring, health monitoring, reconnaissance and surveillance.
Wireless sensor network localization techniques are used to estimate the locations of the sensors
with unknown positions in a network using the available a priori knowledge of positions of, typically,
a few specific sensors in the network and inter-sensor measurements such as distance, time difference
of arrival, angle of arrival and connectivity. Sensor network localization techniques are not just trivial
extensions of the traditional localization techniques like GPS or radar-based geolocation techniques.
They involve further challenges in several aspects: (1) a variety of measurements may be used in sensor
network localization; (2) the environments in which sensor networks are deployed are often complicated,
involving urban environments, indoor environments and non-line-of-sight conditions; (3) wireless sensors
are often small and low-cost sensors with limited computational capabilities; (4) sensor network
localization techniques are often required to be implemented using available measurements and with
minimal hardware investment; (5) sensor network localization techniques are often required to be suitable
for deployment in large scale multi-hop networks; and (6) the choice of sensor network localization
techniques to be used often involves consideration of the trade-off among cost, size and localization
accuracy to suit the requirements of a variety of applications. It is these challenges that make localization
in wireless sensor networks unique and intriguing.
This book is intended to cover the major techniques that have been widely used for wireless sensor
network localization and capture the most recent developments in the area. It is based on a number of
stand-alone chapters that together cover the subject matter in a fully comprehensive manner. However,
despite its focus on localization in wireless sensor networks, many localization techniques introduced
in the book can be applied in a variety of wireless networks beyond sensor networks.
The targeted audience for the book includes professionals who are designers and/or planners for
wireless localization systems, researchers (academics and graduate students), and those who would like
to learn about the field. Although the book is not exactly a textbook, the format and flow of information
have been organized such that it can be used as a textbook for graduate courses and research-oriented
courses that deal with wireless sensor networks and wireless localization techniques.
This book consists of 18 chapters. It begins with 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. The other chapters are organized into three parts: measurement techniques, localization
theory, and algorithms, experimental study and applications.
Measurement techniques are of fundamental importance in sensor network localization. It is the type
of measurements employed and the corresponding precision that fundamentally determine the estimation
accuracy of a localization system and the localization algorithm being implemented by this system.
Measurements also determine the type of algorithm that can be used by a particular localization system.
The part on Measurement Techniques includes Chapters II-V, which discuss various aspects of measurement
techniques used in sensor network 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 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 time-of-arrival measurements
and the corresponding mitigation techniques. 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 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.
Chapters VI-XV give an in-depth discussion of the fundamental theory underpinning sensor network
localization and various localization approaches. 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 presents a class of computationally efficient sequential algorithms based on
graph theory for estimating sensor locations using inaccurate distance measurements. Chapter VIII presents
several centralized and distributed localization algorithms based on multidimensional scaling techniques
for implementation in regular and irregular networks. Chapters IX-XI feature a thorough discussion on
theoretical and practical issues involved in the design and implementation of RSS-based localization
algorithms. 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 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 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, for example, 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 presents a machine learning approach to localization.
The applicability of two learning methods, the classification method and the regression model, to RSSbased
localization is discussed. 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. Chapters XIV and XV consider the 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 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.
Chapters XVI, XVII, and XVIII discuss the applications of localization techniques in tracking and
sensor network routing. 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 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 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.
University of Sydney, Australia
National ICT Australia, Australia & Australian National University, Australia
List of ReviewersBrian Anderson, the Australian National University; Australia
Adrian Bishop, KTH Royal Institute of Technology, Sweden
Chun Tung Chou, University of New South Wales, Australia
Soura Dasgupta, the University of Iowa, USA
Kutluyýl Doðançay, University of South Australia, Australia
Jia Fang, Yale University, USA
Tolga Girici, TOBB University of Economics and Technology, Turkey
Fredrik Gustafsson, Linköping University, Sweden
Hatem Hmam, Defence Science and Technology Organisation, Australia
Julien Hendrickx, Université catholique de Louvain, Belgium
Tibor Jordán, Eötvös University, Hungary
Anushiya Kannan, the University of Sydney, Australia
Emre Köksal, the Ohio State University, USA
Ullrich Köthe, the University of Hamburg, Germany
Anthony Kuh, University of Hawaii at Manoa, USA
Lavy Libman, National ICT Australia, Australia
Sarfraz Nawaz, University of New South Wales, Australia
Michael L. McGuire, University of Victoria, Canada
Garry Newsam, Defence Science and Technology Organisation, Australia
M. Özgür Oktel, Bilkent University, Turkey
Neal Patwari, University of Utah, USA
Parastoo Sadeghi, the Australian National University, Australia
Yi Shang, University of Missouri-Columbia, USA
Qinfeng Shi, the Australian National University and National ICT Australia, Australia
Bülent Tavlý, TOBB University of Economics and Technology, Turkey