SENSOR NETWORKOPERATIONS

Edited by

Shashi Phoha

Thomas LaPorta

Christopher Griffin

IEEE PRESS

A JOHN WILEY & SONS, INC PUBLICATION

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SENSOR NETWORKOPERATIONS

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Piscataway, NJ 08854

IEEE Press Editorial BoardMohamed E. El-Hawary, Editor-in-Chief

M. Akay R. J. Herrick F. M. B. PereiraJ. B. Anderson S. V. Kartalopoulos C. SinghJ. E. Brewer M. Montrose G. ZobristT. G. Croda M. S. Newman

Kenneth Moore, Director of IEEE Book and InformationCatherine Faduska, Acquisitions Editor, IEEE Press

Jeanne Audino, Project Editor, IEEE Press

SENSOR NETWORKOPERATIONS

Edited by

Shashi Phoha

Thomas LaPorta

Christopher Griffin

IEEE PRESS

A JOHN WILEY & SONS, INC PUBLICATION

Copyright C© 2006 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc.Published simultaneously in Canada.

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To Him in whose presence to present oneself is to find oneself.—Shashi Phoha

I dedicate this book to Lisa, Abigail, and Sophia.—Thomas F. LaPorta

For Amy, my darling wife, who put up with me during this project. I love youmore than you can imagine.

—CHG

CONTENTS

PREFACE xiii

CONTRIBUTORS xv

I SENSOR NETWORK OPERATIONS OVERVIEW 1

1 Overview of Mission-Oriented Sensor Networks 3

1.1 Introduction / 31.2 Trends in Sensor Development / 41.3 Mission-Oriented Sensor Networks: Dynamic Systems Perspective / 8References / 10

II SENSOR NETWORK DESIGN AND OPERATIONS 11

2 Sensor Deployment, Self-Organization, and Localization 13

2.1 Introduction / 132.2 SCARE: A Scalable Self-Configuration and Adaptive

Reconfiguration Scheme for Dense Sensor Networks / 142.3 Robust Sensor Positioning in Wireless Ad Hoc Sensor Networks / 352.4 Trigonometric k Clustering (TKC) for Censored Distance Estimation / 512.5 Sensing Coverage and Breach Paths in Surveillance Wireless

Sensor Networks / 68References / 86

3 Purposeful Mobility and Navigation 91

3.1 Introduction / 913.2 Controlled Mobility for Efficient Data Gathering in Sensor

Networks with Passively Mobile Nodes / 923.3 Purposeful Mobility in Tactical Sensor Networks / 1133.4 Formation and Alignment of Distributed Sensing Agents with

Double-Integrator Dynamics and Actuator Saturation / 126

vii

viii CONTENTS

3.5 Modeling and Enhancing the Data Capacity of Wireless SensorNetworks / 157

References / 179

4 Lower Layer Issues—MAC, Scheduling, and Transmission 185

4.1 Introduction / 1854.2 SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for

Sensor Networks / 1864.3 Comprehensive Performance Study of IEEE 802.15.4 / 2184.4 Providing Energy Efficiency for Wireless Sensor Networks

Through Link Adaptation Techniques / 237References / 257

5 Network Routing 263

5.1 Introduction / 2635.2 Load-Balanced Query Protocols for Wireless Sensor Networks / 2645.3 Energy-Efficient and MAC-Aware Routing for Data Aggregation in

Sensor Networks / 2915.4 LESS: Low-Energy Security Solution for Large-scale Sensor

Networks Based on Tree-Ripple-Zone Routing Scheme / 308References / 329

6 Power Management 337

6.1 Introduction / 3376.2 Adaptive Sensing and Reporting in Energy-Constrained Sensor

Networks / 3386.3 Sensor Placement and Lifetime of Wireless Sensor Networks:

Theory and Performance Analysis / 3546.4 Algorithms for Maximizing Lifetime of Battery-Powered Wireless

Sensor Nodes / 3676.5 Battery Lifetime Estimation and Optimization for Underwater

Sensor Networks / 397References / 416

7 Distributed Sensing and Data Gathering 421

7.1 Introduction / 4217.2 Secure Differential Data Aggregation for Wireless Sensor Networks / 4227.3 Energy-Conserving Data Gathering Strategy Based on Trade-off

Between Coverage and Data Reporting Latency in Wireless SensorNetworks / 442

7.4 Quality-Driven Information Processing and Aggregation inDistributed Sensor Networks / 467

CONTENTS ix

7.5 Progressive Approach to Distributed Multiple-Target Detection inSensor Networks / 486

References / 504

8 Network Security 509

8.1 Introduction / 5098.2 Energy Cost of Embedded Security for Wireless Sensor Networks / 5108.3 Increasing Authentication and Communication Confidentiality in

Wireless Sensor Networks / 5228.4 Efficient Pairwise Authentication Protocols for Sensor and Ad Hoc

Networks / 5358.5 Fast and Scalable Key Establishment in Sensor Networks / 5578.6 Weil Pairing-Based Round, Efficient, and Fault-Tolerant Group

Key Agreement Protocol for Sensor Networks / 571References / 580

III SENSOR NETWORK APPLICATIONS 587

9 Pursuer–Evader Tracking in Sensor Networks 589

9.1 Introduction / 5899.2 The Problem / 5909.3 Evader-Centric Program / 5929.4 Pursuer-Centric Program / 5959.5 Hybrid Pursuer–Evader Program / 5969.6 Efficient Version of Hybrid Program / 5999.7 Implementation and Simulation Results / 6009.8 Discussion and Related Work / 605

References / 607

10 Embedded Soft Sensing for Anomaly Detection in MobileRobotic Networks 609

10.1 Introduction / 60910.2 Mobile Robot Simulation Setup / 61410.3 Software Anomalies in Mobile Robotic Networks / 61510.4 Soft Sensor / 61610.5 Software Anomaly Detection Architecture / 61610.6 Anomaly Detection Mechanisms / 61810.7 Test Bed for Software Anomaly Detection in Mobile Robot Application / 61910.8 Results and Discussion / 62310.9 Conclusions and Future Work / 626Appendix A / 626Appendix B / 627References / 628

x CONTENTS

11 Multisensor Network-Based Framework for VideoSurveillance: Real-Time Superresolution Imaging 631

11.1 Introduction / 63211.2 Basic Model of Distributed Multisensor Surveillance System / 63211.3 Superresolution Imaging / 63611.4 Optical Flow Computation / 63811.5 Superresolution Image Reconstruction / 64411.6 Experimental Results / 64411.7 Conclusion / 645References / 646

12 Using Information Theory to Design Context-SensingWearable Systems 649

12.1 Introduction / 64912.2 Related Work / 65112.3 Theoretical Background / 65112.4 Adaptations / 65412.5 Design Considerations / 66212.6 Case Study / 66312.7 Results / 66612.8 Conclusion / 674Appendix / 674References / 675

13 Multiple Bit Stream Image Transmission over WirelessSensor Networks 677

13.1 Introduction / 67713.2 System Description / 67913.3 Experimental Results / 68513.4 Summary and Discussion / 686References / 687

14 Hybrid Sensor Network Test Bed for Reinforced Target Tracking 689

14.1 Introduction / 68914.2 Sensor Network Operational Components / 69014.3 Sensor Network Challenge Problem / 69414.4 Integrated Target Surveillance Experiment / 69514.5 Experimental Results and Evaluation / 69814.6 Conclusion / 701References / 703

15 Noise-Adaptive Sensor Network for Vehicle Tracking in the Desert 705

15.1 Introduction / 70615.2 Distributed Tracking / 708

CONTENTS xi

15.3 Algorithms / 71015.4 Experimental Methods / 71215.5 Results and Discussion / 71415.6 Conclusion / 715References / 715

ACKNOWLEDGMENTS 717

INDEX 719

ABOUT THE EDITORS 723

PREFACE

In recent years, interest in sensor networks has evolved from a subject solely of researchto one of deployed systems. As new applications have arisen that use sensor networks, thedemands on these networks have grown. One of the main challenges with these networks ishow to manage missions from application requirements to network operation. By their na-ture, most sensor networks are mission specific. They therefore place specific requirementson network systems that in turn dictate the use of certain algorithms.

In this book we present the state-of-the art on methods for sensor network operations. Thisincludes algorithms for configuring networks to meet mission requirements and applicationsin the real world.

FOCUS

The focus of this book is on sensor network operations, starting from translating missionspecifications into operational algorithms and applications. The technologies presented aregeneral in terms of the types of transducers and data being gathered.

INTENDED AUDIENCE

This book is intended for researchers and practitioners interested in any aspect of sensornetwork operations. For researchers, this book serves as a starting point into research inareas such as algorithms for sensor placement, power management, routing, or applicationsof sensor networks. It may serve as a comprehensive tutorial or be used as the basis fornew research projects. For practitioners, this book contains many algorithms, protocols, andexperiments that will prove useful in the design and deployment of mission-specific sensornetworks.

ORGANIZATION

This book is organized into three parts. The first part provides the motivation and overviewof the book and gives a background on sensor platforms and mission-oriented sensor net-works. The second part of the book is arranged into seven chapters and presents many

xiii

xiv PREFACE

algorithms for controlling sensor networks. This includes deployment and localization,mobility management, low layer protocols, routing, power management, data gathering anddissemination, and security. The third part of the book presents several sensor networkapplications and illustrates how sensor networks may be used.

SHASHI PHOHATHOMAS LAPORTACHRISTOPHER GRIFFIN

January 2006

CONTRIBUTORS

Gordon B. Agnew, University of Waterloo, Ontario, Canada

Anish Arora, Ohio State University, Computer Science & Engineering, Columbus, Ohio

Mahesh Arumugam, Michigan State University, East Lansing, Michigan

Saurabh Bagchi, Purdue University, West Lafayette, Indiana

Kiran S. Balagani, Pennsylvania State University, Mechanical Engineering, StateCollege, Pennsylvania

N. Balakrishnan, Indian Institute of Science, Supercomputing Research Center,Bangalore, Karnataka, India

Pierre Baldi, Center for Pervasive Communications and Computing, Irvine, California

Steve Beck, University of Tennessee, Knoxville, Tennessee

Pratik K. Biswas, Pennsylvania State University, State College, Pennsylvania

Hasan Çam, Arizona State University, Computer Science and Engineering, Tempe,Arizona

Guohong Cao, Pennsylvania State University, State College, Pennsylvania

Krishnendu Chakrabarty, Duke University, Electrical and Computer Engineering,Durham, North Carolina

Chang Wen Chen, Florida Institute of Technology, Electrical Computer Engineering,Melbourne, Florida

Wook Choi, University of Texas at Arlington, Computer Science and Engineering,Arlington, Texas

Kaviraj Chopra, University of Arizona, Electrical and Computer Engineering, Tucson,Arizona

Song Ci, University of Massachusetts, Boston, Massachusetts

Sajal K. Das, University of Texas at Arlington, Computer Science and Engineering,Arlington, Texas

Sridhar Dasika, University of Arizona, Electrical and Computer Engineering, Tucson,Arizona

Hakan Deliç, Bogazici University, Bebek, Istanbul, Turkey

Murat Demirbas, Ohio State University, Computer Science & Engineering, Columbus,Ohio

xv

xvi CONTRIBUTORS

Roberto Di Pietro, Università, di Roma, Dipartimento di Informatica, Roma, Italy

Tassos Dimitriou, Athens Information Technology, Athens, Greece

Cem Ersoy, Bogazici University, Bebek, Istanbul, Turkey

Mohamed Gouda, University of Texas at Austin, Computer Sciences, Austin, Texas

Stanley Grant, California Institute for Telecommunications and InformationTechnology, La Jolla, California

Eric Grele, Pennsylvania State University, State College, Pennsylvania

Christopher Griffin, Pennsylvania State University, State College, Pennsylvania

Bechir Hamdaoui, University of Wisconsin, Madison, Wisconsin

Sharif Hamid, University of Nebraska–Lincoln, Computer and Electronics Engineering,Omaha, Nebraska

Kristin Herlugson, Washington State University, Pullman, Washington

Anh Tuan Hoang, National University of Singapore, Electrical & Computer Engineering,Singapore

Alireza Hodjat, UCLA, Electrical Engineering, Los Angeles, California

Fei Hu, Rochester Institute of Technology, Computer Engineering Department, Rochester,New York

Tzonelih Hwang, National Cheng Kung University, Department of Computer Science andInformation Engineering, Tainan, Taiwan

S. S. Iyengar, Louisiana State University, Computer Science Department, Baton Rouge,Louisiana

Ekta Jain, University of Texas at Arlington, Electrical Engineering, Arlington, Texas

Xiang Ji, Pennsylvania State University, Department of Computer Science and Engineer-ing, Universtiy Park, Pennsylvania

Holger Junker, Wearable Computing Lab, EE, Zurich, Switzerland

Raja Jurdak, University of California, Irvine, Information and Computer Science, Irvine,California

G. Kesidis, Pennsylvania State University, State College, Pennsylvania

John Koch, Pennsylvania State University, State College, Pennsylvania

Ioannis Krontiris, Athens Information Technology, Athens, Greece

Sandeep Kulkarni, Michigan State University, East Lansing, Michigan

Thomas LaPorta, Pennsylvania State University, State College, Pennsylvania

Bennett Lau, Purdue University, West Lafayette, Indiana

Ha V. Le, Vientnam National University, Computer Science Department, Hanoi, Vietnam

Duke Lee, UC Berkeley, Electrical Engineering and Computer Science, Berkeley,California

Myung J. Lee, City College of New York, Electrical Engineering, New York, NewYork

CONTRIBUTORS xvii

Tian-Fu Lee, National Cheng Kung University, Department of Computer Science andInformation Engineering, Tainan, Taiwan

Zhiyuan Li, Purdue University, West Lafayette, Indiana

Jie Lian, University of Waterloo, Ontario, Canada

Qilian Liang, University of Texas at Arlington, Electrical Engineering, Arlington, Texas

R. Logananthraj, University of Louisiana at Lafayette, Center for Advanced ComputerStudies, Lafayette, Louisiana

Cristina Videira Lopes, University of California, Irvine, Chemical Engineering andMaterials Science, Irvine, California

Yung-Hsiang Lu, Purdue University, West Lafayette, Indiana

Stefan Lucks, NEC Europe Ltd., Network Laboratories, Heidelberg, Germany andMannheim University, Computer Science, Mannheim, Germany

Paul Lukowicz, Electronics Laboratory, EE, Zurich, Switzerland

Bharat Madan, Pennsylvania State University, State College, Pennsylvania

Nipoon Malhotra, Purdue University, West Lafayette, Indiana

Luigi V. Mancini, University of Rome, Department of Information, Rome, Italy

Carter May, Rochester Institute of Technology, Computer Engineering Department,Rochester, New York

Alessandro Mei, University of Rome, Department of Information, Rome, Italy

Mehul Motani, Institute for Infocomm Research, Singapore

Amit U. Nadgar, Pennsylvania State University, Mechanical Engineering, State College,Pennsylvania

Kshirasagar Naik, University of Waterloo, Ontario, Canada

Prashant Nair, Arizona State University, Computer Science and Engineering, Tempe,Arizona

Fotios Nikakis, Athens Information Technology, Athens, Greece

Krishna Nuli, University of Nebraska—Lincoln, Computer and Electronics Engineering,Omaha, Nebraska

Ertan Onur, Bogazici University, Bebek, Istanbul, Turkey

Suat Ozdemir, Arizona State University, Computer Science and Engineering, Tempe,Arizona

Symeon Papavassiliou, New Jersey Institute of Technology, Electrical and ComputerEngineering, Newark, New Jersey

Shashi Phoha, Pennsylvania State University, University Park, Pennsylvania

Vir V. Phoha, Louisiana Tech University, Computer Science, Ruston, Louisiana

Raviraj Prasad, University of Nebraska—Lincoln, Computer and Electronics Engineer-ing, Omaha, Nebraska

Hairong Qi, University of Tennessee, Knoxville, Tennessee

xviii CONTRIBUTORS

Parmeswaran Ramanathan, University of Wisconsin, Madison, Wisconsin

Vaithiyam Ramesh, Rochester Institute of Technology, Rochester, New York

Hamid Sharif, University of Nebraska, Lincoln, Nebraska

Asok Ray, Pennsylvania State University, Mechanical Engineering, University Park,Pennsylvania

Sandip Roy, Washington State University, Pullman, Washington

Harshavardhan Sabbineni, Duke University, Electrical and Computer Engineering,Durham, North Carolina

Ali Saberi, Washington State University, Pullman, Washington

H. Ozgur Sanli, Arizona State University, Computer Science and Engineering, Tempe,Arizona

Vrudhula Sarma, University of Arizona, Electrical and Computer Engineering, Tucson,Arizona

Guna Seetharaman, Air Force Institute of Technology, Department of Electrical andComputer Engineering, Wright Patterson AFB, Ohio

Sivakumar Sellumuthu, Wayne State University, Computer Science, Detroit, Michigan

Sengupta Raja, UC Berkeley, Electrical Engineering and Computer Science, Berkeley,California

Kewei Sha, Wayne State University, Computer Science, Detroit, Michigan

Weisong Shi, Wayne State University, Computer Science, Detroit, Michigan

Waqaas Siddiqui, Rochester Institute of Technology, Rochester, New York

Yuldi Tirta, Purdue University, West Lafayette, Indiana

Gerhard Troester, Wearable Computing Lab, EE, Zurich, Switzerland

Pravin Varaiya, UC Berkeley, Civil Engineering, Berkeley, California

Raviteja Varanasi, Pennsylvania State University, Mechanical Engineering, StateCollege, Pennsylvania

Ingrid Verbauwhede, UCLA, Electrical Engineering, Los Angeles, California

Xiaoling Wang, University of Tennessee, Knoxville, Tennessee

André Weimerskirch, Ruhr-University Bochum, Communication Security, Bochum,Germany

Hsiang-An Wen, National Cheng Kung University, Department of Computer Science andInformation Engineering, Tainan, Taiwan

Dirk Westhoff, NEC Europe Ltd., Network Laboratories, Heidelberg, Germany andMannheim University, Computer Science, Mannheim, Germany

Min Wu, University of Missouri–Columbia, Electrical Engineering, Columbia, Missouri

Jie Yang, New Jersey Institute of Technology, Electrical and Computer Engineering,Newark, New Jersey

Bin Yao, Pennsylvania State University, State College, Pennsylvania

CONTRIBUTORS xix

Erik Zenner, NEC Europe Ltd., Network Laboratories, Heidelberg, Germany andMannheim University, Computer Science, Mannheim, Germany

Hongyuan Zha, Pennsylvania State University, Department of Computer Science andEngineering, State College, Pennsylvania

Jianliang Zheng, City College of New York, Electrical Engineering, New York, New York

Jin Zhu, New Jersey Institute of Technology, Electrical and Computer Engineering,Newark, New Jersey

ISENSOR NETWORK

OPERATIONS OVERVIEW

1OVERVIEW OF

MISSION-ORIENTEDSENSOR NETWORKS

1.1 INTRODUCTION

Sensor networks represent a new frontier in technology that holds the promise of unprece-dented levels of autonomy in the execution of complex dynamic missions by harnessingthe power of many inexpensive electromechanical microdevices. Miniature sensing andcomputational devices, often embedded in wireless electromechanical platforms, are be-ing developed to interact directly with the physical world. Spanning time and space, andcognizant of a common mission, they monitor changes in the operational environment andcollaborate to actuate distributed tasks in dynamic and uncertain environments. Dispersedover a hostile battlefield, these devices may self-organize to act as numerous eyes and earsof soldiers surveying the field from a safe distance. Embedded in unmanned air vehicles,they may monitor bio/chemical plumes in the atmosphere or handle hazardous materialson the ground. Mobile robots with embedded sensor systems explore the surface of Mars;and integrated systems of undersea robots are being designed to hunt for mines in shallowwater and to develop high fidelity now casts and forecasts of the ocean through time–spacecoordinated sampling. Sensor networks are expected to play an important role in transporta-tion management and safety and in medical applications. More commonplace applicationsinclude fine-grain monitoring of indoor environments, buildings, and home appliances. Ingeneral, the next phase of automation calls on networks of sensors to take on the dull,dirty, and dangerous functions of human interest, accomplishing them with the perceptionand adaptation of humans, in collaboration with humans. As a system of interacting sen-sor nodes, a sensor network is a human-engineered, complex dynamic system that mustcombine its understanding of the physical world with its computational and control func-tions and operate with constrained resources. As a distributed dynamic system, these tinydistributed devices must collectively comprehend the time evolution of physical and oper-ational phenomena and predict their effects on mission execution and then actuate controlactions that execute common high-level mission goals.

Sensor Network Operations, Edited by Phoha, LaPorta, and GriffinCopyright C© 2006 The Institute of Electrical and Electronics Engineers, Inc.

3

4 OVERVIEW OF MISSION-ORIENTED SENSOR NETWORKS

This book presents new advances for engineering and operating sensor networks tomeet specified mission goals. Prior to deployment, these mission-oriented sensor networks(MoSNs) need to be endowed with distributed high-level representations of mission specifi-cations that can be dynamically executed by harnessing the collective powers of distributedsensor/actuator nodes in unknown or uncertain environments. Collaborative intelligent in-ference is necessary to circumvent limitations of sensor data, communications, and equip-ment faults. Emergent behaviors and phase transitions must be modeled, predicted, andcontrolled.

1.2 TRENDS IN SENSOR DEVELOPMENT

Shashi Phoha and Thomas LaPorta

Sensors of physical phenomena with integrated servomechanisms have been commonplacethroughout the latter half of the twentieth century controlling thermostats and valves, mon-itoring flow or adapting to changes in pressure or stress, and providing alarms for fire orflooding. As dynamic systems, they have been expected to perform these and many otherlocalized isolated tasks with precision and reliability. These applications relied on staticallypositioned sensors designed to operate independently for long periods of time (months toyears) with nonrenewable power supplies. Traditional sensor technology was characterizedby large transducers, highly capable processing platforms, and complex signal and dataprocessing software. These characteristics limited the types of applications that could makeuse of sensor technology. Sensor technology has matured resulting in smaller and moreefficient transducers, processing platforms, and communication modules. In addition, thecommunications capabilities of sensors have greatly improved to allow large-scale networksof sensors to be formed. These advancements have paved the way for a much broader setof applications of sensors.

The present-day demands on sensor networks entail comprehensive perception of locallysensed changes in the physics of the environment and adaptive time–space coordinatedcontrol of individual servomechanisms in support of a common mission [1]. The state-of-the-art in sensor technology now supports extremely small sensors that may be highly mobileand power efficient and are equipped with sufficient computing capabilities to run distributedalgorithms to manage their motion, process data, and form and manage networks. As aresult, algorithms for managing sensor networks, and the applications that use them, havegrown increasingly complex. In this book we provide a collection of studies that represent acomprehensive treatment of the current state of research with respect to sensor networks. Weprovide a brief overview of the state-of-the art in sensor platforms and algorithms dealingwith the computational infrastructure issues for sensor networks.

1.2.1 Sensor Platforms

Sensor platforms are comprised of four main components: transducers, a hardware com-puting platform, an operating system, and communication modules. The transducers areresponsible for monitoring an area of interest and gathering data. The computing platformand operating system are responsible for processing and formatting data received from thetransducers so that it is useful to an application that is analyzing data from the sensor field.

1.2 TRENDS IN SENSOR DEVELOPMENT 5

These modules also run control algorithms to move sensors, form networks, aggregate data,and perform security functions. The hardware computing platform typically consists of acentral processing unit (CPU), memory, and input/output (I/O) ports. The operating systemruns on the computing hardware and is used to provide a software interface to the hardwareand to provide a degree of programmability. The communication module has two functions.First, it provides an interface for the transducers to transmit their gathered data into thecomputing platform. Second, it is used to transmit data back to a server where it is analyzedalong with data received from other sensors. This module may include multiple I/O inter-faces. Today, wireless interfaces are becoming the dominant communication technology forsensor networks because of their ease of deployment and reduced cost.

There are a tremendous number of research efforts on transducer technologies thatare beyond the scope of this book. Overviews of some major efforts can be found athttp://www.cens.ucla.edu/ and http://www.el.utwente.nl/tt/. This research has resulted inminiature transducers for sensing many types of phenomena, thus placing the onus on thedesigners of computing platforms, operating systems, and communication modules to re-duce the size and cost of their components so that entire sensor packages are small. Inthe following subsections we review the prevailing technology in the hardware computingplatform, operating systems, and communication modules.

Computing Hardware By far, the most popular processing platform for small sensordevices is based on the so-called Mote hardware that was developed at the University ofCalifornia at Berkeley. This family of hardware platform has been productized by Crossbow(www.xbow.com) as the MICA product line. The platforms are characterized by small size,power efficiency, and very limited CPU and memory capabilities when compared to conven-tional processing platforms, such as desktop personal computers (PCs). However, despitetheir limitations, they provide a highly capable system for developing sensor applicationsin a form factor that may be used in many harsh, inaccessible environments.

The MICA 2 Mote weighs 0.7 ounces and is 58 × 32 × 7 mm, making it ideal for de-ployment for many applications that require very small sensors. The MICA 2 has 128 kbytesof program memory and 512 kbytes of memory for storing samples. The MICA 2 has a10-bit analog-to-digital (A/D) converter, so it can store over 100,000 samples in its mem-ory. The MPR400CB processor board runs the communications and networking protocolssimultaneously with application software. The MICA 2 has a 51-pin expansion connectorto allow it to interface with many types of external transducers. It also supports severalinternal transducer cards. It draws 8 mA while active, and less than 15 µA while in sleepmode. We will discuss the radio capabilities of the MICA 2 in the next subsection, but itis designed to be deployed in large-scale sensor networks of over 1000 nodes. If a smallerplatform is required, the MICA 2DOT has capabilities similar to the MICA 2, but has aform factor of approximately the size of a quarter, or a thickness of 6 mm and a diameterof 25 mm. The major difference between the MICA 2 and the MICA 2DOT is that theMICA 2DOT offers far fewer I/O connections. It has 18 pins for connecting external pe-ripherals. It is clear from the description of the hardware computing platform, that while thepresence of a CPU and memory allows for many types of algorithm to execute, they mustbe specially designed to account for the hardware limitations. We illustrate this point withthree examples. First, consider security. In most Internet environments, security is providedusing encryption using either DES or AES. The DES algorithm requires about 20 kbytesof memory to store the program if written in C, and approximately another 20 kbytes of

6 OVERVIEW OF MISSION-ORIENTED SENSOR NETWORKS

memory to store the variable used during its run-time operation. Therefore, it would occupyalmost one-third of the available memory on the platform, making it infeasible to use. Solu-tions include using hardware support for encryption/decryption or using simpler algorithms.These choices present trade-offs in terms of hardware complexity, power consumption, andoverall strength of security. Second, consider routing. In an Internet environment, routing isperformed using proactive protocols that exchange link state or distance vectors, requiringlarge tables to be stored in individual routers. These tables included next-hop routes for alldestinations. In a large sensor network, if sensor nodes forward data for each other, thesetables will become prohibitively large to store on memory-limited sensor nodes. Therefore,new routing algorithms and protocols must be developed.

Finally, the operating system itself is typically several megabytes on a conventionalcomputing platform. Given the limited memory on a Mote, new operating systems mustbe defined, as discussed in the next subsection. Another example of very simple sensornodes are RFID tags, which are often passive devices with no power or computing capabil-ities. Active badges, such as those developed in the iBadge project (http://nesl.ee.ucla.edu/projects/ibadge/) at UCLA are another example. The iBadge is 2.3 ounces and has a lifetimeof over 4 h. It uses BlueTooth for radio communication and has on-board localization andspeech processing capabilities.

In addition to simple end devices, much more capable sensor computing platformsexist. These are typically used as gateways to aggregate traffic from simple sensors to abackbone network or operate in controlled environments with persistent power supplies. Oneexample is the Crossbow Stargate XScale Network Interface and Single Board Computer.The Stargate runs the Linux operating system and provides USB and PCMCIA and Ethernetinterfaces. Another example is the Sensoria (http://www.sensoria.com/) sGate. Like theStargate, the sGate runs Linux. It has a 32-bit 300-MIPs processor. Essentially, these aregeneral-purpose processors that can perform complex functions to support security, routing,and data processing.

Operating Systems Operating systems for sensor nodes must be very lightweight andoccupy only a small amount of memory. Because sensor applications have many commoncharacteristics, the operating system design can be very specialized. The operating systemsmost commonly used across a wide range of sensor platforms is the TinyOS, which wasdeveloped as part of the Smart Dust project at Berkeley, the same project that led to theMote. While the Mote has been productized by Crossbow, the TinyOS is maintained asopen source by the research group at Berkeley and has a very large user community. Detailsof the TinyOS, the source code, and a list of platforms that support its use are available athttp://webs.cs.berkely.edu/tos.

The TinyOS is designed to support event-driven applications. In addition, it supportsconcurrency so that many events may be monitored simultaneously. These two character-istics are the most important user features of the OS. It is designed to run with minimalsupport from hardware, thus enabling sensor computing platforms to use simple, low-powerdevices. TinyOS supports programming in a language very similar to C. More capable sen-sor nodes, such as the Stargate and sGate discussed above, often use off-the-shelf operatingsystems, such as Linux.

Communication Modules As stated earlier, the communication modules of sensorplatforms support both reading data from transducers and communication links that are

1.2 TRENDS IN SENSOR DEVELOPMENT 7

used to form a network for passing sensor data back to a server for processing. Wireless isthe most popular media for sensor networks. Much research is still ongoing to determine thebest wireless communications technology and low layer access protocols to be used in sen-sor networks. Considerations include transmission range, power consumption, bandwidth,and traffic types to be supported. Whereas many sensor applications of disparate type havemigrated to the Mote platform and TinyOS for a computing platform, because these appli-cations have vastly different data transmission requirements, several radio technologies arestill under consideration. For example, many sensor applications assume that sensors willbe densely deployed and that low-bit-rate telemetry or event reporting will be transmittedacross the network. For these applications, a low-power, low-bit-rate radio suffices becausesensors may relay traffic for each other, and not much data is being transmitted. On theother hand, sensor networks that support applications that include the transmission of im-ages or video streams when an event of interest is detected, must support the transmissionof high-bit-rate, bursty data. These sensors require the use of radios more typical of wirelesslocal area networks. Because power consumption of wireless transmission may be high, theradio interfaces tend to be more specialized with respect to applications than the computinghardware platform or operating system.

The MICA 2 sensors use radios that operate in the ISM band, specifically at 868, 916,315, or 315 MHz. Depending on the model, between 4 and 50 channels are supported on asingle platform. Data is transmitted at 38.4 kbaud using Manchester encoding. These radioswork at low power, 25 to 27 mA, for transmitting at maximum power, 8 to 10 mA to receive,and less than 1 µA while in sleep mode. Their outdoor transmission range is 500 to 1000 ft.One ongoing research effort to produce a much lower power radio is the PicoRadio projectat Berkeley. Details can be found at http://bwrc.eecs.berkeley/Research/PicoNet. The goalof this project is to produce a radio that costs less than 50 cents and draws less than 5 nJ percorrectly transmitted bit. In fact, the goal is to design the overall node to be so low power thatit can scavenge energy from the environment through vibrations or other means. A seconddirection for radio advancements for sensor networks is through the 802.15.4 standard.Ember (http://www.ember.com/) has a commercially available version of a radio designedfor sensor networks based on this technology. The radio is 7 × 7 mm, has a range of 75 m,and supports 128-bit AES encryption. The radio operates in the 2.4 GHz ISM band andsupports up to 16 channels with 5-MHz spacing per channel. Data is transmitted at 250 kbpsusing OQPSK Direct Sequence Spread Spectrum. The power consumption is similar to thatof the MICA 2 radio 20.7 mA to transmit, 19.7 mA to receive, and 0.5 µA while idle. Otherwireless interfaces are also popular in sensor networks, including well-known standardssuch as BlueTooth and 802.11.

Sensor Platform Summary As we have discussed, a very small form factor for sensornodes is critical for many applications. To meet these requirements great innovations havebeen applied to transducers, computing hardware, operating system, and communicationdesign. These systems are now commercially available from several companies. With theability to support more complex applications, more complex algorithms to support theseapplications are required to run in the sensor nodes. Even with the advances in sensorplatform technology, the resulting platforms are still quite limited compared to desktop andserver computing platforms. For this reason, much research is ongoing in designing andimplementing these algorithms with high efficiency.

8 OVERVIEW OF MISSION-ORIENTED SENSOR NETWORKS

1.2.2 Sensor Network Algorithms

Many algorithms and protocols execute in sensor nodes to fulfill the mission of the net-worked sensing system. These algorithms must first enable dispersed sensors to form anetwork, determine their locations, and reconfigure or perhaps move to reposition so thatthe system may fulfill its mission. They must allow sensor nodes to efficiently gather data,access transmission media, communicate information to distant nodes, and disseminate in-formation that has been learned. Depending on the type of application, different levels ofsecurity must be provided to protect the integrity and privacy of the data being gatheredand disseminated. Finally, all of these algorithms must be designed with power efficiencyin mind.

1.3 MISSION-ORIENTED SENSOR NETWORKS: DYNAMICSYSTEMS PERSPECTIVE

Shashi Phoha

For executing complex time-critical missions, a sensor network may be viewed as a dis-tributed dynamic system with dispersed interacting smart sensing and actuation devicesthat may be embedded in mobile or stationary platforms. A sensor network operates onan infrastructure for sensing, computation, and communications, through which it per-ceives the time evolution of physical dynamic processes in its operational environment.A mission-oriented sensor network (MoSN) is such a dynamic system that has also beenendowed with a high-level description of the goals of a specific mission. The MoSN nodesaccept inputs from interacting nodes for situation awareness and participate in individualor cluster-wide dynamic adaptation to meet mission goals. Advances in integrated wirelesscommunications, fast servocontrolled sensors/actuators, and micro- and nanotechnologies,have enabled large-scale integration of inexpensive computational and sensing devices thatcan be spatially dispersed for distributed monitoring of physical phenomena. With intel-ligent mechanisms for self-organization and adaptation, the sensor network can take onmany functions of human interest with the perception and adaptation of humans. The inter-active nonlinear and multi-time-frame dynamics of the resulting systems can approach thecomplexity of biological systems.

Part II of the book covers recent research developments relating to the computational,communications, and networking designs of MoSNs that provide an adaptive infrastructurefor dependable data collection for real-time control and actuation. In harnessing the truepotential of networked sensors, a perceptive infrastructure is needed that adapts to the dy-namics of the mission. The infrastructure enables these dynamically self-reconfigurable andintrospective networks of possibly mobile sensor nodes to be capable of understanding andinterpreting mission objectives and adapting to the dynamics of harsh and often unknownphysical environments. These tiny distributed devices must collectively comprehend thetime evolution of physical phenomena and their effect on mission execution to close thedistributed feedback control loop.

Part III of the book presents a wide range of pragmatic applications that are enabled bysensor networks. Multiple types of sensors are involved: acoustic, video, wearable context-sensitive sensor nodes, and even multimodal sensor nodes. A system of wearable sensorsis described for context recognition in human subjects. An unmanned underwater sensor