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COGNITIVE NETWORKS COGNITIVE NETWORKS Towards Self-Aware Networks Edited by Qusay H. Mahmoud University of Guelph, Canada Copyright 2007 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 Email (for orders and customer service enquiries): cs-books@wiley.co.uk Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to permreq@wiley.co.uk, or faxed to (+44) 1243 770620. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The Publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the Publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Other Wiley Editorial Offices John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USA Wiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, Germany John Wiley & Sons Australia Ltd, 42 McDougall Street, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons Canada Ltd, 6045 Freemont Blvd, Mississauga, Ontario, L5R 4J3, Canada Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Anniversary Logo Design: Richard J. Pacifico Library of Congress Cataloging-in-Publication Data: Cognitive networks : towards self-aware networks / edited by Qusay H. Mahmoud. p. cm. ISBN 978-0-470-06196-1 (cloth) 1. Software radio. 2. Wireless communication systems. 3. Autonomic computing. I. Mahmoud, Qusay H., 1971- TK5103.4875.C62 2007 621.384 – dc22 2007011302 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-470-06196-1 (HB) Typeset in 10/12pt Times by Laserwords Private Limited, Chennai, India Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire This book is printed on acid-free paper responsibly manufactured from sustainable forestry in which at least two trees are planted for each one used for paper production. To the authors of the individual chapters, without whom this book would not exist. And to readers of this book, who will be the future innovators in the field Contents Contributors xi Foreword 1 xv Foreword 2 xix Preface xxi Acknowledgements xxiii Introduction xxv 1 Biologically Inspired Networking 1 Kenji Leibnitz, Naoki Wakamiya and Masayuki Murata 1.1 Introduction 1 1.2 Principles of Biologically Inspired Networking 2 1.3 Swarm Intelligence 9 1.4 Evolutionary and Adaptive Systems 14 1.5 Conclusion 19 References 19 2 The Role of Autonomic Networking in Cognitive Networks 23 John Strassner 2.1 Introduction and Background 23 2.2 Foundations of Autonomic Computing 24 2.3 Advances in Autonomic Computing – Autonomic Networking 26 2.4 The FOCALE Architecture 34 2.5 Application to Wired and Wireless Cognitive Networks 44 2.6 Challenges and Future Developments 48 2.7 Conclusions 50 Glossary 50 References 51 3 Adaptive Networks 53 Jun Lu, Yi Pan, Ryota Egashira, Keita Fujii, Ariffin Yahaya and Tatsuya Suda 3.1 Introduction 53 3.2 Dynamic Factors 54 3.3 Network Functions 55 3.4 Representative Adaptation Techniques 59 3.5 Discussion 73 viii Contents 3.6 Conclusion 74 References 74 4 Self-Managing Networks 77 Raouf Boutaba and Jin Xiao 4.1 Introduction: Concepts and Challenges 77 4.2 The Vision and Challenges of Self-Management 78 4.3 Theories for Designing Self-Managing Networks 81 4.4 Self-Management Intelligence: To Know and to Act 83 4.5 Self-Management Advances in Specific Problem Domains 86 4.6 Benchmarking and Validation 90 4.7 Self-Stabilization 91 4.8 Conclusion 92 References 93 5 Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges 97 Thomas G. Dietterich and Pat Langley 5.1 Introduction 97 5.2 Problem Formulations in Machine Learning 99 5.3 Tasks in Cognitive Networking 105 5.4 Open Issues and Research Challenges 113 5.5 Challenges in Methodology and Evaluation 116 5.6 Summary 117 Acknowledgements 118 References 118 6 Cross-Layer Design and Optimization in Wireless Networks 121 Vineet Srivastava and Mehul Motani 6.1 Introduction 121 6.2 Understanding Cross-Layer Design 123 6.3 General Motivations for Cross-Layer Design 124 6.4 A Taxonomy of Cross-Layer Design Proposals 129 6.5 Proposals for Implementing Cross-Layer Interactions 134 6.6 Cross-Layer Design Activity in the Industry and Standards 136 6.7 The Open Challenges 138 6.8 Discussion 141 6.9 Conclusions 143 References 143 7 Cognitive Radio Architecture 147 Joseph Mitola III 7.1 Introduction 147 7.2 CRA I: Functions, Components and Design Rules 158 7.3 CRA II: The Cognition Cycle 174 7.4 CRA III: The Inference Hierarchy 179 Contents ix 7.5 CRA V: Building the CRA on SDR Architectures 187 7.6 Summary and Future Directions 199 References 201 8 The Wisdom of Crowds: Cognitive Ad Hoc Networks 203 Linda Doyle and Tim Forde 8.1 Introduction 203 8.2 Towards Ad Hoc Networks 204 8.3 A Cognitive Ad Hoc Network 206 8.4 The Wisdom of Crowds 211 8.5 Dynamic Spectrum: Scenarios for Cognitive Ad Hoc Networks 214 8.6 Summary and Conclusions 219 References 220 9 Distributed Learning and Reasoning in Cognitive Networks: Methods and Design Decisions 223 Daniel H. Friend, Ryan W. Thomas, Allen B. MacKenzie and Luiz A. DaSilva 9.1 Introduction 223 9.2 Frameworks for Learning and Reasoning 224 9.3 Distributed Learning and Reasoning within an MAS Framework 227 9.4 Sensory and Actuator Functions 236 9.5 Design Decisions Impacting Learning and Reasoning 237 9.6 Conclusion 243 References 244 10 The Semantic Side of Cognitive Radio 247 Allen Ginsberg, William D. Horne and Jeffrey D. Poston 10.1 Introduction 247 10.2 Semantics, Formal Semantics and Semantic Web Technologies 248 10.3 Community Architecture for Cognitive Radio 251 10.4 Device Architecture for Cognitive Radio and Imperative Semantics 261 10.5 An Architecture for Cognitive Radio Applications 265 10.6 Future of Semantics in Cognitive Radio 268 10.7 Conclusion 268 References 268 11 Security Issues in Cognitive Radio Networks 271 Chetan N. Mathur and K. P. Subbalakshmi 11.1 Introduction 271 11.2 Cognitive Radio Networks 272 11.3 Building Blocks of Communication Security 275 11.4 Inherent Reliability Issues 278 11.5 Attacks on Cognitive Networks 279 11.6 Cognitive Network Architectures 285 11.7 Future Directions 286 11.8 Conclusions 289 x Contents Acknowledgements 289 References 289 12 Intrusion Detection in Cognitive Networks 293 Herve Debar 12.1 Introduction 293 12.2 Intrusion Detection 293 12.3 Threat Model 301 12.4 Integrated Dynamic Security Approach 305 12.5 Discussion 310 12.6 Conclusion 311 References 312 13 Erasure Tolerant Coding for Cognitive Radios 315 Harikeshwar Kushwaha, Yiping Xing, R. Chandramouli and K.P. Subbalakshmi 13.1 Introduction 315 13.2 Spectrum Pooling Concept 318 13.3 Overview of Erasure Channels 319 13.4 Traditional Erasure Codes 321 13.5 Digital Fountain Codes 322 13.6 Multiple Description Codes 328 13.7 Applications 329 13.8 Conclusion 330 References 330 Index 333 Contributors Raouf Boutaba David R. Cheriton School of Computer Science University of Waterloo 200 University Ave. W., Waterloo, Ontario, Canada N2L 3G1 rboutaba@cs.uwaterloo.ca R. Chandramouli Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ 07030, USA mouli@stevens.edu Luiz A. DaSilva Virginia Tech 4300 Wilson Blvd. Suite 750 Arlington, VA 22203, USA ldasilva@vt.edu Herve Debar France Telecom R&D 42 rue des Coutures BP 6243 F-14066 Caen Cedex 4, France herve.debar@orange-ftgroup.com Thomas G. Dietterich School of Electrical Engineering and Computer Science 1148 Kelley Engineering Center Oregon State University Corvallis, OR 97331-5501 USA tgd@eecs.oregonstate.edu Linda Doyle CTVR, Trinity College University of Dublin, Ireland ledoyle@tcd.ie Royta Egashira Information and Computer Science University of California, Irvine Irvine, CA 92697-3435, USA egashira@ics.uci.edu Tim K. Forde CTVR, Trinity College University of Dublin, Ireland timforde@mee.tcd.ie Daniel H. Friend Department of Electrical and Computer Engineering (mail code 0111) Virginia Tech 302 Whittemore Hall Blacksburg, VA 24061-0111 USA dhfriend@vt.edu Keita Fujii Information and Computer Science University of California, Irvine Irvine, CA 92697-3435, USA kfujii@ics.uci.edu Allen Ginsberg The MITRE Corporation 7515 Colshire Dr. McLean, VA 22102-7142, USA aginsberg@mitre.org xii Contributors William D. Horne The MITRE Corporation 7515 Colshire Dr. McLean, VA 22102-7142, USA whorne@mitre.org Harikeshwar Kushwaha Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ 07030, USA harikeshwar@gmail.com Pat Langley Institute for the Study of Learning and Expertise 2164 Staunton Court Palo Alto, CA 94306, USA langley@isle.org Kenji Leibnitz Graduate School of Information Science and Technology Osaka University 1–5 Yamadaoka, Suita, Osaka 565–0871, Japan leibnitz@ist.osaka-u.ac.jp Jun Lu Information and Computer Science University of California, Irvine Irvine, CA 92697-3435, USA lujun@ics.uci.edu Allen B. MacKenzie Department of Electrical and Computer Engineering Virginia Tech 302 Whittemore Hall Blacksburg, VA 24061-0111, USA mackenab@vt.edu Qusay H. Mahmoud Department of Computing and Information Science University of Guelph Guelph, Ontario, N1G 2 W1 Canada qmahmoud@cis.uoguelph.ca Chetan Mathur Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ, 07030, USA cnanjund@gmail.com Joseph Mitola III The MITRE Corporation Tampa, Fl, USA jmitola@mitre.org Mehul Motani Department of Electrical and Computer Engineering National University of Singapore 10 Kent Ridge Crescent Singapore 119260 motani@nus.edu.sg Masayuki Murata Graduate School of Information Science and Technology Osaka University 1–5 Yamadaoka, Suita, Osaka 565–0871, Japan murata@ist.osaka-u.ac.jp Yi Pan Information and Computer Science University of California, Irvine Irvine, CA 92697-3435, USA ypan@ics.uci.edu Contributors xiii Jeffrey D. Poston The MITRE Corporation 7515 Colshire Dr. McLean, VA 22102-7142, USA jdposton@mitre.org John Strassner Motorola Labs 1301 East Algonquin Road Mail Stop IL02-2240 Schaumburg, IL 60010, USA john.strassner@motorola.com Vineet Srivastava HelloSoft, Inc. 8-2-703, Road No.12, Banjara Hills, Hyderabad - 500 034, Andhra Pradesh, India vineet.personal@gmail.com K.P. Subbalakshmi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ, 07030, USA ksubbala@stevens.edu Tatsuya Suda Information and Computer Science University of California, Irvine Irvine, CA 92697-3435, USA suda@ics.uci.edu Ryan W. Thomas Department of Electrical and Computer Engineering Virginia Tech 302 Whittemore Hall Blacksburg, VA 24061-0111, USA rwthomas@vt.edu Naoki Wakamiya Graduate School of Information Science and Technology Osaka University 1–5 Yamadaoka, Suita, Osaka 565–0871, Japan wakamiya@ist.osaka-u.ac.jp Jin Xiao David R. Cheriton School of Computer Science University of Waterloo 200 University Ave. W., Waterloo, Ontario, Canada N2L 3G1 j2xiao@cs.uwaterloo.ca Yiping Xing Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken, NJ 07030, USA yipingxing@gmail.com Ariffin Yahaya Information and Computer Science University of California, Irvine Irvine, CA 92697-3435, USA ariffin@ics.uci.edu Foreword 1 Professor Qusay H. Mahmoud (‘Q’) has contributed a landmark to the evolution of cognitive radio technologies with the publication of his text, Cognitive Networks. Specif- ically, this text clearly marks the beginning of the transition of cognitive radio from enabling technologies in relatively abstract, academic and pre-competitive settings to more clearly address what might be called the critical business-case enablers for broad market relevance. From its introduction at KTH, The Royal Institute of Technology, Stockholm, in 1998, cognitive radio has been about market relevance, overcoming the superficial lack of radio spectrum by pooling, prioritization and space–time adaptation to not just the radio frequency (RF) situation, but also to the needs of the users in the scene. Textbooks to date, however, have contributed more significantly to the foundations of the technology or to relatively small market segments such as secondary spectrum or ad hoc networks than to the broader Internet-scale wireless markets of increasingly heterogeneous cellu- lar services and commodity consumer electronics. Communications networks for these broader real-world markets typically must be (or become) secure, scalable, reliable, con- trollable, interoperable and, of course, billable. It is difficult to state the importance of Q’s book in a few words. Although a few chapters (like my own) are more foundational than transitional, most of the chapters move cognitive networks solidly along a transition from theory to practice. Let me offer a few highlights that qualify as paradigm shifts. First, Herve Debar’s (France Telecom) chapter (Chapter 12) on intrusion detection in cognitive networks draws on the IETF’s intrusion detection working group to characterize the problem of knowledge attack and to structure management and policy plans with operations that define a paradigm shift in network security. Since cognitive networks are inherently flexible in the extreme, it would be difficult or impossible for cognitive wireless networks (CWNs) to transition from theory and niche applications to broadly applicable practice without a solid handle on security. This chapter addresses in a systematic way, the intrusion detection aspect of information security framed comprehensively by Mathur and Subbalakshmi in their companion chapter on security issues, which characterizes security issues from availability and access to privacy and non-repudiation, along with classes of attack that would be problematic for CWNs in the literature. Although Debar’s chapter addresses only intrusion detection in detail, this pair of chapters set a high standard indeed for the articulation of a critical transition issue – security – and the related contribution that shows the way towards transition. In another important chapter (Chapter 2), Motorola calls the foundational layers of recon- figurable wireless networks the ‘autonomic’ layers, adapting IBM’s term, an allusion to the autonomic nervous system of mammals that controls involuntary actions such as heart beat xvi Foreword 1 and respiration. Although I prefer my own original cognition cycle – observe, orient, plan, decide, act and learn – to the simpler Motorola–IBM cycle – monitor, analyze, plan and execute (with knowledge in the middle, but without learning) – their FOCALE architecture that realizes the cycle in cognitive networks ties learning to business goals. That’s a big improvement on my own OOPDAL loop, which wasn’t explicitly tied to the inevitable busi- ness logic from which the revenue comes. This chapter, like several others, also introduces ‘layers’ and related objects and relationships for services, management and the control of the foundational reconfigurable networks. I found the differences in device interface language for controlling different kinds of routers compelling evidence for what I call computational semantics interoperability: they proposed an ontology-mapping construct as essential for the transition towards practical cognitive networks. The MITRE chapter by Ginsberg, Horne and Poston (Chapter 10) develops this notion of semantic interoperability among heterogeneous networks further, drawing on the web ontology language (OWL) from the semantic web community, in some sense a technology looking for a problem that may in fact have found a home in the creation of machine- readable specifications. When I teach radio engineering courses in the US, almost no radio engineers have used Z.100, the International Telecommunications Union’s standard specification and description language (SDL) for digital systems, particularly for the state machines and message sequence charts (MSC) of radio. Not so in Europe where Z.100 was invented so that engineers from across the EC could collaborate in the mathematical language of SDL. For me, it was a watershed event when a few years ago, the GSM MoU committee determined that the machine-readable SDL would be normative while the human-readable text would be explanatory, reversing decades-long practice that the natural language text of a specification be normative while the figures and computer- readable code in the specification be illustrative. In this context, Boutaba and Xiao’s chapter (Chapter 4) on self-managing networks shows how the technical ideas of semantic interoperability have substantial cost lever- age: 80% of information technology is expended on operations and maintenance, with nearly half of service outages caused by human error. Although the telecommunica- tions industry continues to automate, it is on the edges of heterogeneous networks – the wired and the wireless, the multi-standard to the core network – that the contribution of cognitive networks becomes most evident. Lu et al. survey a broad scope of ideas, approaches and architectures for self-management in Chapter 3 on adaptive networks. One of the more intriguing is the ant colony idea developed in greater detail by Liebnitz et al. in Chapter 1 on biologically inspired networking. Behavior, after all, is crucial. Their first figure shows how the price of scaling up to Internet-sized networks is a loss of determinism because the overhead of centralized control becomes prohibitive as net- work size increases into the millions and billions of nodes. Reminiscent of foundational work in artificial life by Stuart Kaufman (At Home in the Universe and the Artificial Life proceedings of the Santa Fe Institute), Leibnitz et al. remind us that biologically inspired architectures may fall short of optimal in some respects, but also may be more robust in dealing with catastrophe. Their mathematical treatment and simulations each relate back to important properties of cognitive networks that enable transition to Internet scale. Foreword 1 xvii Not to condemn with faint praise, all of the chapters, including those not yet mentioned in this brief note, each contribute importantly to critical issues in cross-layer optimization, coding, distributed learning and overall cognitive network robustness. I’m sure the grow- ing community of cognitive radio will benefit greatly from this important work. Dr. Joseph Mitola III Consulting Scientist The MITRE Corporation Tampa, FL, USA Foreword 2

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