Distributed Sensor Networks: A Multiagent Perspective

376 Pages·2003·21.956 MB·English

by  Victor Lesser, Charles L. Ortiz Jr., Milind Tambe (auth.), Victor Lesser, Charles L. Ortiz Jr., Milind Tambe (eds.)

DISTRIBUTED SENSOR NETWORK S
MULTIAGENT SYSTEMS,
ARTIFICIAL SOCIETIES,
AND SIMULATED ORGANIZATIONS
International Book Series
Series Editor: Gerhard Weiss
Technische Universität München
Editorial Board:
Kathleen M. Carley, Carnegie Mellon University, PA , USA
Yves Deniazeau, CNRS Laboratoire LEIBNIZ, France
Ed Durfee, University of Michigan, USA
Les Gasser, University of Illinois at Urbana-Champaign, IL, USA
Nigel Gilbert, University of Surrey, United Kingdom
Michael Huhns, University of South Carolina, SC, USA
Nick Jennings, University of Southampton, UK
Victor Lesser, University of Massachusetts, MA, USA
KatiaSycara, Carnegie Mellon University, PA, USA
Gerhard Weiss, Technical University of Munich, Germany (Series Editor)
Michael Wooldridge, University of Liverpool, United Kingdom
Books in the Series:
CONFLICTING AGENTS: Conflict Management in Multi-Agent Systems, edited by
Catherine Tessier, Laurent Chaudron and Heinz-Jürgen Müller, ISBN: 0-7923-7210-7
SOCIAL ORDER IN MULTIAGENT SYSTEMS, edited by Rosaria Conte and
Chrysanthos Dellarocas, ISBN: 0-7923-7450-9
SOCIALLY INTELLIGENT AGENTS: Creating Relationships with Computers and
Robots, edited by Kerstin Dautenhahn, Alan H. Bond, Lola Canamero and Bruce Edmonds,
ISBN: 1-4020-7057-8
CONCEPTUAL MODELLING OF MULTI-AGENT SYSTEMS: The CoMoMAS
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GAME THEORY AND DECISION THEORY IN AGENT-BASED SYSTEMS, edited by
Simon Parsons, Piotr Gmytrasiewicz, Michael Wooldridge, ISBN: 1-4020-7115-9
REPUTATION IN ARTIFICIAL SOCIETIES: Social Beliefs for Social Order, by Rosaria
Conte, Mario Paolucci, ISBN: 1-4020-7186-8
AGENT AUTONOMY, edited by Henry Hexmoor, Cristiano Castelfranchi, Rino Falcone,
ISBN: 1-4020-7402-6
AGENT SUPPORTED COOPERATIVE WORK, edited by Yiming Ye, Elizabeth
Churchill, ISBN: 1-4020-7404-2
DISTRIBUTED SENSOR NETWORK S
A Multiagent Perspectiv e
Edited by
VICTOR LESSER
University of Massachusetts
CHARLES L. ORTIZ, JR.
SRI International
MILIND TAMBE
University of Southern California
Springer Science+Business Media, LLC
Library of Congress Cataloging-in-Publication Data
DISTRIBUTED SENSOR NETWORKS: A Multiagent Perspective
Victor Lesser, Charles L. Ortiz, Jr., Milind Tambe
ISBN 978-1-4613-5039-2 ISBN 978-1-4615-0363-7 (eBook)
DOI 10.1007/978-1-4615-0363-7
Copyright © 200 3 by Springer Science+Business Media New Yor k
Originally published by Kluwer Academic Publishers in 200 3
Softcover reprint of the hardcover 1st edition 2003
All rights reserved. No part of this work may be reproduced, stored in a retrieval system,
or transmitted in any form or by any means, electronic, mechanical, photocopying,
microfilming, recording, or otherwise, without the written permission from the Publisher,
with the exception of any material supplied specifically for the purpose of being entered and
executed on a computer system, for exclusive use by the purchaser of the work.
Printed on acid-free paper.
Contents
Contributing Authors xi
Acknowledgments xvii
Introduction to a Multiagent Perspective
Victor Lesser, Charles L. Ortiz, Jr., Milind Tambe
1. Introduction 1
2. Part 1: Sensor network challenge problem 2
3. Part 2: Distributed resource allocation: Architectures and protocols 4
4. Part 3: Analysis 7
5. Future work 8
Part I The Sensor Network Challenge Problem
2
The Radsim Simulator 11
James H. Lawton
1. Introduction 11
2. Radsim Simulation Model 12
3. Simulation Objects 13
4. The External API 18
5. Radsim Configuration 19
Acknowledgments 20
3
Challenge Problem Testbed 21
Paul Zemany, Michael Gaughan
1. Introduction 21
2. Challenge Problem Metrics 22
3. Challenge Problem Test Bed 23
3.1 Overview: Distributed Sensing Test Bed 24
4. Tracking Processing 25
5. Resource Management Process 27
6. Solution Independent Metrics 30
4
Visualization and Debugging Tools 33
Alexander Egyed, Bryan Horling, Raphen Becker, and Robert Balzer
vi DISTRIBUTED SENSOR NETWORKS
1. Visualization 33
1.1 ANTs infrastructure visualization 35
1.2 ANTs agent visualization 37
2. Debugging 40
2.1 Controlling the environment 41
5
Target Tracking with Bayesian Estimation 43
Juan E. Vargas, Kiran Tvalarparti, Zhaojun Wu
1. Introduction 43
2. Sensor Model 44
3. Process Model 45
3.1 Time Frames 46
3.2 Location Model 48
3.3 Amplitude Handler 48
3.4 Frequency Handler 50
3.5 Motion Model 51
3.6 Target Location 54
4. Multiple Target Tracking 54
5. Conclusions 55
6. Acknowledgments 58
References 58
Part II Distributed Resource Allocation: Architectures and Protocols
6
Dynamic resource-bounded negotiation in non-additive domains 61
Charles L. Ortiz, Jr., Timothy W Rauenbusch, Eric Hsu, Regis Vincent
1. Introduction 62
2. Center-based task assignment 64
3. Negotiation in context 69
3.1 Allocation Improvement 70
3.2 Experimental Evaluation 71
4. Combinatorial task allocation 74
4.1 Incremental Task Allocation Improvement Algorithm 76
4.2 Empirical Evaluation 78
5. Dynamic negotiation 78
5.1 Rich bids 81
5.1.1 Task interaction semantics and bid generation 82
5.1.2 Dynamic mediation algorithm 82
5.1.3 Task contention, team composition and fault tolerance 84
5.2 Experimental results and evaluation 86
6. System architecture: interleaving negotiation and execution 90
6.1 Visualization tools and geometric reasoning 91
6.2 Experimental results 94
6.3 Auction results 94
6.4 Mediation experiments 100
7. Summary and related work 103
8. Acknowledgments 106
References 106
Contents vii
7
A satisficing, negotiated, and learning coalition formation architecutre 109
Leen-Kiat Soh, Costas Tsatsoulis, HHseyin Sevay
1. Introduction 110
2. Initial Coalition Formation 113
3. Allocation Algorithms 117
4. Coalition Finalization 119
4.1 Negotiation Strategy 120
4.2 Negotiation Protocol 122
4.3 Case-Based Reasoning (CBR) 123
4.4 Learning 124
4.4.1 Learning to Form Coalitions Better 125
4.4.2 Learning to Negotiate Better 126
5. Coalition Acknowledgment 127
6. Experimental Results 127
6.1 Case-Based Negotiation Strategy 128
6.2 Coalition Formation 130
6.3 Experiments with Learning 132
7. Related Work 133
7.1 Coalition Formation 133
7.2 Negotiation 135
8. Conclusions 135
References 137
8
Using Autonomy, Organizational Design and Negotiation in a DSN 139
Bryan Horling, Roger Mailler, Jiaying Shen, Regis Vincent, and Victor Lesser
1. Overview 140
2. Organizational Design 142
3. Agent Architecture 148
3.1 Java Agent Framework 148
3.1.1 Communication 150
3.1.2 Directory Services 151
3.2 Soft Real-Time Control 152
3.3 T JEMS 155
3.3.1 Scheduling 156
3.3.2 Periodic Tasks 158
4. Resource Allocation 159
4.1 Problem Solver 159
4.1.1 Sensor Agent 160
4.1.2 Sector Manager 161
4.1.3 Track Manager 162
4.2 SPAM 167
4.2.1 Abstraction 167
4.2.2 Utility 168
4.2.3 Protocol 169
4.2.4 Stage 0 & 1 170
4.2.5 Stage 2 171
4.2.6 Generating Solutions 175
5. Results 177
6. Conclusions 180
References 182
V III DISTRIBUTED SENSOR NE7WORKS
9
Scaling-up Distributed Sensor 185
Networks
Osher Yadgar, Sarit Kraus, and Charles L. Ortiz, 1r.
1. The large scale ANTS challenge problem and the DDM 187
2. Descriptions of algorithms 189
2.1 The raw data transformation and capsule generation algo-
rithm 192
2.2 Leader 10calInfo generation algorithm 197
2.3 The movement of a sampler agent 203
3. Simulation, experiments and results 204
3.1 Simulation environment 204
3.2 Evaluation methods 205
3.3 Results 206
4. Related work 214
5. Conclusions 216
6. Acknowledgments 216
References 216
10
Distributed Resource Allocation 219
Pragnesh Jay Modi, Paul Scerri, Wei-Min Shen and Milind Tambe
1. Introduction 219
2. Application Domain 221
3. Modeling Multiagent Resource Allocation via Distributed Constraint
SatisfactIon 225
3.1 Formal Definitions 226
3.2 Properties of Resource Allocation 230
3.2.1 Task Complexity 230
3.2.2 Task Relationship Complexity 231
3.3 Subclasses of Resource Allocation 232
3.4 Dynamic Distributed CSP 232
3.5 Mapping SCF Problems into DyDisCSP 235
3.5.1 Correctness of Mapping I 237
3.6 Mapping WCF Problems into DyDisCSP 239
3.6.1 Correctness of Mapping II 240
4. Adopt algorithm for DCOP 242
4.0.2 Overview of Algorithm 243
4.1 Evaluation 244
5. Application of DCR to Distributed Sensor Networks 246
5.1 Distributed Constraint Reasoning for Distributed Sensors 247
5.2 Probabilistic Task Representation 249
5.3 Updates from Sensors 250
5.4 Updates from Overheard Communication 251
5.5 Hardware Experiments 252
6. Conclusion 254
References 255
11
Distributed Coordination through Anarchic Optimization 257
Stephen Fitzpatrick & Lambert Meertens
1. Distributed Constraint Optimization 258
1.1 Quality Metric: Degree of Conflict 259
Contents ix
2. A Peer-to-Peer Optimization Algorithm 259
2.1 Algorithmic Costs 262
2.2 Experimental Results 263
2.3 Asynchronous Execution 266
3. Radar Tracking 267
3.1 Summary of the Challenge Problem 268
3.2 World Estimates 270
3.3 Trajectories 272
3.4 Measurements and Sensor Models 274
3.5 Data Fusion 275
3.6 Coordination Mechanism 275
3.7 Proximate Metric 278
3.8 Proximate Metric with respect to Probability Distributions
over Trajectories 279
3.9 Quality of Measurements with respect to Single Trajectory 280
3.9.1 Persistence 280
3.9.2 Adhesion 281
3.9.3 Overall Quality 283
3.9.4 Scaling and Adding Mappings 283
3.10 Measurement Feasibility 284
3.11 Overall Quality (including Operational Cost) 285
4. Peer-to-Peer Sensor Coordination Algorithm 285
4.1 Local World Estimates 286
4.2 Local Schedules 287
4.3 Local Schedule Quality Metrics 287
4.4 Distributed Coordination Mechanism 288
4.4.1 Target Models 288
4.4.2 Schedules 289
4.4.3 Local Metrics 289
4.4.4 Search 290
4.5 Experimental Results 292
5. Related Work 293
6. Conclusions 293
7. Acknowledgments 294
References 294
Part III Insights into Distributed Resource Allocation Protocols based on Formal
Analyses
12
Communication and Computation in Distributed CSP Algorithms 299
Cesar Fernandez, Ramon Bejar, Bhaskar Krishnamachari, Carla Gomes, Bart Selman
1. Introduction 300
2. Distributed CSPs 302
3. SensorDCSP-A benchmark for DisCSP algorithms 302
4. DisCSP algorithms 303
5. Complexity profiles ofDisCSP algorithms on SensorDCSP 306
5.1 Randomization and restart strategies 308
5.2 Active delaying of messages 309
6. The effect of the communication network data load 310
7. Conclusions 316
References 317
x DISTRIBUTED SENSOR NE7WORKS
13
A Comparative Study of Distributed Constraint Algorithms 319
Weixiong Zhang, Guandong Wang, Zhao Xing, Lars Wittenburg
1. Introduction 320
2. Distributed Scan Scheduling 321
3. Model in Multiple-coloring 322
4. Low Overhead Distributed Algorithms 323
4.1 Distributed breakout algorithm (DBA) 324
4.2 Distributed Stochastic Algorithm (DSA) 324
5. Threshold Behavior of DSA 328
6. DSA vs. DBA on Solution Quality 329
6.1 Solution quality in terms of network sizes 330
6.2 Anytime performance 331
7. DSA vs. DBA on Communication Cost 333
8. Solving Scheduling Problem 335
9. Conclusions 336
References 337
14
Analysis of Negotiation Protocols by Distributed Search 339
Guandong Wang, Weixiong Zhang, Roger Mailler, Victor Lesser
1. Introduction and Overview 340
2. Target Tracking and the SPAM Protocol 342
2.1 Tracking mUltiple targets 342
2.2 The SPAM protocol 343
3. Constraint Problems in Cooperative Negotiation 345
4. Negotiation Protocol as Search Algorithms 346
4.1 Negotiation as distributed search 346
4.2 SPAM protocol as search algorithms 347
4.2.1 Sequential SPAM 348
4.2.2 Synchronous SPAM 350
5. Experimental Analysis and Results 351
5.1 Completeness 351
5.2 Time complexity 355
5.3 Convergency and performance 356
5.4 Scalability 358
5.5 Summary 360
6. Conclusion and Discussions 360
References 361