Hoai An / Bouvry / Tao Modelling, Computation and Optimization in Information Systems and Management Sciences

Second International Conference MCO 2008, Metz, France - Luxembourg, September 8-10, 2008, Proceedings
1. Auflage 2008
ISBN: 978-3-540-87477-5
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 14, 632 Seiten

Reihe: Communications in Computer and Information Science

This book constitutes the refereed proceedings of the Second International Conference MCO 2008, Metz, France, September 2008. The 65 revised full papers presented were carefully reviewed and selected from 160 submissions. The papers are organized in topical sections on optimization and decision making, data mining theory, systems and applications, computer vision and image processing, computer communications and networks, optimization and search techniques for security, reliability, trust.

Autoren/Hrsg.

Hoai An, Le Thi

Bouvry, Pascal

Tao, Pham Dinh

Inhaltsverzeichnis

1;Title Page;2
2;Preface;5
3;Organization;6
4;Table of Contents;9
5;Optimal Flight Paths Reducing the Aircraft Noise during Landing;15
5.1;Introduction;15
5.2;Optimal Control Problem;16
5.2.1;Dynamics of Problem;16
5.2.2;Path Constraints;17
5.2.3;Cost Function;18
5.3;Methods of Resolution;20
5.3.1;Indirect Method;20
5.3.2;Direct Method;21
5.4;Numerical Application;21
5.4.1;Indirect Method;22
5.4.2;Direct Method;22
5.5;Conclusion;23
5.6;References;24
6;Scalability Analysis of a Novel Integer Programming Model to Deal with Energy Consumption in Heterogeneous Wireless Sensor Networks;25
6.1;Introduction;25
6.2;The Wireless Sensor Network;26
6.3;Model for Optimizing the Energy Consumption;28
6.4;Computational Results;31
6.5;Discussion;33
6.6;Conclusion and Future Work;34
6.7;References;34
7;Single Straddle Carrier Routing Problem in Port Container Terminals: Mathematical Model and Solving Approaches;35
7.1;Introduction;35
7.2;The Problem Formulation;37
7.3;A Combined DCA and New Cutting Plane Techniques;39
7.3.1;Construction of a Cutting Plane from a Local Solution of the Penalty Function;40
7.3.2;Updating of the Upper Bound and the Best Known Solution - Separation of a Feasible Solution;41
7.3.3;Description of the Algorithm DCA CUT;42
7.4;NumericalExperiments;42
7.5;Summary and Conclusions;43
7.6;References;44
8;Employing “Particle Swarm Optimization” and “Fuzzy Ranking Functions” for Direct Solution of EOQ Problem;46
8.1;Introduction;46
8.2;The Particle Swarm Optimization Algorithm;47
8.3;Fuzzy Multi-item EOQ Model;49
8.3.1;Solution of Fuzzy Multi-item EOQ Problem Via the Signed Distance Method;50
8.3.2;Solution of Fuzzy Multi-item EOQ Problem Via Ranking with Integral Value;51
8.3.3;Solution of Fuzzy Multi-item EOQ Problem Via Ranking of Fuzzy Numbers through the Comparison of Their Expected Intervals;52
8.4;Conclusion;54
8.5;References;55
9;Linear Reformulations of Integer Quadratic Programs;57
9.1;Introduction;57
9.2;The \BBL Approach;59
9.3;The \BIL Approach;60
9.4;Computational Results;62
9.5;Concluding Remarks;64
9.6;References;65
10;Control of Some Graph Invariants in Dynamic Routing;66
10.1;Introduction;66
10.2;Some Definitions;67
10.3;Addition of an Edge;68
10.3.1;First Case;68
10.3.2;Second Case;68
10.4;Removal of an Edge;69
10.5;Addition of a Vertex;69
10.6;Removal of a Vertex;71
10.7;Conclusion;72
10.8;References;72
11;A Simulation Tool for Analyzing and Improving the Maternity Block Management;73
11.1;Introduction;73
11.2;Literature Survey;74
11.3;Decision-Making Aid Tool Design: Approach and Tools;76
11.4;Application: A Decision-Making Aid Tool for a Maternity Block;79
11.5;Conclusion;81
11.6;References;81
12;Solving the Multiple Objective Integer Linear Programming Problem;83
12.1;Introduction;83
12.2;Problem Formulation;84
12.3;Principle of the Method;85
12.4;MainResult;86
12.5;Computational Results;87
12.6;Conclusion;89
12.7;References;89
13;Generalized Polychotomic Encoding: A Very Short Bit-Vector Encoding of Tree Hierarchies;91
13.1;Introduction;91
13.2;State of the Art;93
13.2.1;Previous Works;93
13.2.2;A Generic Algorithm;94
13.3;Heuristic;95
13.3.1;Description;95
13.3.2;Theoretical Results;96
13.4;Experimental Results;97
13.5;Conclusion;98
13.6;References;98
14;Mathematical Programming Formulations for the Bottleneck Hyperplane Clustering Problem;101
14.1;Introduction;101
14.1.1;Previous Work;102
14.2;Problem Formulation;102
14.3;Reformulations;103
14.4;Approximations;105
14.4.1;Linearization of $\ell_1$ Unit Constraint;106
14.4.2;Linearization of $\ell_\infty$ Unit Constraint;106
14.4.3;Pure $\ell_\infty$ Approximation;107
14.4.4;Sandwiching $\ell_1/\ell_\infty$ Approximation;107
14.4.5;Alternating $\ell_1/\ell_\infty$ Approximation;107
14.5;Computational Experiments;108
14.6;Conclusion;109
14.7;References;110
15;Constraint Propagation with Tabu List for Min-Span Frequency Assignment Problem;111
15.1;Introduction;111
15.2;Hybrid Method with Tabu List and Constraint Propagation;113
15.2.1;Global Procedure;113
15.2.2;Constraint Propagation;114
15.2.3;Nogood Management;115
15.2.4;Uninstantiation Procedure;115
15.2.5;Extension Procedure;115
15.3;Tests and Results;116
15.3.1;Frequency Assignment Problem;116
15.3.2;Results and Comparisons;117
15.4;Conclusion;118
15.5;References;119
16;Evolutionary Optimisation of Kernel and Hyper-Parameters for SVM;121
16.1;Introduction;121
16.2;Support Vector Machines;122
16.3;Related Work;124
16.4;Evolutionary Kernel of Kernels (\textit{eKoK});124
16.4.1;Model Architecture;124
16.4.2;The Representation of the (\textit{eKoK});125
16.4.3;Fitness Assignment;126
16.4.4;Genetic Operations;126
16.5;Experimental Validation and Discussions;127
16.6;Conclusions;129
16.7;References;130
17;Lot-Sizing and Sequencing on a Single Imperfect Machine;131
17.1;Introduction;131
17.2;ProblemP-Cost;133
17.3;Conclusions;137
17.4;References;138
18;Best and Worst Optimum for Linear Programs with Interval Right Hand Sides;140
18.1;Problem Statement;140
18.2;Uncertainty on Right Hand Sides: Main Results;141
18.3;Linear Programs with Interval Right Hand Sides: The Case of Inequality Constraints;141
18.3.1;Best Optimal Solution;142
18.3.2;Worst Optimal Solution;142
18.4;Linear Programs with Interval Right Hand Sides: The Case of Equality Constraints;142
18.4.1;Best Optimal Solution;143
18.4.2;Worst Optimal Solution;144
18.5;Linear Programs with Interval Right Hand Sides: The General Case;146
18.5.1;Best Optimal Solution;146
18.5.2;Worst Optimal Solution;147
18.6;Conclusion;147
18.7;References;148
19;Transit Network Re-timetabling and Vehicle Scheduling;149
19.1;Introduction;149
19.2;Problem Description;151
19.2.1;Properties of Our Approach;151
19.2.2;Input;152
19.2.3;Variables;152
19.2.4;Constraints;152
19.2.5;Objectives;153
19.3;Solution Approach;154
19.3.1;Iterated Local Search;154
19.3.2;Vehicle Assignment;154
19.3.3;Evaluation Function;154
19.3.4;Moves and Neighborhoods;155
19.3.5;Initial Solution and General Procedure;155
19.4;Experimentations and Numerical Results;156
19.4.1;Data Set;156
19.4.2;Computational Results;156
19.5;Conclusion;157
19.6;References;158
20;Traveling Salesman Problem and Membership in Pedigree Polytope - A Numerical Illustration;159
20.1;Introduction;159
20.1.1;The Pedigree Polytope;161
20.2;The Membership Problem;162
20.2.1;Solving $F_{4}$ and Forming $N_{4}$};162
20.2.2;Defining Dummy and Rigid Arcs in $F_{4}$};163
20.2.3;Checking the Necessary Condition for Membership in the Pedigree Polytope for $k>4$;163
20.3;Conclusion;167
20.4;References;167
21;The Minimum Weight In-Tree Cover Problem;169
21.1;Introduction;169
21.2;An Algorithm for the Problem $\mbox{MWICP}$;172
21.2.1;An Algorithm for the Problem \sc{Separation};173
21.3;Generalization to Multiple Roots;173
21.4;References;178
22;On Importance of a Special Sorting in the Maximum-Weight Clique Algorithm Based on Colour Classes;179
22.1;Introduction;179
22.2;Description of the Algorithm to Be Extended;180
22.2.1;Branch and Bound Routine and Pruning Using Colour Classes;180
22.2.2;Backtracking and Colour Classes;181
22.3;New Algorithm Including Sorting Strategy;182
22.3.1;Sorting;182
22.3.2;Colouring Algorithm for the Maximum Weigh Clique Algorithm;183
22.3.3;Maximum Weigh Clique Algorithm;184
22.4;Computational Results and Discussion;185
22.5;Conclusion;187
22.6;References;187
23;An Extended Comparison of the Best Known Algorithms for Finding the Unweighted Maximum Clique;189
23.1;Introduction;189
23.2;Introduction into Branch and Bound Algorithms;190
23.3;Different Levels of Using a Vertex Colouring in Nowadays Algorithms;191
23.3.1;Re-applying a Heuristic Vertex Colouring;191
23.3.2;Re-using a Heuristic Vertex Colouring;192
23.4;Tests;193
23.5;Conclusion;194
23.6;References;195
24;An Adapted Branch and Bound Algorithm for Approximating Real Root of a Ploynomial;196
24.1;Introduction;196
24.2;Quadratic Bounding Functions;197
24.3;Description of the Adapted Branch and Bound Algorithm;199
24.3.1;Convergence of the Algorithm;200
24.4;Illustrative Examples and Computational Results;201
24.5;Conclusion;202
24.6;References;202
25;Portfolio Selection under Piecewise Affine Transaction Costs: An Integer Quadratic Formulation;204
25.1;Introduction;204
25.2;The Investor Portfolio Selection Model;205
25.3;An Integer Quadratic Formulation;206
25.4;Algorithm of Resolution;207
25.5;References;210
26;An Exact Method for a Discrete Quadratic Fractional Maximum Problem;211
26.1;Introduction;211
26.2;Notations and Definitions;211
26.3;SomeResults;212
26.4;Notations;214
26.4.1;Algorithm;215
26.5;Illustrative Example;215
26.6;Conclusion;216
26.7;References;216
27;Disaggregation of Bipolar-Valued Outranking Relations;218
27.1;Introduction;218
27.2;$\mathcal{M}_1$: Model with a Single Preference Threshold;219
27.3;$\mathcal{M}_2$: Model with Two Preference Thresholds;221
27.4;$\mathcal{M}_3$: Model with Two Preference and Two Veto Thresholds;224
27.4.1;Example;227
27.5;References;227
28;A Performance Study of Task Scheduling Heuristics in HC Environment;228
28.1;Introduction;228
28.2;Related Work;229
28.3;Problem Definition;230
28.4;Task Partitioning Heuristic;230
28.4.1;Heuristics Notation;232
28.5;Experimental Results and Analysis;232
28.5.1;Dataset;232
28.5.2;Comparative Performance Evaluation;232
28.5.3;Algorithm to Find Best Heuristic;235
28.6;Conclusions;236
28.7;References;237
29;Performance Evaluation in a Queueing System $M_2/G/1$ ;238
29.1;Introduction;238
29.2;Description of the Systems;239
29.3;Strong Stability in an $M_2/G/1$ Queue with PreemptivePriority;242
29.3.1;Estimation of Stability;244
29.3.2;Inegality of Stability;246
29.4;Conclusion;246
29.5;References;247
30;Outcome-Space Polyblock Approximation Algorithm for Optimizing over Efficient Sets;248
30.1;Introduction;248
30.2;Bases of the Algorithm;250
30.2.1;Equivalent Form of Problem ($P_2$);250
30.2.2;Polyblock Approximation;252
30.3;Polyblock Approximation Algorithm;253
30.3.1;Algorithm for Solving Problem ($P_2$);253
30.3.2;Convergence of Algorithm;255
30.3.3;Example;256
30.4;References;256
31;A DC Programming Approach for Mixed-Integer Linear Programs;258
31.1;Introduction;258
31.2;DC Reformulation for MILP;259
31.2.1;Reformulation of Integer Set;259
31.2.2;Reformulation of MILP as a DC Program;260
31.3;DCA for Solving Problem (11);261
31.4;A Combination of DCA with a B&B Scheme;262
31.5;Computational Experiments;264
31.6;Conclusion and Future Work;266
31.7;References;267
32;Simulation-Based Optimization for Steel Stacking;268
32.1;Introduction;268
32.1.1;Literature Overview;269
32.1.2;Contributions;269
32.2;Formal Description;269
32.3;ProposedSolution;270
32.3.1;Simulation Setup;270
32.4;Stacking Strategies;271
32.4.1;Minimize Conflicts;271
32.4.2;Delay Conflicts;272
32.4.3;Flexibility Optimization;272
32.5;Computational Results;274
32.6;Conclusions and Future Work;276
32.7;References;277
33;Robust Hedging of Electricity Retail Portfolios with CVaR Constraints;278
33.1;Introduction;278
33.2;Basic Assumptions and Notational Conventions;280
33.3;The Optimization Model;281
33.3.1;Expliciting CVaR Constraints;283
33.4;An Application;284
33.5;Conclusive Remarks and Future Directions;285
33.6;References;286
34;Value Functions and Transversality Conditions for Infinite Horizon Optimal Control Problems;287
34.1;Necessary Condition for Optimality;287
34.1.1;Maximum Principle with an Infinite Horizon;288
34.2;Properties of the Value Function;290
34.2.1;Lipschitz Continuity of the Value Function;290
34.2.2;Convexity of the Value Function;290
34.3;Sufficient Conditions for Optimality;291
34.3.1;Sufficiency Theorems;291
34.4;Necessary and Sufficient Condition for Optimality;294
34.4.1;Concavity of the Hamiltonian;294
34.4.2;Transversality Condition at Infinity;295
34.4.3;References;295
35;Modeling the Mobile Oil Recovery Problem as a Multiobjective Vehicle Routing Problem;297
35.1;Introduction;297
35.2;Formulations Using One Vehicle;298
35.2.1;Subtour Eliminations Constraints;299
35.3;A Three-Indexed Formulation Using Several Vehicles;301
35.4;A Two-Indexed Formulation Using Several Vehicles;302
35.5;Computational Results;303
35.6;Concluding Remarks;305
35.7;References;306
36;Empirical Analysis of an Online Algorithm for Multiple Trading Problems;307
36.1;Introduction;307
36.2;Problem Formulation;308
36.3;Multiple Trade Problem;309
36.4;Experimental Results;311
36.5;Conclusions;315
36.6;References;315
37;A Novel Approach for the Nurse Scheduling Problem;317
37.1;Introduction;317
37.2;Nurse Scheduling Environment;318
37.3;Conclusion;320
37.4;References;321
38;Postoptimal Analysis in Nonserial Dynamic Programming;322
38.1;Introduction;322
38.2;Discrete Optimization Problems and Their Graph Representations;323
38.3;Graph Partitioning and Quotient Graphs;324
38.4;Nonserial Dynamic Programming Block Elimination Scheme;324
38.5;Postoptimality Analysis;326
38.5.1;Postoptimality Analysis in DO;326
38.5.2;Family of Related DO Subproblems in NSDP Block Procedure;326
38.6;References;330
39;A Novel Optimization in Guillotine Cut Applied Reel of Steel;332
39.1;Introduction;332
39.2;The Problem of Guillotine Cut and Selection of Reels in Stock;332
39.3;Cutting of Steel Reels;333
39.4;The Model of Guillotine Cut and Selection of Reels in Stock;333
39.5;Application in the Metal-Mechanics Industry;335
39.6;Model for Reels Blanks in Stock;337
39.7;Computational Results;338
39.7.1;Comparative Analyses of the Results;339
39.8;Conclusion;340
39.9;References;341
40;Robust Production Planning: An Alternative to Scenario-Based Optimization Models;342
40.1;Introduction;342
40.2;Aggregate Production Planning Models;343
40.2.1;Scenario-Based Optimization Model;344
40.2.2;An Alternative Model;346
40.3;Experimental Design and Computational Result;347
40.4;Conclusion and Further Research;350
40.5;References;351
41;Challenging the Incomparability Problem: An Approach Methodology Based on ZAPROS;352
41.1;Introduction;352
41.2;ZAPROS III Method;352
41.3;A New Approach Methodology;354
41.3.1;Formal Statement of the Problem;354
41.3.2;Elicitation of Preferences;354
41.3.3;Comparison of Alternatives;355
41.4;ProposedTool;356
41.5;Application of the Tool;357
41.5.1;Industrialization Process of Cashew Chestnut;357
41.5.2;The Choice of a Prototype for Digital Mobile Television;358
41.6;Conclusions;360
41.7;References;361
42;DC Programming Approach for a Class of Nonconvex Programs Involving $l_0$ Norm;362
42.1;Introduction;362
42.2;Reformulations;363
42.2.1;First Reformulation;363
42.2.2;Second Reformulation;364
42.3;DC Programming and DCA for (5) and (6);364
42.3.1;DC Programming and DCA;364
42.3.2;DCA for Solving (1) and (2);365
42.4;Applications;368
42.5;Experiments and Preliminary Numerical Results;369
42.5.1;Data Sets;370
42.5.2;Numerical Results;370
42.6;Conclusion;370
42.7;References;371
43;Finding Maximum Common Connected Subgraphs Using Clique Detection or Constraint Satisfaction Algorithms;372
43.1;Introduction;372
43.2;Preliminaries;373
43.3;Clique Detection;374
43.4;Constraint Satisfaction Problems;377
43.5;Experimental Results;379
43.6;Conclusion;380
43.7;References;381
44;Analysis and Solution Developement of the Single-Vehicle Inventory Routing Problem;383
44.1;Introduction;383
44.2;Formulation of Single-Vehicle IRP;384
44.2.1;Review of Some Important Concepts;384
44.2.2;The Modified Formulation for Single-Vehicle IRP;385
44.3;Analysis and Solution Strategy;387
44.3.1;Problem Features;387
44.3.2;Solution Strategy;388
44.4;Numerical Example;391
44.5;Conclusion;391
44.6;References;392
45;A Methodology for the Automatic Regulation of Intersections in Real Time Using Soft-Computing Techniques;393
45.1;Introduction;393
45.2;Methodology;394
45.2.1;Flow Observations;395
45.2.2;Estimation Model: Dynamic O-D Matrix;395
45.2.3;Mobility Patterns Extraction;396
45.2.4;Semaphoric Regulation;397
45.3;ModelPerformance;398
45.3.1;Real-Time Flow Observations;398
45.3.2;Estimation Model;398
45.3.3;Inference in Prototypes;398
45.3.4;Implementation of the Optimum Control;399
45.4;Computational Experience;399
45.5;Conclusions and Future Work;401
45.6;References;402
46;Composite Dispatching Rule Generation through Data Mining in a Simulated Job Shop;403
46.1;Introduction;403
46.2;Problem Definition;404
46.3;A Brief Overview of the TACO-Miner Algorithm;404
46.4;Simulation Study;406
46.4.1;Job Shop Model;406
46.4.2;Simulation Experiments;407
46.5;The Methodology and Results;407
46.6;Conclusions;410
46.7;References;411
47;Co-author Network Analysis in DBLP: Classifying Personal Names;413
47.1;Introduction;413
47.2;Detecting the Language of a Personal Name in DBLP;414
47.2.1;System Overview;414
47.2.2;Application to DBLP;416
47.3;Language Detection Using Co-author Network;417
47.3.1;DBLP as a Co-author Network;417
47.3.2;Language Detection Using Co-author Network;418
47.3.3;Classification Using Probabilistic Voting Approach;418
47.4;Evaluation of the Results and Discussion;420
47.5;Summary;420
47.6;References;421
48;On Clustering the Criteria in an Outranking Based Decision Aid Approach;423
48.1;Introduction;423
48.2;A Bipolar-Valued Ordinal Criteria Correlation Index;425
48.3;Principal Component Analysis of the Criteria Correlation;427
48.4;Bipolar-Valued Clustering from the Criteria Correlation Index - $\Ttilde$;429
48.5;Conclusion;431
48.6;References;431
49;A Fast Parallel SVM Algorithm for Massive Classification Tasks;433
49.1;Introduction;433
49.2;Newton Support Vector Machine;435
49.3;Incremental Newton SVM Algorithm;437
49.4;Parallel Incremental Newton SVM Using GPUs;437
49.5;Numerical Test Results;439
49.6;Conclusion and Future Work;440
49.7;References;441
50;A Wavelet Based Multi Scale VaR Model for Agricultural Market;443
50.1;Introduction;443
50.2;Relevant Theories;445
50.2.1;Value at Risk;445
50.2.2;Wavelet Analysis;446
50.3;Wavelet Decomposed Value at Risk;446
50.4;Empirical Studies;448
50.4.1;Data and Experiment Design;448
50.4.2;Empirical Results;449
50.5;Conclusion;451
50.6;References;452
51;Using Formal Concept Analysis for the Extraction of Groups of Co-expressed Genes;453
51.1;Introduction;453
51.2;Background;454
51.2.1;Gene Expression Matrices and Profiles;454
51.2.2;Background on FCA;456
51.3;Applying FCA to GEM Analysis;457
51.4;Experiments;460
51.5;Conclusion and Future Works;462
51.6;References;462
52;Join on Closure Systems Using Direct Implicational Basis Representation;464
52.1;Introduction;464
52.2;Preliminaries;465
52.2.1;Basic Definitions and Notations;465
52.2.2;Closure Systems;465
52.3;Join of Closure Systems Using Their Implicational Representation;467
52.3.1;General Case;468
52.3.2;Particular Case: Direct Implicational Basis;468
52.4;Conclusion;470
52.5;References;470
53;$\G^1$ Blending B-Spline Surfaces and Optimization;472
53.1;Introduction;472
53.2;B-Spline Surface Review;473
53.3;Sufficient Conditions of $\G^1$continuity of Two B-SplinePatches;474
53.4;Constructionofa $\G^1$ Blending Patch of Two B-Splines;476
53.4.1;Solution for Two Bicubic B-Spline Surfaces;477
53.4.2;Numerical Example;479
53.5;Optimization of Blending B-Spline Surfaces;479
53.6;Conclusion and Perspectives;480
53.7;References;481
54;A Delineation Algorithm for Particle Images Online;482
54.1;Introduction;482
54.2;Particle Image Evaluation;484
54.3;Particle Image Segmentation Algorithm;487
54.4;Conclusion;490
54.5;References;491
55;Improving Inter-cluster Broadcasting in Ad Hoc Networks by Delayed Flooding;492
55.1;Introduction;492
55.2;WCPD Inter-cluster Broadcasting;493
55.3;The Delayed Weighted Cluster Flooding Protocol (DWCF);495
55.4;Experiments and Results;498
55.5;Related Work;500
55.6;Conclusion and Future Work;500
55.7;References;501
56;Improved Time Complexities of Algorithms for the Directional Minimum Energy Broadcast Problem;502
56.1;Introduction;502
56.2;Preliminaries;504
56.3;The Reduced Beam Procedure;505
56.4;DirectionalBIP;505
56.4.1;An Implementation of BIP with Quadratic Running Time;506
56.4.2;Directional BIP as an Extension of Prim’s Algorithm;507
56.5;Conclusions;509
56.6;References;509
57;Optimised Recovery with a Coordinated Checkpoint/Rollback Protocol for Domain Decomposition Applications;511
57.1;Introduction;511
57.2;Related Works;512
57.3;Improved Coordinated Checkpoint/Rollback Protocol;513
57.3.1;Execution Model and Abstract Representation;514
57.3.2;Definition of a Checkpoint;514
57.3.3;Recovery After Failures;515
57.3.4;Complexity Analysis;516
57.4;Simulations;516
57.4.1;Checkpoint Period Influence;516
57.4.2;Processor Number Influence;517
57.5;Conclusion;518
57.6;References;519
58;A Context-Aware Broadcast Protocol for Mobile Wireless Networks;521
58.1;Introduction;521
58.2;Description of the Protocol;523
58.2.1;Objectives;523
58.2.2;Requirements;524
58.2.3;MathematicalModel;525
58.2.4;Triggers;526
58.2.5;Random Assessment Delay;526
58.2.6;Node Memory;527
58.3;Experimentation;528
58.3.1;Simulation Environment;528
58.3.2;Results;528
58.4;Conclusion and Future Works;532
58.5;References;532
59;Stability of Two-Stage Queues with Blocking;534
59.1;Introduction;534
59.2;The Real Model;535
59.2.1;The General Process;535
59.2.2;The Embedded Markov Chain $\X_n$;536
59.3;The Ideal Model;536
59.3.1;The Embedded Markov Chain $X_n$;537
59.4;The Strong Stability;537
59.5;Stability of the Ideal Model;539
59.6;Deviation of the Transition Operator;541
59.7;Deviation of the Stationary Distribution;541
59.8;Conclusion;542
59.9;References;542
60;A Competitive Neural Network for Intrusion Detection Systems;544
60.1;Introduction;544
60.2;Competitive Model;545
60.3;Experimental Results;546
60.4;Conclusions;550
60.5;References;550
61;Transfer Graph Approach for Multimodal Transport Problems;552
61.1;Introduction;552
61.2;Problem Description;553
61.3;Classical Problems;554
61.3.1;Classical Shortest Path Problem;554
61.3.2;Multiobjectif Shortest Path Problem;555
61.3.3;Time Dependent Shortest Path Problem;555
61.4;Transfer Graph Approach;555
61.4.1;Approach Description;556
61.4.2;Shortest Path Algorithm in Transfer Graph;558
61.5;Conclusion;560
61.6;References;560
62;Wireless Traffic Service Communication Platform for Cars;562
62.1;Introduction;563
62.2;Platform and Services;565
62.3;Technical Requirements;568
62.4;System Description;569
62.5;Conclusions;570
62.6;References;571
63;System Architecture for C2C CommunicationsBased on Mobile WiMAX;572
63.1;Introduction;572
63.2;Related Work;573
63.2.1;Reference Network Architecture;573
63.2.2;Mobile WiMAX Car Implementations;573
63.2.3;C2C Communications Based on WiMAX;574
63.2.4;Neighbor Detection Algorithms;574
63.3;Existing System Model for C2C Communications;575
63.3.1;Assumptions and Requirements;575
63.3.2;Operation Modes of Mobile WiMAX;575
63.3.3;Enabling C2C Communications for Mobile WiMAX;577
63.4;Optimized C2C Communication Mechanism;578
63.4.1;Neighbor Detection Module;579
63.4.2;Neighbor List;580
63.4.3;Routing Decision Module;580
63.5;Discussion;580
63.6;Conclusion;580
63.7;References;581
64;Accuracy and Efficiency in Simulating VANETs;582
64.1;Introduction;582
64.2;Simulation of VANETs;583
64.2.1;VANET Simulation Alternatives;584
64.2.2;Selecting a Simulator;584
64.3;VanetMobiSim/ns-2 and JANE Simulators;585
64.3.1;VanetMobiSim/ns-2 Simulator;585
64.3.2;JANE: The Java Ad-Hoc Network Environment;586
64.4;The CARLINK-UMA Scenario;586
64.5;Real Tests Versus Simulation;588
64.6;Conclusions;590
64.7;References;591
65;Design of Highly Nonlinear Balanced Boolean Functions Using an Hybridation of DCA and Simulated Annealing Algorithm;593
65.1;Introduction;593
65.2;Basic Notations;594
65.3;A Deterministic Optimization Approach: DCA;595
65.3.1;A Deterministic Combinatorial Optimization Formulation;595
65.3.2;Continuous Optimization Formulation;596
65.3.3;DC Formulation;597
65.3.4;DCA to Solve (Qdc) ([9]);597
65.4;An Hybridation DCA-SA Approach;598
65.4.1;Simulated Annealing (SA) Procedure;598
65.4.2;A Combined DCA-SA Scheme;598
65.5;Experimental Results;599
65.6;Conclusion;601
65.7;References;601
66;Non-standard Attacks against Cryptographic Protocols, with an Example over a Simplified Mutual Authentication Protocol;603
66.1;Introduction;603
66.2;General Attack Model;604
66.3;Description of the SASI Protocol;604
66.3.1;CR-SASI: The Simplified SASI Protocol;605
66.4;Cryptanalysis of the SASI Protocol;606
66.4.1;Experimental Results;607
66.5;Concluding Remarks;609
66.6;References;609
67;Provable Security against Impossible Differential Cryptanalysis Application to CS-Cipher;611
67.1;Introduction;611
67.2;Output Differential;615
67.2.1;Formal Treatment for $\CSC^*$;616
67.2.2;Results for $\CSC^*$/CS-Cipher;618
67.3;Conclusion;620
67.4;References;620
68;VNSOptClust: A Variable Neighborhood Search Based Approach for Unsupervised Anomaly Detection;621
68.1;Introduction;621
68.2;Related Work in Cluster Analysis;622
68.3;The VNSOptClust Algorithm;624
68.4;Experimental Results;627
68.5;Conclusion;628
68.6;References;629
69;Author Index;631