Network Modeling, Simulation and Analysis in MATLAB Theory and Practices

The purpose of this book is first to study MATLAB programming concepts, then the basic concepts of modeling and simulation analysis, particularly focus on digital communication simulation. The book will cover the topics practically to describe network routing simulation using MATLAB tool. It will cover the dimensions' like Wireless network and WSN simulation using MATLAB, then depict the modeling and simulation of vehicles power network in detail along with considering different case studies.

Key features of the book include:

• Discusses different basics and advanced methodology with their fundamental concepts of exploration and exploitation in NETWORK SIMULATION.
• Elaborates practice questions and simulations in MATLAB
• Student-friendly and Concise
• Useful for UG and PG level research scholar
• Aimed at Practical approach for network simulation with more programs with step by step comments.
• Based on the Latest technologies, coverage of wireless simulation and WSN concepts and implementations

Dac-Nhuong Le obtained his PhD in computer science from Vietnam National University, Vietnam in 2015. He is Deputy-Head of Faculty of Information Technology, Haiphong University, Vietnam. His area of research includes: evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT and image processing in biomedicine. He has authored 4 computer science books and has multiple research articles in international journals.

Abhishek Kumar Pandey is pursuing a doctorate in computer science from the University of Madras and is doing ongoing research on face recognition using the IoT concept. He has a Masters of Technology in Computer Science and Engineering from Government Engineering College, Ajmer, Rajasthan Technical University, Kota, India. He has been working as an Assistant Professor of Computer Science at Aryabhatt Engineering College and Research Center, Ajmer, as well as a visiting faculty member at Government University MDS Ajmer.

Sairam Tadepalli completed his Bachelors in Computer Science and Engineering and Masters in Cloud Computing and is pursuing a PhD in Machine Learning from Vellore Institute of Technology. He has a certification in data science from John Hopkins University, USA.

Pramod Singh Rathore has a Masters of Technology in Computer Science and Engineering from Government Engineering College, Ajmer, Rajasthan Technical University, Kota, India. He has been working as the Assistant Professor of Computer Science at Aryabhatt Engineering College and Research Centre, Ajmer, and also as a visiting faculty member at Government University MDS Ajmer. He has authored a book on network simulation.

Jyotir Moy Chatterjee is currently working as an Assistant Professor of IT at Lord Buddha Education Foundation (Asia Pacific University of Technology and Innovation), Kathmandu, Nepal. He received his M.Tech from KIIT University, Bhubaneswar, Odisha and B.Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute University, Chennai, (Tamil Nadu). His research interests include cloud computing, big data, privacy preservation and data mining.

List of Figures xi

List of Tables xv

Foreword xvii

Preface xix

Acknowledgments xxi

Acronyms xxiii

1 Introduction to Modeling, Simulations and Analysis 1

1.1 MATLAB Modeling and Simulation 2

1.2 Computer Networks Performance Modeling and Simulation 4

1.2.1 Computer-Based Models 4

1.2.2 Computer Network Simulation 5

1.3 Discrete-Event Simulation for MATLAB 6

1.3.1 Terminology and Components of Discrete-Event Simulation 7

1.3.2 The Principle of Discrete-Event Simulation 8

1.3.3 ESTA Algorithm 9

1.3.4 ANALYSIS: Determination of Time to Attain Steady State Condition for MATLAB 11

1.4 Simulation Software Selection for MATLAB 11

1.5 Simulation Tools Based on High Performance 12

1.5.1 Network Model 13

1.5.2 Network Simulators 15

1.6 Conclusion 22

References 23

2 Introduction to MATLAB Programming 25

2.1 Introduction 26

2.2 Basic Features 27

2.2.1 Features of MATLAB 27

2.2.2 Uses of MATLAB 27

2.3 Notation, Syntax, and Operations 27

2.3.1 Practical Examples for MATLAB 27

2.3.2 Use of Semicolon (;) in MATLAB 28

2.3.3 Adding Comments 29

2.3.4 Commonly Utilized Operators and Special Characters 29

2.3.5 Unique Variables and Constants 30

2.3.6 Sparing Process 30

2.3.7 MATLAB Decisions 30

2.3.8 MATLAB Loops 31

2.4 Import and Export Operations 32

2.4.1 Import Data in MATLAB 32

2.4.2 Export Data in MATLAB 38

2.5 Elements 40

2.5.1 Commands 40

2.5.2 MATLAB Basics 41

2.5.3 Creating Matrices 42

2.5.4 Framework Operations 42

2.5.5 Using M-Files 44

2.6 Plotting 47

2.6.1 Including Various Types of Graphs 48

2.6.2 Creation of a Multiple Number of Functions in a Similar Graph 49

2.6.3 Creating a Graph According to Various Colors 50

2.7 Uncommon Function 51

2.8 Executable Files Generation 52

2.9 Calling and Accumulating Executable Documents 54

2.10 Calling Objects from External Programs 55

2.11 JAVA Classes 56

2.12 The Guide 56

2.12.1 Open a New User Interface 57

2.12.2 Guide Window Size Setting 58

2.12.3 Design the User Interface 58

2.12.4 Adjust the Components 59

2.12.5 Mark the Push Buttons 60

2.12.6 Menu Items-Rundown Pop-Up 61

2.12.7 Static Test Alteration Procedure in MATLAB 61

2.12.8 Spare the Layout 62

2.12.9 Behavior of the App 63

2.12.10 Produce Data to Plot in MATLAB 63

2.12.11 Pop-Up Menu Characteristics 65

2.12.12 Behavior of Push Button 66

2.13 Effective Programming through MATLAB 67

2.13.1 Condition 68

2.13.2 Practice Programs 68

2.13.3 Specific Functions in MATLAB 69

2.14 Clones Process Using MATLAB 69

2.14.1 GNU Octave 69

2.14.2 Scilab 70

2.14.3 Sage 70

2.15 Parallel MATLAB System 71

2.15.1 Run a Batch Job 71

2.15.2 Run a Batch Parallel Loop 72

2.15.3 Current Folder Browser - Run Script as Batch Job 73

2.16 Conclusion 74

References 75

3 Digital Communication System Simulation Using MATLAB 77

3.1 Introduction to Digital Communication 78

3.1.1 Data Transmission 78

3.1.2 Example 79

3.1.3 The Conversion of Analog and Digital Signals 80

3.1.4 Information, Bandwidth, and Noise 82

3.2 Simulation of Rayleigh Fading Model 83

3.2.1 Rayleigh Fading Basics 83

3.2.2 Rayleigh Fading 84

3.3 BPSK Modulation and Demodulation 86

3.3.1 BPSK Modulation 86

3.3.2 BPSK Demodulation 87

3.4 QPSK Modulation and Demodulation 89

3.4.1 QPSK Transmitter 90

3.4.2 QPSK Receiver 93

3.4.3 Performance Simulation over AWGN 93

3.5 Image Error Rate vs Signal-to-Noise Ratio 94

3.5.1 M-QAM Modulation 94

3.5.2 Baseband Rectangular M-QAM Modulator 95

3.6 Recreation of OFDM Framework 99

3.6.1 Figuring (Es /N0 ) or (Eb /N0 ) for OFDM Framework 101

3.6.2 Impact of Cyclic Prefix on Es /N 101

3.6.3 Effect of Unused Subcarriers on Es/N 102

3.6.4 Arrangement of Subcarriers 103

3.6.5 MATLAB Sample Code 103

3.7 Conclusion 108

References 109

4 Statistical Analysis of Network Data Using MATLAB 111

4.1 Introduction to Association Networks 112

4.2 Time Series, Stationary, Time Series Decomposition, De-trending 114

4.2.1 Time Series Analysis 114

4.2.2 Stationarity 115

4.2.3 Time Series Decomposition 117

4.2.4 De-trending 118

4.3 Autocorrelation, Test for Independence, Linear Autoregressive Models 124

4.3.1 Autocorrelation 124

4.3.2 ACF and IACF Parameters 126

4.3.3 Test of Independence 128

4.3.4 Linear Autoregressive Models 135

4.3.5 Linear Prediction and Autoregressive Modeling 137

4.4 Mutual Information and Test for Independence 139

4.4.1 Testing the Significance of the Null Hypothesis I(X; Y ) = 0 139

4.4.2 Producing the Mutual Information Distribution from Surrogates 141

4.5 Spurious Cross-Correlation, Vector Autoregressive Models and Dynamic Regression Models 143

4.5.1 Cross Correlation 143

4.5.2 Vector Autoregression (VAR) Models 146

4.5.3 Coupled Dynamical Systems 149

4.6 Conclusion 150

References 150

5 Network Routing Simulation Using MATLAB 155

5.1 Evaluation of Granger Causality Measures on Known Systems 156

5.1.1 A Historical Viewpoint 158

5.1.2 Application to Recreated Information 164

5.1.3 Application to FMRI BOLD Information from a Visuospatial Consideration Undertaking 170

5.2 Demand Modeling and Performance Measurement 173

5.2.1 Objectives 173

5.2.2 Approach to Model Development 174

5.2.3 Development of Models 175

5.2.4 Outline of Findings from Phase Two: Model Validation 176

5.3 Universal Algorithms and Sequential Algorithms 178

5.3.1 Genetic Algorithm for Improvement Utilizing MATLAB 178

5.3.2 Masses Diversity-Measure-Run, Prosperity Scaling 182

5.4 Acoustic-Centric and Radio-Centric Algorithms 190

5.5 AODV Routing Protocol 194

5.5.1 Keeping Up Sequence Numbers 196

5.5.2 Association Breaks 196

5.5.3 Neighborhood Repairs 197

5.5.4 Security Considerations 197

5.6 Conclusion 203

References 204

6 Wireless Network Simulation Using MATLAB 209

6.1 Radio Propagation for Shadowing Methods 210

6.1.1 Radio Propagation Modeling 210

6.1.2 Partition Dependence 210

6.1.3 Small-Scale Blurring 210

6.1.4 Free-Space Propagation 211

6.1.5 Ray Tracing 212

6.1.6 Indoor Propagation 220

6.1.7 Classic Empirical Models 221

6.1.8 COST 231-Hata Model 221

6.1.9 COST 231-Walfish-Ikegami Model 222

6.1.10 Erceg Model 224

6.1.11 Multiple Slope Models 225

6.2 Mobility: Arbitrary Waypoint Demonstrates 234

6.2.1 Random Waypoint Model 234

6.2.2 Regular Problems with Random Waypoint Model 235

6.2.3 Irregular Waypoint on the Border (RWPB) 235

6.2.4 Markovian Waypoint Model 235

6.3 PHY: SNR-Based Bundle Catches, Communication, Dynamic Transmission Rate and Power 235

6.3.1 MAC: IEEE 802.11 236

6.3.2 IEEE 802.11 RTS/CTS Exchange 237

6.4 NET: Ad Hoc Routing 238

6.4.1 Dynamic Destination Sequenced Distance Vector 240

6.4.2 Wireless Routing Protocol 243

6.4.3 Global State Routing 243

6.4.4 Fisheye State Routing 244

6.4.5 Hierarchical State Routing 244

6.4.6 Zone-Based Hierarchical Link State Routing Protocol 245

6.4.7 Clusterhead Gateway Switch Routing Protocol 246

6.4.8 Cluster-Based Routing Protocols 247

6.4.9 Ad Hoc On-Demand Distance Vector Routing 248

6.4.10 Dynamic Source Routing Protocol 249

6.4.11 Temporally Ordered Routing Algorithm 250

6.4.12 Associativity-based Routing 252

6.4.13 Signal Stability Routing 253

6.5 APP: Overlay Routing Protocols 254

6.5.1 System/Application Designs, Optimizations, and Implementations on Overlay Networks 254

6.5.2 Routing Overlays for VoIP 255

6.5.3 Measurement, Modeling, and Improvement of BitTorrent Overlays 256

6.6 Conclusion 259

References 260

7 Mobility Modeling for Vehicular Communication Networks Using MATLAB 267

7.1 Vehicle Network Toolbox 268

7.1.1 Transmit and Receive CAN Messages 268

7.1.2 Examine Received Messages 271

7.1.3 CAN Message Reception Callback Function 272

7.2 Network Management (NM) 274

7.2.1 Plan Your Network Installation 274

7.2.2 Planning Your Network Installation 275

7.2.3 Setting Up a Remote Client Access Configuration 275

7.2.4 Setting Up Local Client Access Configuration 275

7.3 Interaction Layer 277

7.3.1 Directing Protocols in MANET 278

7.3.2 Specially Appointed On-Demand Distance Vector 278

7.3.3 Dynamic Source Routing (DSR) 278

7.3.4 Diagram of Mobility Model 279

7.3.5 Results and Analysis 280

7.3.6 Association Variation Results 282

7.4 Transport Protocols 285

7.4.1 TCP Transport Protocol 285

7.4.2 User Datagram Protocol, or UDP 286

7.4.3 Reliable Data Protocol, or RDP 286

7.4.4 Transmission Control Protocol, or TCP 286

7.5 Conclusion 287

References 288

8 Case Studies and Sample Codes 291

8.1 Case Determination and Structure 292

8.1.1 Exhibiting Analysis 293

8.1.2 Case Example 293

8.1.3 The Best Strategy 293

8.1.4 Impediment of the Technique 293

8.1.5 Sorts of Contextual Investigations 294

8.1.6 Relevant Examinations in Business 294

8.1.7 Summing Up from Logical Investigations 294

8.1.8 History 295

8.1.9 Related Vocations 295

8.2 Case Study 1: Gas Online 296

8.2.1 Load Data into Project 296

8.2.2 Construct Boundary Models 296

8.3 Case Study 2 302

8.3.1 Case 1: Create a Credit Scorecard Dissent 302

8.3.2 Case 2: Binning Information 304

8.4 Case Study 3: Random Waypoint Mobility Model 306

8.5 Case Study 4: Node localization in Wireless Sensor Network 312

8.6 Case Study 5: LEACH Routing Protocol for a WSN 325

8.7 Conclusion 334

References 334

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