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E-raamat: Network Modeling, Simulation and Analysis in MATLAB: Theory and Practices

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(Vellore Institute of Technology, India), (Lord Buddha Education Foundation (Asia Pacific University of Technology a), (University of Madras, India), (Rajasthan Technical University, Kota, India), (Vietnam National University, Vietnam)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Aug-2019
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119631453
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Network Modeling, Simulation and Analysis in MATLAB: Theory and PracticesSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Aug-2019
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119631453
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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
List of Figures
xi
List of Tables
xv
Foreword xvii
Preface xix
Acknowledgments xxi
Acronyms xxiii
1 Introduction to Modeling, Simulations and Analysis
1(24)
1.1 MATLAB Modeling and Simulation
2(2)
1.2 Computer Networks Performance Modeling and Simulation
4(2)
1.2.1 Computer-Based Models
4(1)
1.2.2 Computer Network Simulation
5(1)
1.3 Discrete-Event Simulation for MATLAB
6(5)
1.3.1 Terminology and Components of Discrete-Event Simulation
7(1)
1.3.2 The Principle of Discrete-Event Simulation
8(1)
1.3.3 ESTA Algorithm
9(2)
1.3.4 ANALYSIS: Determination of Time to Attain Steady State Condition for MATLAB
11(1)
1.4 Simulation Software Selection for MATLAB
11(1)
1.5 Simulation Tools Based on High Performance
12(10)
1.5.1 Network Model
13(2)
1.5.2 Network Simulators
15(7)
1.6 Conclusion
22(1)
References
23(2)
2 Introduction to MATLAB Programming
25(52)
2.1 Introduction
26(1)
2.2 Basic Features
27(1)
2.2.1 Features of MATLAB
27(1)
2.2.2 Uses of MATLAB
27(1)
2.3 Notation, Syntax, and Operations
27(5)
2.3.1 Practical Examples for MATLAB
27(1)
2.3.2 Use of Semicolon (;) in MATLAB
28(1)
2.3.3 Adding Comments
29(1)
2.3.4 Commonly Utilized Operators and Special Characters
29(1)
2.3.5 Unique Variables and Constants
30(1)
2.3.6 Sparing Process
30(1)
2.3.7 MATLAB Decisions
30(1)
2.3.8 MATLAB Loops
31(1)
2.4 Import and Export Operations
32(8)
2.4.1 Import Data in MATLAB
32(6)
2.4.2 Export Data in MATLAB
38(2)
2.5 Elements
40(7)
2.5.1 Commands
40(1)
2.5.2 MATLAB Basics
41(1)
2.5.3 Creating Matrices
42(1)
2.5.4 Framework Operations
42(2)
2.5.5 Using M-Files
44(3)
2.6 Plotting
47(4)
2.6.1 Including Various Types of Graphs
48(1)
2.6.2 Creation of a Multiple Number of Functions in a Similar Graph
49(1)
2.6.3 Creating a Graph According to Various Colors
50(1)
2.7 Uncommon Function
51(1)
2.8 Executable Files Generation
52(2)
2.9 Calling and Accumulating Executable Documents
54(1)
2.10 Calling Objects from External Programs
55(1)
2.11 JAVA Classes
56(1)
2.12 The Guide
56(11)
2.12.1 Open a New User Interface
57(1)
2.12.2 Guide Window Size Setting
58(1)
2.12.3 Design the User Interface
58(1)
2.12.4 Adjust the Components
59(1)
2.12.5 Mark the Push Buttons
60(1)
2.12.6 Menu Items-Rundown Pop-Up
61(1)
2.12.7 Static Test Alteration Procedure in MATLAB
61(1)
2.12.8 Spare the Layout
62(1)
2.12.9 Behavior of the App
63(1)
2.12.10 Produce Data to Plot in MATLAB
63(2)
2.12.11 Pop-Up Menu Characteristics
65(1)
2.12.12 Behavior of Push Button
66(1)
2.13 Effective Programming through MATLAB
67(2)
2.13.1 Condition
68(1)
2.13.2 Practice Programs
68(1)
2.13.3 Specific Functions in MATLAB
69(1)
2.14 Clones Process Using MATLAB
69(2)
2.14.1 GNU Octave
69(1)
2.14.2 Scilab
70(1)
2.14.3 Sage
70(1)
2.15 Parallel MATLAB System
71(3)
2.15.1 Run a Batch Job
71(1)
2.15.2 Run a Batch Parallel Loop
72(1)
2.15.3 Current Folder Browser - Run Script as Batch Job
73(1)
2.16 Conclusion
74(1)
References
75(2)
3 Digital Communication System Simulation Using MATLAB
77(34)
3.1 Introduction to Digital Communication
78(5)
3.1.1 Data Transmission
78(1)
3.1.2 Example
79(1)
3.1.3 The Conversion of Analog and Digital Signals
80(2)
3.1.4 Information, Bandwidth, and Noise
82(1)
3.2 Simulation of Rayleigh Fading Model
83(3)
3.2.1 Rayleigh Fading Basics
83(1)
3.2.2 Rayleigh Fading
84(2)
3.3 BPSK Modulation and Demodulation
86(3)
3.3.1 BPSK Modulation
86(1)
3.3.2 BPSK Demodulation
87(2)
3.4 QPSK Modulation and Demodulation
89(5)
3.4.1 QPSK Transmitter
90(3)
3.4.2 QPSK Receiver
93(1)
3.4.3 Performance Simulation over AWGN
93(1)
3.5 Image Error Rate vs Signal-to-Noise Ratio
94(5)
3.5.1 M-QAM Modulation
94(1)
3.5.2 Baseband Rectangular M-QAM Modulator
95(4)
3.6 Recreation of OFDM Framework
99(9)
3.6.1 Figuring (E/N0) or (Eb/N0) for OFDM Framework
101(1)
3.6.2 Impact of Cyclic Prefix on E/N
101(1)
3.6.3 Effect of Unused Subcarriers on Es/N
102(1)
3.6.4 Arrangement of Subcarriers
103(1)
3.6.5 MATLAB Sample Code
103(5)
3.7 Conclusion
108(1)
References
109(2)
4 Statistical Analysis of Network Data Using MATLAB
111(44)
4.1 Introduction to Association Networks
112(2)
4.2 Time Series, Stationary, Time Series Decomposition, De-trending
114(10)
4.2.1 Time Series Analysis
114(1)
4.2.2 Stationarity
115(2)
4.2.3 Time Series Decomposition
117(1)
4.2.4 De-trending
118(6)
4.3 Autocorrelation, Test for Independence, Linear Autoregressive Models
124(15)
4.3.1 Autocorrelation
124(2)
4.3.2 ACF and IACF Parameters
126(2)
4.3.3 Test of Independence
128(7)
4.3.4 Linear Autoregressive Models
135(2)
4.3.5 Linear Prediction and Autoregressive Modeling
137(2)
4.4 Mutual Information and Test for Independence
139(4)
4.4.1 Testing the Significance of the Null Hypothesis I(X; Y) = 0
139(2)
4.4.2 Producing the Mutual Information Distribution from Surrogates
141(2)
4.5 Spurious Cross-Correlation, Vector Autoregressive Models and Dynamic Regression Models
143(7)
4.5.1 Cross Correlation
143(3)
4.5.2 Vector Autoregression (VAR) Models
146(3)
4.5.3 Coupled Dynamical Systems
149(1)
4.6 Conclusion
150(1)
References
150(5)
5 Network Routing Simulation Using MATLAB
155(54)
5.1 Evaluation of Granger Causality Measures on Known Systems
156(17)
5.1.1 A Historical Viewpoint
158(6)
5.1.2 Application to Recreated Information
164(6)
5.1.3 Application to FMRI BOLD Information from a Visuospatial Consideration Undertaking
170(3)
5.2 Demand Modeling and Performance Measurement
173(5)
5.2.1 Objectives
173(1)
5.2.2 Approach to Model Development
174(1)
5.2.3 Development of Models
175(1)
5.2.4 Outline of Findings from Phase Two: Model Validation
176(2)
5.3 Universal Algorithms and Sequential Algorithms
178(12)
5.3.1 Genetic Algorithm for Improvement Utilizing MATLAB
178(4)
5.3.2 Masses Diversity-Measure-Run, Prosperity Scaling
182(8)
5.4 Acoustic-Centric and Radio-Centric Algorithms
190(4)
5.5 AODV Routing Protocol
194(9)
5.5.1 Keeping Up Sequence Numbers
196(1)
5.5.2 Association Breaks
196(1)
5.5.3 Neighborhood Repairs
197(1)
5.5.4 Security Considerations
197(6)
5.6 Conclusion
203(1)
References
204(5)
6 Wireless Network Simulation Using MATLAB
209(58)
6.1 Radio Propagation for Shadowing Methods
210(24)
6.1.1 Radio Propagation Modeling
210(1)
6.1.2 Partition Dependence
210(1)
6.1.3 Small-Scale Blurring
210(1)
6.1.4 Free-Space Propagation
211(1)
6.1.5 Ray Tracing
212(8)
6.1.6 Indoor Propagation
220(1)
6.1.7 Classic Empirical Models
221(1)
6.1.8 COST 231-Hata Model
221(1)
6.1.9 COST 231-Walfish-Ikegami Model
222(2)
6.1.10 Erceg Model
224(1)
6.1.11 Multiple Slope Models
225(9)
6.2 Mobility: Arbitrary Waypoint Demonstrates
234(1)
6.2.1 Random Waypoint Model
234(1)
6.2.2 Regular Problems with Random Waypoint Model
235(1)
6.2.3 Irregular Waypoint on the Border (RWPB)
235(1)
6.2.4 Markovian Waypoint Model
235(1)
6.3 PHY: SNR-Based Bundle Catches, Communication, Dynamic Transmission Rate and Power
235(3)
6.3.1 MAC: IEEE 802.11
236(1)
6.3.2 IEEE 802.11 RTS/CTS Exchange
237(1)
6.4 NET: Ad Hoc Routing
238(16)
6.4.1 Dynamic Destination Sequenced Distance Vector
240(3)
6.4.2 Wireless Routing Protocol
243(1)
6.4.3 Global State Routing
243(1)
6.4.4 Fisheye State Routing
244(1)
6.4.5 Hierarchical State Routing
244(1)
6.4.6 Zone-Based Hierarchical Link State Routing Protocol
245(1)
6.4.7 Clusterhead Gateway Switch Routing Protocol
246(1)
6.4.8 Cluster-Based Routing Protocols
247(1)
6.4.9 Ad Hoc On-Demand Distance Vector Routing
248(1)
6.4.10 Dynamic Source Routing Protocol
249(1)
6.4.11 Temporally Ordered Routing Algorithm
250(2)
6.4.12 Associativity-based Routing
252(1)
6.4.13 Signal Stability Routing
253(1)
6.5 APP: Overlay Routing Protocols
254(5)
6.5.1 System/Application Designs, Optimizations, and Implementations on Overlay Networks
254(1)
6.5.2 Routing Overlays for VoIP
255(1)
6.5.3 Measurement, Modeling, and Improvement of BitTorrent Overlays
256(3)
6.6 Conclusion
259(1)
References
260(7)
7 Mobility Modeling for Vehicular Communication Networks Using MATLAB
267(24)
7.1 Vehicle Network Toolbox
268(6)
7.1.1 Transmit and Receive CAN Messages
268(3)
7.1.2 Examine Received Messages
271(1)
7.1.3 CAN Message Reception Callback Function
272(2)
7.2 Network Management (NM)
274(3)
7.2.1 Plan Your Network Installation
274(1)
7.2.2 Planning Your Network Installation
275(1)
7.2.3 Setting Up a Remote Client Access Configuration
275(1)
7.2.4 Setting Up Local Client Access Configuration
275(2)
7.3 Interaction Layer
277(8)
7.3.1 Directing Protocols in MANET
278(1)
7.3.2 Specially Appointed On-Demand Distance Vector
278(1)
7.3.3 Dynamic Source Routing (DSR)
278(1)
7.3.4 Diagram of Mobility Model
279(1)
7.3.5 Results and Analysis
280(2)
7.3.6 Association Variation Results
282(3)
7.4 Transport Protocols
285(2)
7.4.1 TCP Transport Protocol
285(1)
7.4.2 User Datagram Protocol, or UDP
286(1)
7.4.3 Reliable Data Protocol, or RDP
286(1)
7.4.4 Transmission Control Protocol, or TCP
286(1)
7.5 Conclusion
287(1)
References
288(3)
8 Case Studies and Sample Codes
291
8.1 Case Determination and Structure
292(4)
8.1.1 Exhibiting Analysis
293(1)
8.1.2 Case Example
293(1)
8.1.3 The Best Strategy
293(1)
8.1.4 Impediment of the Technique
293(1)
8.1.5 Sorts of Contextual Investigations
294(1)
8.1.6 Relevant Examinations in Business
294(1)
8.1.7 Summing Up from Logical Investigations
294(1)
8.1.8 History
295(1)
8.1.9 Related Vocations
295(1)
8.2 Case Study 1: Gas Online
296(6)
8.2.1 Load Data into Project
296(1)
8.2.2 Construct Boundary Models
296(6)
8.3 Case Study 2
302(4)
8.3.1 Case Is Create a Credit Scorecard Dissent
302(2)
8.3.2 Case 2: Binning Information
304(2)
8.4 Case Study 3: Random Waypoint Mobility Model
306(6)
8.5 Case Study 4: Node localization in Wireless Sensor Network
312(13)
8.6 Case Study 5: LEACH Routing Protocol for a WSN
325(9)
8.7 Conclusion
334(1)
References
334
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.
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