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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-63143-9

List of Figures

1.1 System modeling and simulation scheme.
1.2 Network simulator abstraction.
1.3 Discrete-event simulation.
1.4 Principles of discrete-event simulation and the system state changes only at discrete point’s t_i in time during the simulation.
1.5 Flow chart explaining a discrete-event test system.
1.6 Wireless sensor network model.
1.7 Tier-based node model.
2.1 MathWorks MATLAB R2018b
2.2 MATLAB decision.
2.3 MATLAB loops.
2.4 MATLAB elements.
2.5 Create an M-File.
2.6 How to add new toolbox to MATLAB.
2.7 Initializing deploy tool in MATLAB.
2.8 Initializing standalone application.
2.9 Initilazing toolboxes required.
2.10 Inspecting MATLAB GUI.
2.11 Simple GUI.
2.12 Selecting callback options.
2.13 Selecting callback options 2.
2.14 Running script as batch job.
3.1 Data transmission system.
3.2 Time domain and frequency domain representation of a signal.
3.3 Analog-to-digital conversion.
3.4 Upper panel: Periodic sampling of an analog signal. Bottom panel: Quantization of a sampled signal.
3.5 Simulation results.
3.6 BPSK modulator.
3.7 BPSK demodulator.
3.8 Sampling and results.
3.9 Waveform simulation model for QPSK modulation.
3.10 Timing diagram for BPSK and QPSK modulations.
3.11 Waveform simulation model for QPSK demodulation.
3.12 Simulated QPSK waveforms at the transmitter side.
3.13 M-QAM modulator.
3.14 M-QAM simulation model.
3.15 Samples of in-phase component.
3.16 An OFDM communication architecture with a cyclic prefix.
3.17 Effect of cyclic prefix on E_s/N₀.
3.18 Assignment of OFDM subcarriers.
3.19 E_b/N₀ Vs BER for BPSKOFDM over AWGN.
3.20 OFDM essential Tx-Rx chain.
3.21 BER results.
4.1 Flowchart of association rules implementation.
4.2 (a) ARN for contact lens data with target node as hard; (b) ARN for contact lens data with target node as soft.
4.3 Schematic diagram showing the activity method of communication in a software with ARN.
4.4 Time series plot for adjusted AUTOSALE/CPI.
4.5 Time series chart with obtained output form.
4.6 Output of wave configeration.
4.7 Error rates identification.
4.8 Density estimate for power spectral.
4.9 Density estimate for periodogram spectral.
4.10 Plottings of the estimate.
4.11 Markov plottings.
4.12 Parameters of new functions.
4.13 Replot status.
4.14 VAR models.
5.1 Each middle purpose of the structure is related to a signal. Edges between focus remain for reliance (shared data) between the signs.
5.2 Basic driving cases that can incite fake unmistakable check of huge Granger causality.
5.3 Granger causality patterns between simulated ROIs.
5.4 Comparison of model estimation by LASSO-GC and pairwise GC.
5.5 Correlation of LASSO-GC and pairwise-GC techniques in recouping the summary measurement.
5.6 Correlation of LASSO-GC and pairwise-GC techniques in recuperating the W summary measurement.
5.7 Examination of network designs with LASSO-GC and connection measures.
5.8 Utilitarian availability examination of dorsal attention network and visual occipital cortex in visualspatial consideration.
5.9 Different phases of approach in attributes.
5.10 GAs in MATLAB’s Optimization Toolbox.
5.11 Rastrigin’s function optimization with the default setting.
5.12 Rastrigin’s function optimization with the default setting, except Stopping Criteria-Stall Generations set 100 and initial range set [1; 1.1].
5.13 Rastrigin’s function optimization with the default setting, except Stopping Criteria-Stall Generations set 100 and initial range set [1; 1.1].
5.14 Rastrigin’s function optimization with the default setting, except Stopping Criteria-Stall Generations set 100 and initial range set [1; 2].
5.15 Lower raw fitness value.
5.16 Higher raw fitness value.
5.17 Stochastic uniform selection method.
5.18 Elite count 10.
5.19 Elite count 3.
5.20 Elite count 1.
5.21 Estimation of frequency relation to error over time.
5.22 Radiation readings for distributed beamforming.
6.1 Spherical free-space propagation: power transmitted over the air radiates spherically, with different gain in different directions. A portion of that power is received on an active aperture area A_e of the receiving antenna.
6.2 Ray model geometry.
6.3 Simple propagation models: free-space one-slope direct line of sight, and two-ray with direct ray and ground reflected ray. In some places, signal adds constructively, in others phase differences cause deep fades.
6.4 Vertical polarization ground reflection coefficient.
6.5 Horizontal polarization ground reflection coefficient.
6.6 Six-ray model (R₀;R₁;R₂) and ten-ray model (R₀ to R₄) geometry: each line hides two rays, one direct, the other bouncing off the ground.
6.7 Ray tracing plots of received signal power indicator as a function of log d₀ for N ∊ {0, 2, 3, 5} for a typical suburban case with street width of 20 feet, and the average distance from the street to the home of w_t = 10 feet (so w_s = 40 feet).
6.8 Ray-tracing geometry for a street corridor; some rays escape the corridor through gaps between homes.
6.9 Ray-tracing power levels down a street, with gaps between homes.
6.10 Beam following impinging on home dividers.
6.11 Angles of incidence illuminating homes in an urban corridor.
6.12 Demonstration of street orientation angle φ for use in COST 231 Walfish-Ikegami model.
6.13 Tree foliage attenuation as a function of frequency.
6.14 COST-231 indoor penetration loss model.
6.15 Penetration odd into private structures, total thickness dispersion.
6.16 In-building loss for residential buildings: measurement campaigns published for different frequencies, in different residential areas.
6.17 In-building loss for urban office buildings and high-rises: measurement campaigns published for different frequencies, in different urban areas.
6.18 Random waypoint model.
6.19 MAC layers in IEEE 802.11 standard.
6.20 RTS/CTS mechanism.
6.21 Ad hoc routing protocols.
6.22 Example of distance vector routing.
6.23 Accuracy of information in FSR.
6.24 An example of clustering in HSR.
6.25 Example of CGSR routing from node 1 to node 12.
6.26 Example of CBRP protocol.
6.27 Route discovery in AODV.
6.28 Creation of record route in DSRP.
6.29 Example of route creation in TORA.
6.30 Re-establishing route on failure of link 5-7. The new reference level is node 5.
6.31 Associativity-based routing.
6.32 An illustrative example of overlay networks.
6.33 A sample overlay network.
6.34 Overlay network broken up into logical layers.
6.35 Overlay network over the Internet.
7.1 Timestamp identification.
7.2 Execute the callback function.
7.3 Remote client access configuration setup.
7.4 Local client access configuration setup.
7.5 PDR vs Node Mobility.
7.6 Packet Loss Rate vs Node Mobility.
7.7 Routing Overhead vs Node Mobility.
7.8 Average End-to-End Delay vs Node Mobility.
7.9 Packet Delivery Ratio vs Number of Connections.
7.10 Routing Overhead vs Number of Connections.
7.11 Packet Loss Rate vs Number of Connections.
7.12 Average End-to-End Delay vs Number of Connections.
8.1 Two-stage model of torque against spark.
8.2 ResStatus plot.
8.3 CustIncome plot.
8.4 CustIncome plot.
8.5 TmAtAddress plot.
8.6 ResStatus plot.
8.7 EmpStatus plot.
8.8 Random waypoint mobility model.
8.9 The location of target and anchor nodes in WSN.
8.10 (a) Simulated path loss model; (b) Histogram of simulated RSS noise modeled as Gaussian mixture.
8.11 RMSE vs N (number of anchors).
8.12 CDF vs error.
8.13 Cluster formation in LEACH.
8.14 Steady-state phase in LEACH.
8.15 LEACH simulation.

List of Tables

1.1 Summary of the most important network simulators.
2.1 Operators and special characters.
2.2 Unique variables and constants.
2.3 Variables and constants.
2.4 Articulations.
2.5 Loop control statements.
2.6 Low-level file I/O functions.
2.7 Framework operations.
2.8 Setting colors.
2.9 Calling objects functions.
2.10 Java class functions.
4.1 MeansRT and error rates under each condition.
4.2 Relations between different networks and test sessions.
4.3 Techniques related to VARM.
5.1 The fraction of non-zero coefficients in each of the 4 submatrices.
5.2 Methodologies related to operations.
5.3 Filtering mechanisms for different models in structural problems.
6.1 Relative permittivity.
6.2 Values for COST 231 Hata and modified Hata models.
6.3 Values of a(h_M) for COST 231 Hata model according to city size.
6.4 Values of C_M for COST 231 Hata model according to city size.
6.5 Values for COST 231 Walfish-Ikegami model.
6.6 Values for Erceg model.
6.7 Values for Erceg model parameters in various terrain categories.
6.8 Vegetation loss caused by tree foliage reported for various frequencies: single-tree model loss in dB, and dB/m loss.
6.9 TGn channel models A to F are used to model MIMO systems in a different environment, with different RMS delay spreads.
6.10 TGn channel models A to F use two slopes n₁ = 2 near transmitter and different values of n₂ beyond a critical distance d0.
6.11 Building penetration loss from COST-231 model.
6.12 Private structures penetration loss: middle odd (μ_i) and standard deviation (σ_i) from exploratory outcomes announced for different frequencies.
6.13 Penetration loss into vehicles: median loss (μ_v) and standard deviation (σ_v) from experimental results reported at various frequencies.
7.1 Parameter values in simulation.

Preface

This book is organized into eight chapters. In Chapter 1 a detailed overview is provided on how MATLAB can be used for network simulation and modeling. Then various types of simulation are described, followed by their working principles and different terminologies, along with the algorithms governing these simulations. The chapter also describes the selection of various software simulations for MATLAB, and the simulation tools based on high performance, followed by the different network models. This chapter will effectively help readers understand the concepts more clearly and provide them with a clear understanding of how to perform these tasks in MATLAB.

In Chapter 2, the power of MATLAB for computational mathematics is shown, followed by a detailed description of the features. A detailed discussion of various areas of MATLAB use is provided. We also discuss multiple notations, operators, and syntax and give hands-on practical examples along with various loop structures and decision controls, import, and export operations, using and creating M-files, different types of plotting and graphs, etc. Also explained are the various clones of MATLAB. This chapter will effectively help readers understand the workings and uses as well as applications of MATLAB more clearly.

In Chapter 3, we explain how digital communication system simulations can be performed using MATLAB. A detailed introduction to digital communications, i.e, data transmission, is provided along with an example. Next, a detailed explanation of simulations of Rayleigh fading model, BPSK modulation and demodulation, QPSK (Quadrature Phase Shift Keying) modulation and demodulation is given, along with their MATLAB coding, and the output is shown as well. We image error rate vs signal-to-noise ratio and OFDM with sample MATLAB coding and their output.

In Chapter 4, we explain how to perform statistical analysis of network data utilizing MATLAB starting from affiliation systems/networks with examples. Time series analysis, statistical stationarity, time series decomposition, de-trending, curve fitting, digital filtering, recurrence reaction, and the connection between recurrence reaction to spline parameter are also explained. Next, details are provided about autocorrelation, test for independence, linear autoregressive models, etc.

In Chapter 5, we explain how network routing simulations can be done using MATLAB. Additionally, deep insights are provided about the evaluation of Granger causality measures on known systems, along with results. We explain the application to fMRI BOLD (Blood-Oxygenation-Level-Dependent) information from a visuospatial consideration undertaking followed by various model development approaches, models validation, universal algorithms, and sequential algorithms, acoustic-centric and radio-centric algorithms, AODV routing protocols, etc.

In Chapter 6, we explain how wireless network simulations can be done using MATLAB. We explain how shadowing methods are used for radio propagation, two-ray model, indoor propagation, classical empirical model, Hata model, Walfisch-Ikegami model, Erceg model, multi-slope model, dispersive model, 3GPP SCM, MAC: IEEE 802.11 (CSMA/CA, virtual carrier sense, and RTS-CTS-DATA-ACK), NET-ad hoc routing, APP-overlay routing protocols, etc.

In Chapter 7, we explain various layers and protocols such as Vehicle Network Toolbox. In this toolbox, an explanation is given on how to make a receiving channel, how to access a chain, how to start a channel, how to transmit a message, etc. Next, the topic of network management (NM) is presented, in which a detailed explanation is given of network installation planning, setting up a remote client access configuration, interaction layer, directing protocols in MANET, along with their results and analysis, and transport layer with protocols.

In Chapter 8, a detailed explanation is provided of various real-time scenarios by using case studies taken from multiple real-time situations with their sample codes and result from analysis for a better understanding of how MATLAB performed in these situations and how it can solve critical simulation problems in detail.

Dr. Dac-Nhuong Le

Deputy-Head, Faculty of Information Technology Haiphong University, Haiphong, Vietnam

Abhishek K. Pandey

Assistant Professor (Computer Science Engineering) ACERC, Visiting faculty, Mdsu, Ajmer Rajasthan, India

Sairam Tadepalli

Junior Data Scientist, AWS Architect, Python Developer

Pramod Singh Rathore

Assistant Professor (Computer Science Engineering) ACERC, Visiting faculty, Mdsu Ajmer Rajasthan, India

Jyotir Moy Chatterjee

Assistant Professor (IT) Lord Buddha Education Foundation (APUTI), Kathmandu, Nepal

Acknowledgments

First of all, I would like to thank the authors for contributing their chapters to this book. Without their contributions, this book would not have been possible. Thanks to all my associates for sharing my happiness at the start of this project and following up with their encouragement when it seemed to be too difficult to complete.

I want to acknowledge and thank the most important people in my life, my grandfather, grandmother, and finally thanks to my wife. This book has been a long-cherished dream of mine which would not have been turned into reality without the support and love of these fantastic people, who encouraged me despite my not giving them the proper time and attention. I am also grateful to my best associates for their blessings and unconditional love, patience, and encouragement.

Dac-Nhuong Le

Deputy-Head, Faculty of Information Technology Haiphong University, Haiphong, Vietnam

Writing a book is harder than I thought and more rewarding than I could have ever imagined. First and foremost, I would like to thank my father Mr. Krishan Dev Pandey for being the coolest father ever and my mother Mrs. Veena Pandey for allowing me to follow my ambitions throughout my childhood. They taught me discipline, tough love, manners, respect, and so much more that has helped me succeed in life. Also, I would like to express my gratitude to my elder sister Mrs. Arpna Tripathi, who always stood by me during every struggle and all my successes. She has been my inspiration and motivation for continuing to improve my knowledge and move my career forward. Also, I’m eternally grateful to my wife, Mrs. Kajal Pandey, for standing beside me throughout my career and the writing of this book. I also thank my wonderful son, Aarudra Pandey, for always making me smile and for understanding on those weekend mornings when I was writing this book instead of playing games with him. I hope that one day he can read it and understand why I spent so much time in front of my computer. Last but not the least, I want to thank my friends who always backed me up during both good and bad days and everyone who ever said anything positive to me or taught me something. I heard it all, and it meant something.

Abhishek K. Pandey

Assistant Professor (Computer Science Engineering) ACERC, Visiting faculty, Mdsu, Ajmer Rajasthan, India.

Acronyms

AAL: Ambient Assistive Learning

ABR: Associativity Based Routing

ACK: Acknowledgement

AODV: Ad hoc On-Demand Distance Vector

ASK: Amplitude-Shift Keying

ANT: Attention Network Test

ASW: Apparent Source Width

ARIMA: Autoregressive Integrated-Moving-Average

AR: Auto Regressive

AS: Autonomous System

ASAP: AS-Aware Peer-relay Protocol

B.A.T.M.A.N: Better Approach To Mobile Adhoc Networking

BPSK: Double Phase Shift Keying

BOLD: Blood-Oxygenation-Level-Dependent

CDF: Cumulative Distribution Function

CSMA/CA: Carrier-Sense Multiple Access with Collision Avoidance

CSMA/CD: Carrier Sense Multiple Access with Collision Detect

CLPC: Cross-Layer setup approach for Power Control

CTS: Clear to Send

CRLB: Cramer-Rao Lower Bound

CBRP: Cluster based Routing Protocol

CGSR: Clusterhead Gateway Switch Routing

DSR: Dynamic Source Routing

DAN: Dorsal Attention Network

DREAM: Distance Routing Effect Algorithm for Mobility

DSSS: Direct Sequence Spread Spectrum

DSDV: Destination-Sequenced Distance-Vector Routing

DSRP: Dynamic Source Routing Protocol

DRP: Dynamic Routing Protocol

DHT: Distributed Hash Table

ECG: Electrocardiogram

EEG: Electroencephalogram

FMRI: Functional Magnetic Resonance Imaging

FEF: Frontal Eye Field

FHSS: Frequency Hopping Spread Spectrum

FFT: Fast Fourier Transform

FSR: Fisheye State Routing

GC: Granger Causality

GA: Genetic Algorithm

GM-SDP: Gaussian Mixture-SemiDefinite Programming

GUI: Graphical User Interface

GSR: Global State Routing

IACF: Interaural Cross correlation Function

IEEE: Institute of Electrical and Electronics Engineers

ITU-T: International Telecommunication Union-Telecommunication

JiST: Java in Simulation Time

LASSO: Least Absolute Shrinkage and Selection Operator

LEACH: Low Energy Adaptive Clustering Hierarchy

LOS: Line of Sight

LCC: Least Cluster Change

LUT: Look-Up-Table

M-QAM M-ary: Quadrature Amplitude Modulation

MWP: Markovian Waypoint Model

MANET: Mobile Ad-hoc Network

MVAR: Multivariate Vector Auto Regressive

MIMO: Multiple In, Multiple Out

MWP: Markovian Waypoint Model

MRL: Message Retransmission List

MUEL: Minimize Usage Extend Life

NM: Network Management

NS: Network Simulator

NHST: Null Hypothesis Significance Testing

NLOS: Non Line of Sight

OFDM: Orthogonal Frequency Division Modulation

OLSR: Optimized Link State Routing Protocol

OS: Operating System

OMNET: Object-Oriented Modular Discrete Event Simulator

PAN: Personal Area Network

PHY: Physical Layer

P2P: Peer-to-Peer

PLL: Phase Locked Loop

PSK: Phase-Shift Keying

PROWLER: Probabilistic Wireless Network Simulator

QPSK: Quadrature Phase Shift Keying

QAM: Quadrature Amplitude Modulation

RWPB: Irregular Waypoint on the Border

RWP: Arbitrary Waypoint

RWPB: Discretionary Waypoint on the Periphery

RERR: Route Error

RREP: Route Reply

RPGM: Reference Point Group Mobility Model

RDP: Reliable Data Protocol

ROI: ROI Region of Interest

RON: Solid Overlay Networks

RTS: Request to Send

RREQ: Route Request

RT: Response Time

RFC: Request for Comments

RSS: Received Signal Strength

RMSE: Root Mean Square Error

SSR: Signal Stability Routing

SSF: Scalable Simulation Framework

SNAP: Simulation Analysis Platform

SST: Signal Stability Table

SRP: Static Routing Protocol

SCM: Spatial Channel Model

SDP: Semidefinite Programming

SNR: Signal-to-Noise Ratio

SwaP: Weight and Power

SWANS: Scalable Wireless Ad hoc Network Simulator

TORA: Temporally Ordered Routing Algorithm

TCP: Transmission Control Protocol

TCP/IP: Transmission Control Protocol/Internet Protocol

TTL: Time to Live

TOSSIM: Tiny Operating System Sensor Network

TDMA: Time Division Multiple Access

UDP: User Datagram Protocol

VAR: Vector Autoregression

VANET: Vehicular Ad hoc Network

VOC: Visual Occipital Cortex

VPN: Virtual Private Network

VoIP: Voice over Internet Protocol

ZRP: Zone Routing Protocol

ZHLS: Zone-based Hierarchical Link State Routing Protocol

WRP: Wireless Routing Protocol

WLS: Weighed Least Square

WSN: Wireless Sensor Network

WOE: Weight of Evidence

WIMAX: Worldwide Interoperability for Microwave Access

3GPP: 3rd Generation Partnership Project

Network Modeling, Simulation and Analysis in MATLAB

Theory and Practices

List of Figures

List of Tables

Foreword

Preface

Acknowledgments

Acronyms