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International Journal of Innovative Technology & Creative Engineering Vol.11 No.6 June 2021
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.5 JUNE 2021
Dear Researcher, Greetings! Articles in this issue discusses about Report on enhancing network security using cryptography and steganography and Simulation Configuration and Performance Metrics EZBRP, SACBRP, BIABRP It has been an absolute pleasure to present you articles that you wish to read. We look forward many more new technologies in the next month. Thanks, Editorial Team IJITCE
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DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE
Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.5 JUNE 2021 Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688
Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.5 JUNE 2021 Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
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Contents Enhancing network security using cryptography and steganography ……………………………...….…. [ 978 ] Simulation Configuration and Performance Metrics EZBRP, SACBRP, BIABRP …..……….……………. [ 983 ]
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ENHANCING NETWORK SECURITY USING CRYPTOGRAPHY AND STEGANOGRAPHY Bahia Khalfan Al-Dhikhri #1, Shaima Ali Salim Al-Habsi#2, Dr. M. Senthilkumar#3 Department of Information Technology, University of Technology and Applied Sciences, Ibra, Sultanate of Oman. 1
36J16112@ict.edu.om 36J16104@ict.edu.om
2
3senthilm@ict.edu.om
for
Abstract
business,
government
individuals.
Nowadays, communication is very important
Cryptography is
and developed a lot. In digital communication
secure
it’s important to secure the transmitted
converting the text into some disgusted form
information and data between sender and
so that only the intended user can remove that
receiver. In this research, we present network
disgust and can read the original secret
security techniques to protect networks
message. It is a way to change data to
using cryptography and steganography in
unreadable format for an illegal user. The
Oman. We are discussing about the network
encrypted information cannot be read without a
security
using
key. Although, steganography is a way of
addition,
securing data inside another form of data.
steganography techniques also apply for the
Steganography methods can be applied to
network protection to enhance the security.
images, video or audio file.
Keywords:
However,
to
transfer
cryptography
the
algorithm.
Cryptography,
data In
Steganography,
I.
transmission of
steganography
information by
is
written
world. Then these networks need protection to the
information
in
in
with hash
marking, however its usage inside pictures is
INTRODUCTION
Day by day, the networks spread fast in the all
the methodology of
characters together
Protection
secure
that
and
networked
additionally common. Moreover, Networks need to
be
protect
using
cryptography
and
steganography. II.
computers. Network security is access protection
RELATED SURVEY
of the files and directories in a computer network.
Recent cryptography doing a powerful approach
Data transmission through the network devices
and plans new cryptographic algorithms about
should
user
computational hardness expectations. It is
authenticating. Network security have both public
hypothetically possible to breakdown, but it is
and private computer networks, which are daily
difficult
used in jobs transactions and communications
Cryptography is applied to secure army related
be
protected
by
assigning
978
to
do
any
practical
means
[2].
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information to secure the national security.
• Stream algorithms: Work on plaintext one byte
Steganography system can be applied using two
at a time, where a byte is a number, character or
techniques. Initially, the longitudinal domain
special character.
based steganography, the secret message bits replace where the least significant bits (LSB) of
• Block algorithms: Work on plaintext in groups of
the cover object. Furthermore, the transform
bytes, called blocks. Typical block sizes for new
domain based steganography [4]. Network
algorithms are the 64-bytes, little suffice that can
investigation and observing systems will not flag
be work with, however it big enough so codes
messages or files that contain steganographic
cannot be break. Regrettably, with the present
data. Therefore, if anyone tried to take important
microprocessors speed, it shows to be relatively
personal data, they could hide it within another
easy task breaking the 64-byte algorithm that
file and send it [6].
used brute force
Cloud Computing
3.1.1
Types of Cryptography:
•
Secret Key Cryptography
Cryptography is a way of transferring the data
•
Public-Key Cryptography
over the Internet by the cryptographic algorithms
•
Hash Functions
III. CRYPTOGRAPHY
to be difficult to attack the confidential or private data. Cryptography done with two terms, encryption and decryption. The process of transferring a message text to cipher text called cryptography.
Moreover,
decryption
is
3.1.2 Advantages and Disadvantages: •
the
opposite process of encryption.
Advantages o
message hiding and safe privacy.
o
You can write whatever you want and however you want to keep your code a secret.
3.1 CRYPTOGRAPHY ALGORITHM
•
Disadvantages
Its mathematical formula utilized to mix the
o
Taking too much of time.
plaintext characters into cipher text. Encryption
o
If you were to send a code to another
means converting plaintext to cipher text using
person in the past, it will take long to get
the cryptography algorithms, and decryption is
to that person.
transforming cipher text back to plaintext using
o
It’s a long process.
the same cryptographic algorithm. Cryptography
algorithms
divided
to
two
categories:
IV.
979
STEGANOGRAPHY
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4.1.2 Steganography
a
method
of
embedding
The Steganography model consists of three components
secreted message/data in a way to be no one enabled to identify the result of the messages, except the sender and receiver. It’s important to disguise the sensitive data or information so nobody can detect.
4.1.1. Types of Steganography: Figure -1 – Steganography Models
•
Text Techniques: this technique hides a data which known as text steganography using
4.1.3
Steganography Techniques:
text media. It hides the text behind the other text file.
•
•
Spatial Domain methods: These methods
Image Techniques: this technique uses
hide data by changing some bits in the image
images as cover object. Steganography has
pixel value. There are many spatial domain
two steps process:
methods such as, least significant bits (LSB),
1) Creating a steganography image, which is
Pixel values differencing (PVD), Edges
the mixture of message and carrier.
based data embedding method (EBE) and
2) Extracting the message image from the
Pixel intensity based LSB.
steganography image.
•
•
Audio Steganography: this method embeds
•
Transform
Domain
techniques:
this
the secret data sound to be act as cover
technique is embedding the secret data in the
media. Such as, MP3, WAV and AU sound
transform or frequency domains of the cover
files.
object. There are many different algorithms
Video steganography: This technique is
and
mixing sound and image and sending it
information in an image. This technique is
together
more strong and complex. There are some
in
combine
form
over
the
transmission medium.
transformations
used
for
hiding
transform domain techniques such as, DFT (discrete Fourier transformation technique), DCT
(discrete
cosine
transformation
technique) and DWT (discrete wavelet transformation technique).
980
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•
Masking and Filtering: This method hides the
that students of schools and colleges only
secret data or message in the more important
about 30% who know about cryptography
parts by marking an image. This technique is
and steganography. Most students of the
stronger than LSB technique. This technique
30% are whom specialists in information
has limitation which is this method can be
security and networks or taking self-learning
useful only for the gray scale images and 24
courses
bits images.
steganography. However, only half of the
about
cryptography
and
employees that we have met known about APPLYING NETWORK PROTECTION USING
V.
CRYPTOGRAPHY AND STEGANOGRAPHY IN
OMAN
cryptography and steganography. 5.1.2
Applying cryptography and steganography courses in schools and colleges:
We are thinking to add these courses
people known about cryptography and steganography
because nowadays many people are facing
60%
network problems like, hacking, account
40%
stooling and others. In addition, people
20%
should protect their data or information using
0% Students
Employees
cryptography and steganography by joining
Others
to these courses. Moreover, we want
Known
stakeholders (Ministry of Education and Ministry of
Manpower)
to employ
the
specialist’s staff and teachers that will help people to improve.
Figure – 2 – Analysis Graph
5.1.3
and steganography in Oman:
5.1.1. How people in Oman know about •
cryptography and steganography •
Because
of
modern
technologies
•
Protection of
knowledge that
background about how they protect their
has shared between computers on the
networks. We have asked different kinds of
Oman networks.
people like, students, employees and others.
•
People in Oman can avoid network problems.
As shown in this Chart. •
Protecting organizations and intuitions information or data in Oman.
and
networking services, people not have enough
Benefits of Applying cryptography
This chart show people whom known about cryptography and steganography. We found
981
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CONCLUSION
VI.
of Computer Science and Network Security, VOL.16 No.4, April 2016
In conclusion, as we mention many people are need to protect their sensitive data to avoid the detection from third party. People have to secure their sensitive data before transporting or storing it. Cryptography and steganography
[6] Harpreet Kaur1, Jyoti Rani, “A Survey on different techniques of steganography”, MATEC Web of Conferences DOI: 10.1051/ 57, 02003 (2016) matec 57 conf/2016 0 ICAET 2016
techniques act as solution to solve these security
[7] Sarita Kumari, “A research Paper on
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Simulation Configuration and Performance Metrics EZBRP, SACBRP, BIABRP #1
Senthil Jayapal, #2Annadurai Manickam nadar, #3Ramesh Palanisamy Department of Information Technology, University of Technology and Applied Sciences- IBRA Sultanate of Oman 1jayapal@ict.edu.om, 2annadurai@ict.edu.om, 3palanisamy@ict.edu.om
Abstract-Network Simulator (Version 2), widely
developers in the community are constantly
known as NS2, is simply an event-driven
working to continue to ensure that NS2 strong
simulation tool that has proved useful in studying
and versatile.
the dynamic nature of communication networks. Both wired and wireless network functions and
2. ARCHITECTURE OF NS2
protocols (e.g., routing algorithms, TCP, UDP)
Fig 6.1, shows the basic architecture of
can be simulated using NS2. In general, NS2
NS2. NS2 provides users with an executable
provides users with a method of specifying such
command ns which takes on input argument, the
network
name of a Tcl simulation scripting file. Users feed
protocols
and
simulating
their
the name of a Tcl simulation script (which sets up
corresponding behaviors.
a simulation) as an input argument of an NS2 executable command ns. In most cases, a
1. INTRODUCTION Ever since its birth in 1989, NS2 has
simulation trace file is created, and is used to plot
gained constant popularity in the networking
graph and/or to create animation. NS2 consists
research community due to its flexibility and
of two key languages: C++ and Object-oriented
modular
and
Tool Command Language (OTcl). While the C++
revisions have marked the growing maturity of
defines the internal mechanism (i.e., a backend)
the tool, thanks to the some of the key players in
of the simulation objects, the OTcl sets up
this field. Among these are the University of
simulation by assembling and configuring the
California and Cornell University who developed
objects and scheduling discrete events (i.e., a
the REAL network simulator 1, the foundation of
frontend). The C++ and the OTcl are linked
NS is on. Since 1995 the Defense Advanced
together using TclCL. Mapped to a C++ object,
Research Projects Agency (DARPA) has been
variables in the OTcl domains are sometimes
supporting the development of NS through the
referred to as handles. Conceptually, a handle
Virtual InterNetwork Testbed (VINT) project.
(e.g., n as a Node handle) is just a string in the
Currently, the National Science Foundation
OTcl domain, and does not contain any
(NSF) has joined the ride in development. In
functionality. Instead, the functionality (e.g.,
addition to all these, a group of researchers and
receiving a packet) is defined in the mapped C++
nature.
Several
revolutions
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object (e.g., of class Connector). In the OTcl
extract a relevant subset of text-based data and
domain, a handle acts as a frontend which
transform it to a more conceivable presentation.
interacts with users and other OTcl objects. It may define its own procedures and variables to
3. DISCRETE-EVENT SIMULATION
facilitate the interaction. The member procedures and variables in the OTcl domain are called
NS2 is a discrete-event simulator, where
instance procedures (instprocs) and instance
actions are associated with events rather than
variables (instvars), respectively. It is important
time. An event in a discrete-event simulator
to the readers are to learn about C++ and OTcl
consists of execution time, a set of actions, and
languages to get a better understanding of these
a reference to the next event (Fig 6.2). These
architecture.
events connect to each other and form a chain of events on the simulation timeline. Unlike a timedriven simulator, in an event-driven simulator, the time between a pair of events does not need to be constant. When the simulation starts, events in the chain are executed from left to right (i.e. chronologically). The next section, discusses the simulation concept of NS2.
Fig 6.1: Basic architecture of NS. NS2 provides a large number of built-in C++ objects. It is advisable to use these C++ objects to set up a simulation using a Tcl simulation script. However, advance users may find these objects to be insufficient. They need to develop their own C++ objects, and use a OTcl configuration interface to put together these objects. After simulation, NS2 outputs either textbased or animation-based simulation results. To interpret these results both graphically and interactively, tools such as NAM (Network
Fig 6.2: Discrete-Event Simulation. Fig 6.2 demonstrates a sample chain of events in a discrete-event simulation. It can be observed that event contains execution time and a reference to the next event. In this figure, Event1 creates and inserts Event5 after Event2 (the execution time of Event 5 is at 3.7 second).
AniMator) and XGraph are used. To analyze a particular behavior of the network, users can
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i. NS2 Simulation Concept NS2 simulation consists of two major
4. NS2 COMPONENTS
phases.
A network object is one of the main NS2
Phase I: Network Configuration Phase
components, which is responsible for packet
In this phase, NS2 constructs a network
forwarding. NS2 implements network objects
and sets up an initial chain of events. The initial
using the polymorphism concept in Object-
chain of events consists of events which are
Oriented Programming (OOP). Polymorphism
scheduled to occur at certain times (e.g., start
allows network objects to take different actions
FTP (File Transfer Protocol) traffic at 1 second.).
ways under different contexts. For example, a
These events are called at-events. This phase
Connector
corresponds to every line in a Tcl simulation
received packet to the next network object, while
script before executing instproc run{} of the
a Queue1 object enques the received packets
Simulator object.
and forwards only the head of the line packet.
Phase II: Simulation Phase invokes
instproc
immediately
passes
the
Based on the functionality, NS2 modules
This part corresponds to a single line, which
object
Simulator::run
{}.
Ironically, this single line contributes to most of
(or objects) can be classified into following four types: ✓ Network
objects
are
responsible
for
the simulation (e.g., 99%). In this part, NS2
sending, receiving, creating, and destroying
moves along the chain of events and executes
packet-related objects. Since these objects
each event chronologically. Here, the instproc
are those derived from class NsObject,
Simulator::run{}
hereforth, they will be referred to as
starts
the
simulation
by
dispatching the first event in the chain of events.
NsObjects.
In NS2, “dispatching an event” or “firing an event”
✓ Packet-related objects are various types of
means “taking actions corresponding to that
packets which are passed around a network.
event”. An action, for example, refers to starting
✓ Simulation-related
objects
control
FTP traffic or creating another event and
simulation timing, and supervise the entire
inserting the created event into the chain of
simulation. Some examples of simulation-
events. In Fig 6.2, at 0.9 s, Event1 creates
related objects are events, handlers, the
Event5, which will be dispatched at 3.7 s, and
Scheduler, and the Simulator.
inserts Event5 after Event2. After dispatching an
✓ Helper objects do not explicitly participate in
event, NS2 moves down the chain and
packet forwarding. However, they implicitly
dispatches the next event. This process repeats
help to complete the simulation. For example,
until the last event corresponding to instproc
a routing module calculates routes from a
halt{} of OTcl class Simulator is dispatched,
source to a destination, while network
signifying the end of simulation.
address identifies each of the network objects.
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✓ Spectrum manager module: It implements the cognitive cycle for each CR user, as 5. SIMULATION OF CRAHNS USING NETWORK SIMULATOR 2
described in Section 6.6.3. It is composed of three main blocks: the spectrum sensing block, the spectrum decision block and the spectrum mobility block. The spectrum sensing block is responsible for detecting the activity of PUs on the current channel. To this aim, the spectrum sensing block interacts with the PU activity module. In the case of PU detection, the spectrum decision block chooses the policy to be adopted, i.e.
Fig. 6.4. NS2-CRAHN architecture
whether to switch to a new channel or to stay It has been extended the NS-2 simulator
on the current channel. If a channel switch is
with various blocks to be able to evaluate the
required, the spectrum decision block can
performance
for
choose the next available channel to be used
CRAHNs. Fig. 6.4 shows the architectural model
for CR user operation, and the spectrum
of the NS-2 CRAHN simulator. Compared to the
mobility block manages the spectrum handoff
traditional NS-2 architecture, it have added the
process.
following features, implemented as extendible
✓ Multi-radio
of
various
approaches
multi-channel
link
layer
stand-alone C++ modules:
module: It implements the multi-radio multi-
✓ PU Activity module: It describes the
channel environment for each CR user.
characteristics of active PUs in the current
Section 6.6.4 provides the details of the link-
scenario,
layer coordination, among the available radio
including
operating
channel,
physical location, and transmitting range. It
interfaces.
Each
also contains the description of PU activity in
spectrum
sharing
each spectrum band, as a sequence of ON
channel access in wireless networks. Current
and OFF periods over simulation time. All the
implementation is based on the CSMA/CA
information about PUs are contained in a PU-
MAC scheme. The spectrum sharing block
log file, which is generated offline. The format
interacts with the PU activity module to model
of PU-log file is described in Section 6.6.1.
the interference caused by PUs on current
✓ Spectrum data module: It describes the
radio block
implements for
the
distributed
ongoing transmissions of CR users.
PHY characteristics of each channel, such as
✓ Network layer module: Traditional routing
operating frequency, channel capacity and
protocols for wireless ad hoc networks can be
average bit error rate (BER). The format of
used at network layer, as well as customized
channel-log file is described in Section 6.6.2.
network protocols for CRAHNs.
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✓ Cross-layer repository module: It enables information sharing among protocols at
7. SPECTRUM MANAGER
different layers of the protocol stack. For example, it may contain information collected
The
spectrum
manager
module
at the PHY layer (e.g. current transmitting
implements the cognitive cycle for each CR user,
power), MAC layer (e.g. current size of the
by using the spectrum sensing block, the
backoff window) and network layer (e.g.
spectrum decision block and the spectrum
current neighbors’ list).
mobility block. It
emphasize
here
that
additional
Following sections provide a detailed description
spectrum policies and spectrum allocation
of the PU-log (Section 6.6.1) and channel-log file
algorithms can be easily integrated into the
(Section 6.6.2), of the spectrum manager
current module, by considering metrics provided
functionalities (Section 6.6.3) and of the rationale
by the MAC, physical or routing layer, or any
of the CR user model (Section 6.6.4).
combination among them. This also facilitates mechanisms that allow a CR user to temporarily
6. CHANNEL-LOG FILE FORMAT
route around the PU activity on a different frequency portion until the current PU activity
The channel-log file contains information about (i)
physical channel characteristics and
(ii)
channel quality.
stops. Spectrum Mobility Block
The spectrum data module is responsible
The spectrum mobility block is invoked
for loading the information from the file and
when the spectrum decision block decides that
making them available to the other modules. For
the CR user must vacate the current channel. It
each CR channel, the log file contains an entry
receives from the spectrum decision block the
with this format
new channel to switch to (e.g. next_channel).
(𝑖𝑑, 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦, 𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ, 𝑛𝑜𝑖𝑠𝑒) − − − (3)
The delay induced by the channel switch is
where 𝑖𝑑 is the identifier of the channel (a
modeled by using a timer. A CR user is not
number between 1 and N), frequency is the
allowed to utilize the radio interface for
channel central frequency, bandwidth is the raw
communication during the handoff operation.
bandwidth of the channel (e.g. 11 Mb/s), and
When the handoff process is completed, the
noise is the average value of the noise on that
spectrum sensing block is invoked to detect the
channel. By using bandwidth and noise, and by
PU activity on next_channel. If next_channel is
knowing the power received at a given location,
found free of PU activities, then a spectrum
it is possible to model the average BER (Bit Error
handoff notification is sent to the upper layer, and
Rate) experienced by the receiver node, for the
protocol reconfiguration is performed at the
QPSK modulation.
network layer.
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channel switching to next_channel, a broadcast 8. CR USER MODEL
message is sent by node C to inform its
The CR user model is shown in Fig. 6.5.
neighborhood about the channel used by its fixed interface. Spectrum Sharing Each interface implements a spectrum sharing scheme, based on a carrier sensing multiple access with collision avoidance (CSMACA) MAC scheme, with acknowledgments (ACK) and frame retransmissions at the MAC layer. It extend the MAC scheme to take into account the
Fig. 6.5. The CR model implemented in the
interference caused by PUs on CR users.
NS2-CRAHN simulator. 9. MAIN COMPONENTS OF A SIMULATION
Link Layer Management Each CR user at initialization designates one interface as its fixed interface, and the
Interpreted Hierarchy
second interface as the switchable interface.
Created by various instprocs, the main OTcl
Moreover, each CR user periodically informs its
simulation components are as follows:
neighbors about the channel used by its fixed
✓ The Scheduler (scheduler_ created by
interface, by broadcasting an HELLO message
instproc Simulator::init)maintains the chain of
on all the available channels. When a CR user
events
(e.g. node A) needs to communicate with a
chronologically.
and
executes
the
events
neighbor node (e.g. node B), it tunes its switching
✓ The null agent (nullAgent_ created by
interface to the channel used by the fixed
instproc Simulator::init) provides the common
interface of node B, and starts transmitting.
packet dropping point.5
Each CR user performs a sensing cycle
✓ Node reference (Node_ created by instproc
on the fixed radio interface, by periodically
Simulator::node) is an associative array
switching between two states: a sensing state
whose elements are the created nodes and
(for a ts time interval) and operational state (for a
indices are node IDs.
To time interval). To this aim, each CR user (say
✓ Link reference (link_ created by instprocs
node C) is associated with a sensing timer and
simplex-link{...}
an operational timer. When the sensing timer
associative array. Associated with an index
expires, C stops sending/receiving data on the
with format “sid:did”, each element of link_ is
current channel and performs sensing operations
the created uni-directional link which carries
to detect the presence of a PU. In the case of
packet from node “sid” to node “did”.
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or
duplexlink{...})
is
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a. SETTINGS
AND
PERFORMANCE
METRICS (Need to be corected at last)
1. Random Walk Mobility Model: A simple mobility
The default simulation settings are as
model
based
on
random
directions and speeds.
follows: We simulate an area of 4,000 ×
2. Random Waypoint Mobility Model: A
3,000 𝑚2 with 6 PUs and 1,334 SUs. The
model based on random waypoints and
transmission range of PUs and SUs is 250 m. We
random speeds that includes pause times
run the simulation 8,000 times, and the PUs and
between changes in destination and
SUs are uniformly distributed at random with a
speed, which will be discussed in more
different random seed in each simulation run. We
detail in the following section.
fix 30 pairs of source and destination PUs with
3. Random Direction Mobility Model: A
random relative locations in each simulation run.
model that forces mobile nodes to travel
SACBRP is used only when the source and
to the edge of the simulation area before
destination SUs are both outside of any PU
changing direction and speed.
region; otherwise, TIGHT is used to send packets
4. Boundless Simulation Area Mobility
over the secondary channel only. Each source
Model: A model that converts a 2D
SU sends one packet of 512 bytes to its
rectangular simulation area into a torus-
destination SU in each simulation run. Therefore,
shaped simulation area.
totally 8,000 packets are sent between each
5. Gauss-Markov Mobility Model: A model
source destination SU pair, and each data point
that uses one tuning parameter to vary
in subsequent figures represents the average for
the degree of randomness in the mobility
240 thousand packets (unless stated otherwise).
pattern. 6. Probabilistic Version of the Random
b. MOBILITY MODEL An important factor in mobile ad-hoc
Walk Mobility Model: A model that
networks is the movement of nodes, which is
utilizes a set of probabilities to determine
characterized by speed, direction and rate of
the next position of a mobile node.
change. Mobility in the “physical world” is
7. City
Section
Mobility
Model:
A
unpredictable, often unrepeatable, and it has a
simulation area that represents streets
dramatic effect on the protocols developed to
within a city.
support node movement. Therefore, different
To thoroughly and systematically study a
“synthetic” types of mobility models have been
new Ad-hoc Network protocol, it is important to
proposed to simulate new protocols. The term
simulate this protocol and evaluate its protocol
‘Synthetic’ means to realistically represent node
performance. Protocol simulation has several
movement without using network traces.
key parameters, including mobility model and
Camp et al., 2002 discusses the following
communicating traffic pattern, among others.
seven different synthetic entity mobility models based on random directions and speeds:
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iii.
i.Random-Based Mobility Models
Transition Length and Duration
In random-based mobility models, the mobile nodes move randomly and freely without
The RWP model correlates the speed
restrictions. To be more specific, the destination,
and the direction change behavior. The time
speed and direction are all chosen randomly and
between two direction change events is no
independently of other nodes. This kind of model
longer an adjustable input parameter of the
has been used in many simulation studies.
model, later it depends on the speed of the nodes
ii. Random Waypoint Mobility Model
and the size and shape of the area. For a given
The random waypoint (RWP) mobility
area, a higher speed results in a higher
model has been widely used in mobile ad-hoc
frequency of direction changes. The speed
network simulations. This mobility model is a
behavior and the direction change behavior are
simple and straightforward stochastic model. In
investigated.
RWP, a mobile node moves on a finite
Consider
RWP
movement
in
a
continuous plane from its current position to a
rectangular area of size a × b and again derive
new
its
the distribution of the transition length L. Without
destination coordinates, its speed of movement,
loss of generality it is assumed that a ≥ b. The
and the amount of time it will pause before when
spatial distribution of the 2D waypoints P = (Px,
it reaches the destination. On reaching the
Py) is now given by the uniform distribution
location
by
randomly
choosing
destination, the node pauses for some time distributed according to some random variable and the process repeats itself.
1 𝑓𝑃𝑥 𝑃𝑦 (𝑥, 𝑦) = {𝑎𝑏 𝑓𝑜𝑟 0 ≤ 𝑥 ≤ 𝑎 𝑎𝑛𝑑 0 ≤ 𝑦 ≤ 𝑏 0 𝑒𝑙𝑠𝑒 − − − (1) The distance between two points P1 = (Px1, Py1) and P2 = (Px2, Py2) is 𝐿 = ‖𝑃2 − 𝑃1 ‖ = √|𝑃𝑥1 − 𝑃𝑥2 |2 + |𝑃𝑦1 − 𝑃𝑦2 |2 = √𝐿2𝑥 + 𝐿2𝑦 − − − (2) Note that the random variable 𝐿𝑥 = |𝑃𝑥1 − 𝑃𝑥2 | represents the random distance between two uniformly distributed coordinates Px1 and Px2 on a 1D line segment [0, a]. The same holds
Fig 6.6 Traveling pattern of mobile node
true for 𝐿𝑦 = |𝑃𝑦1 − 𝑃𝑦2 | if it is replaced a by b. In
using the Random Waypoint Mobility
addition, both random distances are independent
model
of each other, and therefore the joint pdf of Lx and Ly is given by
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𝑓𝐿𝑥, 𝐿𝑦 (𝑙𝑥 , 𝑙𝑦 ) = 𝑓𝐿 (𝑙𝑥 )𝑓𝐿 (𝑙𝑦 ) =
4 𝑎2 𝑏 2
With arcos h (x) = In (𝑥 + √𝑥 2 − 1). Figure 4.4 shows the curve for E{𝐿}/a over (b/a).
(−𝑙𝑥 + 𝑎) (−𝑙𝑦
For example, the expected length within a square
+ 𝑏)for 0 ≤ 𝑙𝑥 ≤ 𝑎 𝑎𝑛𝑑 0 ≤ 𝑙𝑦 ≤ 𝑏 𝑎𝑛𝑑 0, otherwise − − − (3) With this expression, it is possible to derive the cdf Pr (𝐿 ≤ 𝑙) by integration of 𝐿𝑥 ,𝐿𝑦 ( 𝑙𝑥, 𝑙𝑦 )
over
the
circular
area𝐷 =
√𝑙𝑥2 + 𝑙𝑦2 ≤ 𝑙 in the (lx-ly)- space, that is,
a size a x a is E{𝐿} = The second moment of L is given by E{𝐿2 } =
1 6
(𝑎2 + 𝑏 2 )
It should be noted that the moments for the ID case are obtained, that is, 𝑙𝑖𝑚𝑏→ ∞ 𝐸{𝐿} = 1 𝑎 3
𝑎𝑛𝑑 𝑙𝑖𝑚𝑏𝑏→0 𝐸{𝐿} =
1 2 𝑎 . 6
The distance pdf 𝑓𝐿 (𝑙) of a circular system
𝑑
area of radius a is derived as follows: i. P=p is the
Pr(𝐿 ≤ 𝑙) = ∬ 𝑓𝐿𝑥 𝑓𝑦 (𝑙𝑥 , 𝑙𝑦 ) − − − (4)
starting waypoint, ii. The conditional probability
𝐷
As in the case of 1D, it cannot compute
𝑃𝑟 (𝐿 ≤ 𝑙 ⁄𝑝 = 𝑝) is derived using basic geometric
this integral in a straightforward manner, namely,
equation on the intersection area of two circles in
by setting the right-hand side of Equation (3) in
polar coordinates, and iii. P(𝐿 ≤ 𝐿)is the
Equation (4), but must take into account that
integration of the pdf 𝑓𝐿 (𝑙) over all [possible
𝑓𝐿𝑥 , 𝐿𝑦 ( 𝑙𝑥 , 𝑙𝑦 ) = 0 𝑓𝑜𝑟 𝑙𝑥 > 𝑎 𝑜𝑟 𝑙𝑦 > 𝑎. Thus ,
starting waypoints in the area. The final result of
it is distinguished between three cases:
these operations provides the transition length L
Solving
these
integrals,
taking
the
of nodes moving according to the Rwp model on
derivative with respect to l, and performing some
a disk [8] of radius a: in accordance of the
trigonometric simplifications lead to the pdf of the
following [1]:
transition length L of nodes moving according to
iv.
the RWP model in a rectangular area of size a × b, a ≥ b:
The transition time is the time taken by a node to move from one waypoint to the next
𝑓𝐿 (𝑙) =
4𝑙 𝑓 (𝑙) − − − (6) 𝑎2 𝑏 2 0
waypoint. The above results are used on the transition length to calculate the stochastic
With For arbitrary a, the value of 𝑓𝐿 (𝑙) is obtained by 1 𝑎
𝑓𝐿 (𝑙) = 𝑓𝑙 (l). The expected value of L is 2
𝐸{𝐿2 } =
Transition Time
2
2
1 𝑏 + [ arccos ℎ 6 𝑎 2
+
𝑎 arccos ℎ 𝑏
variable and an outcome, are denoted by 𝜏. It is considered as follows: 𝑉𝑖 = 𝑣 = 𝑐𝑜𝑛𝑠𝑡 ∀𝑖 𝑎𝑛𝑑 𝑣 >
2
1 𝑎 𝑏 𝑎 𝑏 [ 2 + 2 + √𝑎2 +𝑏 2 (3 − 2 − 2 )] 15 𝑏 𝑎 𝑏 𝑎 2
properties of the transition time. The random
√𝑎2 +𝑏 2
0. It implies that the speed of a node is constant during the entire movement. In this situation, it has
𝑏
√𝑎2 +𝑏 2 𝑎
𝑇= ]−−
1 𝐿 − − − (12) 𝑣
Hence, the expected transition time is
− (8)
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𝐸{𝑇} = 1 𝐸{𝐿} − 𝑣
− − −(18)
− − (13)
𝐼𝑛 (𝑣𝑚𝑎𝑥/𝑣𝑚𝑖𝑛 ) 𝐸{𝐿} − − − (19) 𝑣𝑚𝑎𝑥 − 𝑣𝑚𝑖𝑛
𝐸{𝑇} =
and its pdf can be computed by Note
𝑓𝑟(𝜏) = 𝑣𝑓𝐿 (𝑣𝜏) − − − (14)
that
𝑙𝑖𝑚𝑣𝑚𝑎𝑥→𝑣
𝑚𝑖𝑛
E
{L} 𝑣
𝐸𝐿 /𝑣𝑚𝑖𝑛
With 𝐸{𝐿} and 𝑓𝐿 taken from Equations (7)
corresponds to Equation (17) for constant speed
and (8) or Equations (10) and (11), respectively.
v = 𝑣𝑚𝑖𝑛 = 𝑐𝑜𝑛𝑠𝑡. Further, it should also be
The speed of the node from a random
noted that the expected time form 𝑣𝑚𝑖𝑛 = 0 is
distribution 𝑓𝑣 (𝜏) at each waypoint (and then
undefined. This is very reasonable because if a
stays constant during one transition) instead of
node chooses V=0, the movement transition will
considering the speed as constant. hence T will
take an infinite time. If the maximum speed can
be:
be expressed as a multiple of the minimum 𝐿
𝑇 = 𝑉 − − − (15) In this case, the random variable T is
speed, that is, 𝑣𝑚𝑎𝑥 = 𝑘𝑣𝑚𝑖𝑛 , with k > 1, it is obtained that 𝐸{𝑇} =
formed as a function 𝑔(𝐿, 𝑉) = 𝐿/𝑉 of two random variables L and V. In general, the
− − (20)
expected value of a variable
vi. Time Duration
𝑔(𝐿, 𝑉) can be
expressed in terms of the joint pdf 𝑓𝐿𝑉 (l, v ) as
𝐼𝑛 𝑘 𝐸 {𝐿} − 𝑘−𝑙 𝑣𝑚𝑖𝑛
A node moves from one waypoint to another and then pauses from a certain time
[15] ∞
∞
𝐸{𝑔(𝐿, 𝑉)} = ∫ ∫ 𝑔 (𝑙, 𝑣)𝑓𝐿𝑉 (𝑙, 𝑣)𝑑𝑙𝑑𝑣 − −
before changing direction in RWP model. The
−∞ −∞
total T of an RWP period is then composed of a
− (16)
movement transition time T and pause time 𝑇𝑝 .
In this case, L and V are independent, and
Now, it is possible to apply the above results for
thus their joint pdf is 𝑓𝐿𝑉 (𝑙, 𝑣) = 𝑓𝐿 (𝑙)𝑓𝑉 (𝑣). The
extending analysis for this case where a node
expected value can then be simplified as follows:
rests a certain pause time in each way point as
𝑣𝑚𝑎𝑥
𝐸{𝑇} = 𝐸{𝑇} ∫ 𝑣𝑚𝑖𝑛
v.
1 𝑓𝑣 (𝑣)𝑑𝑣 − − − (17) 𝑣
Transition Length and Duration
follows: 𝑇 = 𝑇 + 𝑇𝑝 − − − (21) This
Transition Length and duration can be explained more in detail by using uniform speed distribution.
linear
independent
random
combination
of
two
variables
yields
an
expected value 𝐸{𝑇΄} = 𝐸{𝑇} + 𝐸{𝑇𝑝 } − − − (22)
Uniform Speed Distribution If uniform speed distribution is employed within [𝑣𝑚𝑎𝑥 , 𝑣𝑚𝑖𝑛 ], the expected transition time 𝑣
is 𝑓𝑟 (𝜏) = ∫𝑣 𝑚𝑎𝑥 𝑣𝑓𝐿 (𝑣𝜏)𝑓𝑣 (𝑣)𝑑𝑣 For 0 ≤ 𝜏 ≤ 𝑚𝑖𝑛
and the pdf 𝜏΄
𝑓𝑟΄ (𝜏΄) = ∫ 𝑓𝑟 (𝜏)𝑓𝑟𝑝 (𝜏΄ − 𝜏)𝑑𝜏 𝑓𝑜𝑟 𝜏΄ ≥ 0 − − 0
− (23)
𝑙
𝜏𝑚𝑎𝑥 𝑤𝑖𝑡ℎ 𝑣𝑚𝑎𝑥 and fr(𝜏) = 0 𝑚𝑖𝑛
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.6 JUNE 2021
The value of E{T΄}represents the average
In Cartesian coordinates, the differential
time between two direction changes. Thus, the
area element dA is given by 𝑑𝐴 = 𝑑𝑥𝑑𝑦 and the
direction change frequency is given by 1/𝐸{𝑇} in
resulting
unit 1/s.
interpreted as the percentage of time that a given
vii.
Spatial Node Distribution
probability
can
Pr (𝑋 ∈ 𝐴΄)
be
mobile RWP node is located in the subarea 𝐴΄
This section investigates the spatial
during a long – run movement process with many
distribution of nodes in RWP model considering
transitions. Since the simulation is done with
a rectangle or circular system area A. In earlier
many mobile RWP nodes (n>1), it can also be
investigation, the distance and time between two
considered as the ensemble average. As a
consecutive waypoints was analyzed. However,
consequence, 𝐸{𝑛΄} = 𝑛𝑃𝑟 (node in A’) denotes
these waypoints, (which represent the starting
the expected number of nodes located in 𝐴΄ at an
and ending points of a node’s movement period),
arbitrarily chosen time instant.
are uniformly distributed per definition.
In
viii. Stochastic
practical it is studied only as the single node
Waypoint Model
Properties
of
Random
because all nodes move independently. The
Random Waypoint model as a discrete
random variable considered can be represented
time stochastic process was described in
as 𝑋 = 𝑓(𝑋, 𝑌) that denotes the Cartesian
Bettstetter et al.,2004. The transition length 𝐿𝑖
location of a mobile node in A at an arbitrary time
is defined as the distance covered by node j that
instant t. A particular outcome of this variable is
moves from one waypoint to another during the
denoted by x. The spatial distribution of a node in
ith epoch.
terms of the probability density function is
(𝑗)
If the simulation field is a circular area with
provided as stated below:
radius a. The probability density function of
𝑓𝑥 (𝑥) = 𝑓𝑥𝑦 (𝑥,𝑦)
transition length L is
= 𝑙𝑖𝑚𝛿→0 Pr ((𝑥 −
𝛿 <𝑋 2
8𝑙 1 1 1 2 −1 √ − 1 − ( ) ] [𝑐𝑜𝑠 2𝜋𝑎2 2𝑎 2𝑎 2𝑎
𝑓𝐿 (𝑙) =
𝛿 𝛿 𝛿 ≤ 𝑥 + ) ∧ (𝑦 − < 𝑌 ≤ 𝑦 + )) 2 2 2
− − − (26) Correspondingly, the expected value of transition
/𝛿 2 − − −(24) Usually, a conversion to polar coordinates 𝑅√𝑋 2 + 𝑌 2 and ∅ = arctan (Y/X) yields the joint distribution 𝑓𝑥(𝑥) over this subarea, that is,
length L is 2𝑎
𝐸[𝐿] = ∫ 𝑙𝑓𝐿 (𝑙)𝑑𝑙 = 0.905𝑎 − − − (27) 0
and the variance of transition length L is 2𝑎
𝐸[𝐿2 ] = ∫ 𝑙 2 𝑓𝐿 (𝑙)𝑑𝑙 = 𝑎2 − − − (28)
Pr(𝑛𝑜𝑑𝑒 𝑖𝑛 𝐴΄) = 𝑃 (𝑋 ∈ 𝐴΄)
0
1
= ∫ ∫ 𝑓𝑥𝑦 (𝑥, 𝑦)𝑑𝐴 − − − (25) 𝐴΄
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Bettstetter et al.,2004 took a further step to derive the probability distribution of transition time as follow 𝑉𝑚𝑎𝑥
𝑓𝑇 (𝑡) = ∫
𝑣𝑓𝐿 (𝑣𝑡)𝑓𝑣 (𝑣)𝑑𝑣 − − − (29)
𝑉𝑚𝑖𝑛
where 𝑓𝑣 (𝑣) is the probability distribution function of movement velocity v and 𝑓𝐿 (𝑙)is the probability distribution function of transition length. 10. CONCLUSION This work emphasized the documentation about NS2 simulator along with mobility model and performance
metrics,
work
performance
comparison
of
contains the
the
proposed
protocols and existing protocol.
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