Performance Evaluation of Mobility Models over UDP Traffic Pattern for MANET Using NS-2

Abstract

The current study presents the simulative study and evaluation of MANET mobility models over UDP traffic pattern to determine the effects of this traffic pattern on mobility models in MANET which is implemented in NS-2.35 according to various performance metri (Throughput, AED (Average End-2-end Delay), drop packets, NRL (Normalize Routing Load) and PDF (Packet Delivery Fraction)) with various parameters such as different velocities, different environment areas, different number of nodes, different traffic rates, different traffic sources, different pause times and different simulation times . A routing protocol.…was exploited AODV(Adhoc On demand Distance Vector) and RWP (Random Waypoint), GMM (Gauss Markov Model), RPGM (Reference Point Group Model) and MGM (Manhattan Grid Model) mobility models above CBR traffic sources. The results of Reference Point Group Model simulation illuminate that routing protocol performance is best with RPG mobility model than other models

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DOI: http://dx.doi.org/10.21123/bsj.2021.18.1.0175 
Performance Evaluation of Mobility Models over UDP Traffic Pattern for
MANET Using NS-2
Alaa Taima Albu-Salih1* Gesoon Al – Abbas2
1 Ministry of Education, General Directorate for Education in Al-Qadisiyah, Iraq
2 Department of Computer, College of Veterinary Medicine, Al- Muthanna University , Al- Muthanna, Iraq.
*Corresponding author: alaa_alhasany1983@yahoo.com, ghusoonjawad@mu.edu.iq
*ORCID ID: https://orcid.org/0000-0001-5006-5898 *, https://orcid.org/0000-0001-7803-6896
Received 30/3/2019, Accepted 26//2020, Published Online First 6//2020, Published 1//202
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract:
The current study presents the simulative study and evaluation of MANET mobility models over UDP
traffic pattern to determine the effects of this traffic pattern on mobility models in MANET which is
implemented in NS-2.35 according to various performance metri (Throughput, AED (Average End-2-end
Delay), drop packets, NRL (Normalize Routing Load) and PDF (Packet Delivery Fraction)) with various
parameters such as different velocities, different environment areas, different number of nodes, different
traffic rates, different traffic sources, different pause times and different simulation times . A routing
protocol.…was exploited AODV(Adhoc On demand Distance Vector) and RWP (Random Waypoint), GMM
(Gauss Markov Model), RPGM (Reference Point Group Model) and MGM (Manhattan Grid Model)
mobility models above CBR traffic sources. The results of Reference Point Group Model simulation
illuminate that routing protocol performance is best with RPG mobility model than other models.
Key word: Bonn Motion, MANET, Mobility Models, Performance Evaluation, Traffic Pattern.
Introduction
The Mobile Ad-hoc Network (MANET) is a
gathering of lymph node, which has the likelihood
to interface on a radio communication sensitive and
green goods a dynamic arrangement with radio
connection and with no associate infrastructure.
The network can dynamically alter with sentence,
some client can leave, and other nodes can junction
to the network, (1).
Most researches of MANET depends on
simulation techniques using the RWP (Random
Waypoint model), which is one of the default cases
in the NS-2 (Network Simulator-2). In the recent
years, various mobility models have been
suggested, (2).
It is so significant to evaluate the available
routing communications protocols functioning in
various mobility examples before choosing the
most appropriate protocol for a specific modelling.
Most MANET researches through routing
communications protocol selected the RWP
mobility model for simulations. Nevertheless,
studies on RWP model and influence on the
performance routing confirm that the
psychoanalysis of the protocol performance using
just RWP model is not sufficient; a given routing
protocol may not deliver good performance with
other mobility models, (3).
The current study investigates the mobility
models performance based on both CBR based
traffic pattern for various parameters such as
different velocities, different stop times, different
simulation environment area, different traffic rates,
different no. of nodes, different traffic sources and
different simulation times. The aim of this
evaluation is to know the best mobility model for
MANET based on five performance metrics: the
throughput, PDF (packet delivery fraction), NRL
(normalizes routing load), packet loss and AED
(average end-2-end delay). To the best of the
researcher’s knowledge, this the first evaluation of
mobility models’ performance that includes the
above seven parameters above UDP traffic pattern.
The remainder of the study is organized as
follows: The second section presents the related
work. The third section provides an overview of
routing protocols, mobility and traffic pattern used
in this study. Section 4 describes the Network
Simulator NS-2 and Bonn Motion are described in
section two. The performance metrics are included
in the fifth section. In Section 6, the steps of the

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proposed strategy are presented. The environment
of simulation is defined in seventh section and then
the simulation results are presented in section 8.
The last section includes conclusion of the current
study.
Background
R. Manoharan and E. Iavarasan, 2010 studied
three most widely used mobility patterns such as
RWP(Random Way Point), PPGM (Reference
Point Group) and MG(Manhattan Grid) mobility
that as well as to the weaknesses and strengths of
the multicast routing protocols, the mobility
models does also have effect on the performance of
the routing protocols. AODV protocol and
Adaptive Demand driven routing protocol have
been selected and executed in NS-2. They observed
that the mobility models do also have effect on the
performance of the routing protocols, (4).
A. Garg, et. al. , 2017 have observed the impact
of CMM (Column Mobility Model), RPGM
(Reference Point Group Mobility) and RWP
(Random Waypoint Model) mobility models on the
functioning of Cluster Based Multicast Tree
(CBMT) approach with DSDV routing protocol
varies widely across different no. of nodes and
node mobility speed in terms of QoS metrics as
average end2end delay, energy consumption,
packet delivery ratio and overhead for MANETs.
They observed that the movement of nodes is
characterized based on mobility velocity. They
found that the RWP has better results in suitable
conditions than the other two models in such
MANET environment, (5).
Prajakta M. Dhamanskar and et al. at 2012
presented the on demand routing protocols
performance such as AODV, TORA and DSR for
mobile nodes following four mobility patterns such
as RWP, RW (Random Walk), MG (Manhattan
Grid) and RPGM (Reference Point Group) mobility
models. They indicated that from the results of the
simulation, the conclusion is that the Reference
Point Group mobility model performance is the
best and the MG performance is the worst as
compared to other mobility models for all the three
protocols. PDR of AODV and TORA is better than
that of DSR but PDR of TORA is the best. Delay is
average in TORA and NRL is the worse in DSR,
(6).
Routing protocols, Mobility and Traffic
Pattern:
MANET Routing protocols:
Numbers of different routing protocols for
MANET were developed and used. Protocols were
classified as proactive and reactive protocols, (7).
This work focuses on applying and using the
AODV as a proactive protocol. AODV Protocol
stands for Ad hoc On Demand Distance Vector
Routing which preserves the table of routing at
each single node. It is proactive communications
protocol & contains three inputs in the routing table
for a name and address, a next hop node, a
sequence number and a hop count. All packets sent
to the target are directed to the next hop leaf node.
The successiveness number indicates the freshness
of a route. The hop count represents the present
distance to the target, (8).
Mobility Models (MMs):
An MM must be tried to imitate the movements
of actual nodes. MMs depend on setting out
different parameter related to the possible
apparent movement of node. Basic factors are the
starting position of leaf node, their movement
direction, the range of a function of velocity, and
the changes of speed over time. MMs can be
categorized into group and entity mobility models.
Entity mobility models (EMM) support situations
when mobile nodes move completely freely from
each other, while in GMM (group mobility models)
nodes are dependent on some predefined leader
node or on each other, (9). This paper, refer to the
used of the following Mobility models:
● RWP (Random Waypoint Mobility Model):
In RWP model, every node of the system chooses
a random destination and travels to it with certain
picked random speed. When a node achieves the
goal, the node stays for a length characterized by
the stop time factor. Next, node chooses a random
goal and rehashes the entire procedure till the point
when the time of simulation is finished, (10).
● RPGM (Reference Point Group Mobility
Model):
In RGGM, nodes are separated into groups. Each
single group has a pioneer that decides the all
nodes movements in the group. At every moment,
velocity group member is calculated in light of
speed and heading of pioneer node right then and
there. This model denotes the warriors movement
in a battle, or voyagers following traveler
leaders,(11).
● GMM (Gauss Markov Mobility Model):
In GMM, at first every node is allocated a present
velocity (direction and speed) at each settled time
interval. The motility of node happens by
refreshing the velocity of every node. Due to
temporal dependency, the estimation of velocity at
the specific time is ascertained based on the
estimation of past velocity,(12).
● MGM (Manhattan Mobility Model):
` In MGM, movement pattern of node were
characterized by outline made out of various

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vertical and horizontal roads. Node allows mobility
along the grid of horizontal and vertical grid on the
map. In light of temporal dependency, speed of a
node at a specific time is subject to the speed of its
past time, (13).
In the created scenarios of mobility model of MMs
utilizing Bonn Motion 2.0 (BM-2.0), so they can be
fused into TCL scripts. BM is java based
instrument for producing mobility scenarios for a
few mobility models, created by University of
Bonn.
Traffic Type
Traffic sources define how the data is conveyed
from source to target. Two types of Traffic sources
can be used in MANET and can be used in this
paper a) UDP (CBR) and b) TCP Traffic patterns.
▪ User Datagram Protocol (UDP):
The characteristics of Constant Bit Rate (CBR)
traffic model are I) predictable: static size of
packet, static interval amongst packets, and static
stream span, ii) unidirectional: there will be no
affirmation from goal for affirming the information
transmission and iii), unreliable: since it has no
connection foundation stage, there is no
certification that the information is conveyed to the
goal (13).
▪ Transmission Control Protocol (TCP):
The characteristics of TCP (Transmission
Control Protocol) traffic pattern are I) reliable:
since connection is created before transmitting
information. II ) conformity: there will be flow
restraint of data to avoid overloading the goal and
congestion controller exists to shape the dealings
such that it conforms to the available network
capacity iii) bi-directional: every packet that has to
be transmitted by the target is acknowledged by the
destination, and. Today more than 90% of the IP
traffic is carried out through TCP traffic pattern,
(14).
In order to create a new traffic generation between
nodes, should first follow "ns-allinone-2.35/ns-
2.35/indep-utils/cmu-scen-gen".
Simulation Tools
NS-2 (Network Simulator):
This simulation subject has been done using the
ns-2.35. The NS-2.35 is discrete issue simulation
software program for network. It simulates
receiving, sending, dropping and forwarding
packets events. The ns-allinone-2 .34 supports
simulation for adhoc wireless networks routing
protocols. NS-2 is with written in OTCL (Object
Tool Common Language) and C++ programming
language. ns-2.35 can be constructed on several
platforms, (15).
In the current study, an Ubuntu platform was
chosen. Linux presents a no. of computer
programming tools that were used in the required
simulation test procedure. To run and test ns-2.35,
the user must write the simulation script of OTCL
programming language. The parameters of
performance can be graphically pictured in
GRAPH program. Moreover, NS-2 also shows a
visual representation of the simulated network by
tracing cases and movements of nodes and
registering them in a data file called a Network
Animator (NAM file).
Bonn Motion(BM):
BM is the apparatus used to compute intermittent
links and the changes of connection in every one of
the models of mobility. BM 2.1a is the dependable
to make all movements data in OTCL with respect
to the mobility model. When the movement
models created , they display a brief period so
awareness of first seconds skirt is necessary
because they don't present the properties needed
from the mobility model needed, the places and
movements of the nodes of each simulation and
even the movement among them are randomly
chosen. BM is responsible for the random
properties of the place and movements of the nodes
and for the traffic ns-2.35 random factors utilized,
(16).
Performance Metrics
 Throughput
The throughput is defined as the total number of
packets received by the destination per time unit
that is delivered from one node through the
channel, (15).
 PDF (Packet Delivery Fraction)
PDF is the packets ratio delivered to the goals to
those created by the sources, (14). (PDF =
(Received packets number / Sent packets number)
* 100)
 AED (Average number end-2-end delay)
AED produced by queuing, buffering, latency,
retransmission and route discovery. The AED is
measured in milliseconds, (9). The AED is
computed by gathering the time which occupied by
all received data packets divided by number of
received packets.
 NRL (Normalize Routing Load)
NRL is the no. of control packets transferred per
data packet received at the target, (4). (NRL = All
routing control packets/ All received packets).
 Drop Packets :
It is defined as the no. of packets that have been
sent by the sources but have not been received at

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the destinations, (17): (Packets loss = Sent packets
– Received packets)
Methodology for Performance Evaluation
The following flowchart shown in Fig. 1 is to
evaluate the effect of the four mobility models on
the performance of 5 metrics (Throughput, Packet
Delivery Fraction (PDF), Average End-to-end
Delay (AED),Normalize Routing Load (NRL) and
packets loss) for an AODV routing protocol in
MANET with various parameters such as different
number of nodes, different traffic sources, different
speeds, different pause times, different simulation
times, different environment area and different
traffic rates.
Figure 1. A performance evaluation process
Simulation Environment
To run simulation with NS2.35, the OTCL
simulation script must be written. The performance
parameters are graphically pictured in X graph
code. Table 1, represents the required Hardware
(HW) and the Operating System (OS)
configurations while Table 2, presents the
suggested MANET’s simulation environment
implemented in this paper.
Table 1. Hardware and Operating System
configuration
Processor
Core i5 , 2.4 GHz
RAM
4 GB
Hard Disk
700 GB
OS
Linux, Ubuntu 14.04
Table 2. simulation environment
Network Simulator
The Network
simulator
NS-2.35
NAM
1.13
MAC Type
802.11
Radio Wave
Propagation
Two Ray Ground
Antenna
Omni Antenna
Traffic and Mobility
Data Traffic Type
CBR
Data Payload
512 bytes
Interface Queue Type
DropTail / PriQueue
Mobility Models
RWP , RPGM , GMM and
MGM
Routing Protocols
Routing Protocols
AODV
Simulation Results
In this section, seven scenarios were suggested
and implemented to evaluate and analyze the four
mobility models performance for MANET over
CBR traffic pattern, these parameters determine the
effect of mobility models on the MANET routing
protocols performance over this traffic pattern,
these parameters will investigate as displayed in
Table 3.
The simulation was carried out 10 times for each
mobility model, the sum of times is 40 for four
mobility models, and the total number of times is
200 for all mobility models under five parameters.
The performance metrics used for rating and
evaluation are packet delivery fraction (PDF),
throughput, no. of packet drop, NRL and AED. The
parameters used in this paper are varying number
of nodes, varying velocities, varying pause times,
varying simulation areas and varying traffic rates.
The results are shown in the following Fgures:
Figure 2 a-e, shows the performance metrics of
AODV over four mobility models (RWP, RPGM,
GMM, and MGM) under CBR traffic type for
parameter 1. In Fig. 2a, the throughput of AODV is
more significant with RPGM and RWP and the
throughput is less significant with GMM and
MGM. Fig. 2.b, shows the PDF of AODV is best in
RPGM and in RWP is somewhat best. PDF in
GMM and MGM is worst. (Fig. 2c) displays the
number of AODV drop packets in GMM and

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MGM is higher than RPGM and RWP, the packets
loss are increased when the no. of nodes decreased.
In Fig.(2.d), the NRL of this protocol is decreased
when the no. of nodes increased. The NRL in
RPGM is low because the leader of group decides
the speed of the members of group, the NRL in
MGM is high. Figure 2e, shows the AED is
decreased when the no. of nodes increased. AED in
RPGM is least and in GMM and MGM is highest.
Figure 3a-e shows the performance metrics of
AODV over four mobility models (RWP, RPGM,
GMM, and MGM) under CBR traffic type for
parameter 2. In Fig. 3a, the throughput of AODV
were decreased when the node speed was
increased. RPGM and RWP have high throughput
and PDF while MGM and GMM have low of it.
Figure 3b displays the PDF of AODV protocol
were decreased when the node speed were
increased. RPGM and RWP have high throughput
and PDF while MGM and GMM have low of it.
Figure 3c, shows the no. of packets loss in GMM
and MGM is highest while in RPGM and RWP is
lowest, the loss packet is increased when the node
speed increased. In Fig. 3d, the NRL of this
protocol is increases with high speed for all
mobility models. RPGM has low NRL than other
mobility models while MGM has high NRL. Figure
3e, shows The AED increased when the node speed
increased. AED in RPGM is lowest and in MGM
and GMM is highest because in RPGM, the group
leader determines the velocity of group members.
Table

3. General Parameters for All Simulation Parameters.

Scenario
Name
Parameter
Number
No. of
nodes
Node
Speed
Pause
Times
Area Size
Traffic
Rate
Traffic
Sources
Simulation
Times
Varying No.
of Nodes
1
25 , 50
75,100
20
15
1000*1000
4
5
75
Varying
Node Speeds
2
25
10 , 20
40 , 60
10
1000*1000
4
5
75
Varying
Pause Times
3
50
40
0 , 6
10 ,14
1000*1000
4
5
75
Varying Area
Sizes
4
60
20
12
500*500 ,
700*700
1000*1000
,1200*1200
4
5
75
Varying
Traffic Rates
5
75
15
10
1000*1000
4 , 8
12 , 16
5
75
Varying
Traffic
Sources
6
25
20
10
1000*1000
4
5 , 10
15 , 20
75
Varying
Simulation
Times
7
75
20
10
1000*1000
4
5
100,200,
300,400
Figure 2.[a-e]: The performance metrics of
AODV over four mobility models (RWP,
RPGM, GMM, and MGM) under CBR traffic
type for parameter 1.
Figure 3. [a-e]: The performance metrics of
AODV over four mobility models (RWP,
RPGM, GMM, and MGM) under CBR traffic
type for parameter 2
a)
Throughp
ut
b) PDF
c) Packet Loss
d) NRL
e) AED
a)
Throughput
b) PDF
c) Packet Loss
d) NRL
e) AED

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Figure 4a-e, shows the performance metrics of
AODV over four mobility models (RWP, RPGM,
GMM, and MGM) under CBR traffic type for
parameter 3. In Fig. 4a, the throughput of RPGM is
extremely better than all the other mobility models
and MGM and GMM have clearly worst results.
Fig. 4b, shows the AODV has best PDF with
RPGM mobility model. RWP is better next to
RPGM. PDF in MGM and GMM is very low when
compared to RWP and RPGM Models. Figure 4c,
shows that in MGM and GMM, the number of
packets loss increased when the value of pause
time increased. RPGM and RWP provide a lowest
no. of packet loss. In Fig. 4d, the normalized
routing load of AODV can be simply sorted in an
order from worst to best as follows: MGM, GMM,
RWP and RPGM. Figure 4e, shows the RPGM and
RWP exhibit the lowest delay and GMM and
MGM have highest delay.
Figure 4. [a-e]: The performance metrics of
AODV over four mobility models (RWP,
RPGM, GMM, and MGM) under CBR traffic
type for parameter 3.
Figure 5a-e, shows the performance
metrics of AODV over four mobility models
(RWP, RPGM, GMM, and MGM) under CBR
traffic type for parameter 4. In Fig. 5a, the
throughput of AODV became lower when the
network load is higher. This protocol is highest in
throughput with RPGM and lowest with MGM and
GMM. Figure 5b, shows the PDF of AODV
became lower when the network load is higher.
This protocol is highest in PDF with RPGM and
lowest with MGM and GMM. In Figure 5c, the no.
of packets loss in GMM and MGM is highest while
in RPGM and RWP is lowest, the loss packet is
increased when the node speed increased. Figure
5d, shows the NRL of this protocol is decreases
with large environment size for all mobility
models. RPGM has low NRL than other mobility
models while MGM has high NRL due to the
restriction of node movement in MGM. In Fig.5e,
the AED increased when the environment size is
increased because the no. of dropped packet was
increased. AED in RPGM is lowest and in MGM
and GMM is highest.
Figure 5. [a-e]: The performance metrics of
AODV over four mobility models (RWP,
RPGM, GMM, and MGM) under CBR traffic
type for parameter4.
Figure 6. [a-e]: The performance metrics of
AODV over four mobility models (RWP,
RPGM,GMM, and MGM) under CBR traffic
type for parameter5
Figure 6a-e, shows the performance metrics
of AODV over four mobility models (RWP,
RPGM, GMM, and MGM) under CBR traffic type
for parameter 5. In Fig. 6a, the throughput of
AODV became lower when the network load is
higher. This protocol is highest in throughput with
RPGM and lowest with MGM and GMM. Figure
6b, shows the PDF of AODV became lower when
a)
Throughput
b) PDF
c) Packet Loss
d) NRL
e) AED
a)
Throughput
b) PDF
c) Packet Loss
e) AED
d) NRL
a)
Throughput
b) PDF
c) Packet Loss
e) AED
d) NRL

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the network load is higher. This protocol is highest
in PDF with RPGM and lowest with MGM and
GMM. Figure 6c, shows the no. of packets loss in
GMM and MGM is highest while in RPGM and
RWP is lowest, the loss packet is increased when
the node speed increased. In Fig. 6d, the NRL. is
decreased when the traffic rate is increased. The
NRL in RPGM is low and in MGM is high. In Fig.
6e, the AED of AODV is increased when the
traffic rate is increased. This protocol with GMM
and MGM shows highest AED but with RPGM
gives lowest AED.
Figure 7. [a-e]: The performance metrics of
AODV over four mobility models (RWP,
RPGM, GMM, and MGM) under CBR traffic
type for parameter 6.
Figure7 a-e, shows the performance
metrics of AODV over four mobility models
(RWP, RPGM, GMM, and MGM) under CBR
traffic type for parameter 6. In Fig. 7a, the
throughput of AODV became lower when the
traffic source is higher. This protocol is highest in
throughput with RWP and lowest with MGM and
GMM. Figure 7b, shows the PDF of AODV
became lower due to the number of packet loss are
decreased. This protocol is highest in PDF with
RWP and lowest with MGM and GMM because in
RPGM, the group leader in each group determines
the group motion behavior and each member in
group deviates its speed and direction randomly
from that leader. Figure 7c, shows the no. of
packets loss in GMM and MGM is highest while in
RPGM and RWP is lowest, the loss packet is
increased when the traffic source increased. In Fig.
7d, the NRL in Figure. is increased when the traffic
source is increased. The NRL in RPGM is low and
in MGM is high. Figure 7e, shows the AED of
AODV is increased when the traffic source is
increased. This protocol with GMM and MGM
shows highest AED but with RPGM gives lowest
AED.
In Fig. 8a, throughput of RPGM is extremely
better than all the other mobility models and MGM
and GMM have clearly worst results. Fig. 8b,
shows the AODV has best PDF with RWP and
RPGM mobility models. RWP is better next to
RPGM. PDF in MGM and GMM is very low when
compared to RWP and RPGM Models. Figure 8c,
shows that in MGM and GMM, the no. of packets
loss increased when the simulation time increased,
RPGM and RWP provide a lowest no. of packet
loss. In Fig. 8d, the NRL of AODV protocol can be
simply sorted in an order from worst to best as
follows: MGM, GMM, RWP and RPGM. Figure
8e, shows the RPGM and RWP exhibit the lowest
delay and GMM and MGM have highest delay
because in MGM and GMM, the nodes can move
only in four direction with predefined probabilities
to change direction only at the intersection point.
Figure 8. [a-e]: The performance metrics of
AODV over four mobility models (RWP,
RPGM, GMM, and MGM) under CBR traffic
type for parameter7.
Conclusion:
This paper presented an evaluated and
analyzed the four mobility models performance
using NS-2.35 and Bonn Motion-2.1.a according to
several performance metrics with various
parameters over CBR traffic pattern. After this
evaluation, it has been found the RPGM is the best
mobility model suited for AODV routing protocol
when compared to other mobility model.
Although the RWP is widely used in MANETs,
but the results of simulation shows that it is not the
best among the mobility models in the case of CBR
traffic pattern. The AODV routing protocol
performance is the best with RPG mobility model
than with other models. The routing protocol has
poor performance when the mobility model is
GMM or MGM mobility models. In Future work,
would prolong this work to study the impact of
these mobility and traffic sources on the most
widely used MANETs routing protocols.
a)
Throughput
b) PDF
c) Packet Loss
d) NRL
e) AED
a)
Throughput
b) PDF
c) Packet Loss
d) NRL
e) AED

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Authors' declaration:
- Conflicts of Interest: None.
- We hereby confirm that all the Figures and
Tables in the manuscript are mine ours. Besides,
the Figures and images, which are not mine
ours, have been given the permission for re-
publication attached with the manuscript.
- Ethical Clearance: The project was approved by
the local ethical committee in Ministry of
Education.
References
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Open Access Baghdad Science Journal P-ISSN: 2078-8665
Published Online First: December 2020 E-ISSN: 2411-7986
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MANETNS-2.35
  (PDF)  (NRL)     (AED)  

  AODV (RWP)   (RPGM)(GMM) (MGM) CBR
RPGM.
 :BonnMotionMANET