simulation and results - shodhgangashodhganga.inflibnet.ac.in › bitstream › 10603 › 7955 ›...
Post on 28-Jun-2020
5 Views
Preview:
TRANSCRIPT
97
CHAPTER 6
SIMULATION AND RESULTS
The protocol proposed in the previous chapter was simulated on the QualNet-5.0
software after making the requisite changes through programming. The QualNet-5.0
provides the base protocols, mobility patterns and energy models etc. The software can
be customized to implement a desired protocol by making suitable modifications in the
different modules. To identify these modules and to implement the changes was a
uphill task but thanks to the technical support received from Eigen Technologies, New
Delhi we were able to simulate the protocol and collected the results.
The QualNet-5.0 provides a comprehensive set of tools with all the components for
custom network modeling and simulation projects. QualNet's unparalleled speed,
scalability, and fidelity make it easy for modelers to optimize existing networks
through quick model setup and in-depth analysis tools. Models in source form provide
developers with a solid library on which to build and experiment with new network
functionality. The end result is accurate prediction of network performance for a
diverse set of application requirements and uses. From wired LANs and WANs, to
cellular, satellite, WLANs and mobile ad hoc networks, QualNet's library is extensive.
Because of its efficient kernel, QualNet models large scale networks with heavy traffic
and mobility in reasonable simulation times.
We now start with our discussion on simulation setup.
6.1 SIMULATION SETUP
For implementing the proposed protocol, an environment had to be created with certain
fixed and variable parameters. These parameters were suitably chosen for carrying out
the simulation process and the proposed protocol was implemented to check the
outcome. A detailed list of parameters chosen is shown in Table 6.1.
98
Table 6.1
Set up Parameters
Examined Protocols AODV, AODV-n
Simulation Period 30-2000 sec
Simulation Area 1500 X 1500 sq. mt
Number of Nodes 50, 60
Traffic Type CBR (UDP)
Energy Model Mica- Motes
Communication Model IEEE 802.11
Battery Model Linear
Default Battery 1200mah
Data Rate 4096 bps
Pay load size 512 byte
Trust update interval 1 sec
Number of malicious nodes 0%-50% step 10%
Number of selfish nodes
0% to 100% step 10% for studying Network Lifetime
0% to 50% step 10% for other metrics
The snapshots of the simulation process have been shown in Fig 6.1(a) and Fig. 6.1 (b).
99
Fig 6.1(a) Snapshot of Simulation process (Nodes = 50)
Fig 6.1(b) Snapshot of Simulation process (Nodes = 60)
100
6.2 DESCRIPTION OF PARAMETERS CHOSEN
Examined Protocols: As described in the previous chapter, we chose the
standard AODV protocol from the QualNet-5.0 software and simulated it to
collect the data about various parameters. Thereafter, we simulated our own
protocol AODV-n and compared its performance with normal AODV.
Simulation period: Depending upon the requirement of the metric to be
studied, different simulation periods were chosen. The maximum simulation
period chosen was 2000 sec while studying the metric network lifetime. The
normally simulation period was 30 sec.
Simulation Area: For performing the simulation, a standard rectangular area of
the dimension 1500 X 1500 mt2 was taken.
Number of nodes: For performing the simulation two sets of readings were
taken one with 50 nodes and other with 60 nodes.
Traffic Type: Going by the convention the traffic type taken was Constant Bit
Rate (CBR).
Energy Model: To take into the account the energy dissipation due to the
various activities and the idle case scenario the energy model chosen was Mica-
Motes as suggested by Eigen technologies and as conveyed by the various
papers.
Communications Model: The IEEE standard 802.11 Distributed Coordination
Function (DCF) [131] was used as the MAC layer routing protocol. All Route
Request and Query packets were broadcasted using the un-slotted Carrier Sense
Multiple Access protocol with Collision Avoidance (CSMA/CA) wherein each
broadcasting node waits for a vacant channel by sensing the medium. If the
channel is vacant, it transmits. In case of a collision, the colliding stations wait
using the Ethernet binary exponential back off algorithm. To unicast packets,
the node first reserves the channel by transmitting a short Ready-to-Send (RTS)
101
frame. The intended recipient node, in response, sends a Clear-to-Send (CTS)
frame to the RTS sender. All nodes overhearing the RTS or CTS frames desist
from transmitting for the Network Allocation Vector (NAV) interval. Upon
receipt of the CTS, the packet is transmitted which is acknowledged by the
recipient.
Battery model: The power dissipation model of the battery was taken to be
linear to consider the uniform decay of power with time when the environment
is not changing.
The default battery, data rate and payload size were taken as per the standards
given in the literature and available on the QualNet-5.0 software.
Trust update interval: This parameter was of critical importance and had to be
optimised. A very short trust update interval will lead to very high overhead of
energy and bandwidth usage and a long trust update interval may not provide
the requisite information in time. Therefore an optimal time of 1 sec was chosen
keeping in similarity with hello interval as used by AODV.
Number of Malicious nodes: The malicious nodes strength was taken upto
50% of the total nodes strength.
Number of Selfish nodes: The selfish nodes strength was taken upto 50% of
the total nodes strength in most of the cases. However, while studying the
network lifetime metric it was the selfish node strength was raised to the level
of 100% to study the impact of such a situation.
6.3 ATTACK PATTERN TAKEN
Selfish Node Attack: In this attack, a node tries to utilize the network resources for its
own profit but is reluctant to spend its own for others since its residual battery power is
low/ very low. As the time passes, the nodes in an Ad-hoc network loose their battery
power and their chances of becoming selfish get increased.
102
Malicious Node Attack: In this attack, a malicious node dumps all the data / Control
packet which it is supposed to forward. It receives all the packets meant for it but at the
same time doesn’t forward the packets that are intended for others.
6.4 ASSUMPTIONS
The following assumptions were made for simulation purpose:
Each node declares its residual battery status correctly.
The malicious node work in individual manner and there is no such group.
The participating nodes are not in a position to modify the contents of control
packets.
6.5 METRICS USED
To evaluate the efficacy of the proposed protocol following metrics were used:
Successful Route Formation: Percentage of route successfully created to the
number of route requests generated by the source.
Average Hop Count: Average number of hops for all successful route
formation.
Throughput: How fast data can pass through a network. In our simulation
scenario it is the number of bits passing through the network in one second.
End-to-End delay: Time taken for a packet to travel from the CBR (Constant
Bit Rate) source to the destination.
Jitter: Occurs when in a transmission scenario different packets take different
amount of time in reaching from source to destination. Jitter can be measured by
using the standard deviation of packet delay. If a communication system has
large amount of jitter then the signal quality is very poor.
Packet delivery ratio (PDR): Ratio of number of data packets successfully
received at the destinations to the total number of data packets sent by various
sources.
Network Lifetime: Time at which first node of network gets dead.
103
Or
Time at which all the node of the network gets dead.
Probability of Reachability: Defined as every fraction of possible reachable
routes to the all possible routes between all different sources to all different
destinations.
6.6 RESULTS AND DISCUSSIONS
6.6.1 SUCCESSFUL ROUTE FORMATION
Fig. 6.2(a) and Fig. 6.2(b) show the results of successful route formation in the
presence of malicious nodes varying from 0 to 50% of the total node strength (60/50). It
was observed that percentage of successful route formation was almost same in case of
plain AODV irrespective of the malicious node concentration. There was a significant
drop in the successful route formation with the increase in malicious node
concentration in case of AODV-1, 2, 3. In the case of AODV-3, the successful route
formation reduces from 70% to 15% when the concentration of malicious nodes
increases from 0 to 50%. While in the case of AODV-2, the reduction was from 70% to
25% and in case of AODV-1 the reduction was found to be from 70% to 28%. The
results conveyed that the choice of a higher trust index threshold is going to have a
large impact on the percentage of successful route formation. Though there was a
decrease in the case of AODV-0, but this drop was small. This showed that though a
passage was offered to the malicious nodes yet their blacklisting had an impact on the
successful route formation leading to the non formation of certain routes.
In case of selfish node scenario, the successful route formation was close to 100% in
case of plain AODV irrespective of selfish node concentration as shown in Fig. 6.2(c)
and Fig. 6.2(d). A small drop was observed in case of AODV-0 with the increase in
selfish node concentration. Though there was a decrease in the percentage of route
formation in case of AODV-1, 2, 3 with the increase in selfish node concentration but
this decrease was much less than in case of malicious node scenario. The underlying
104
reason for this being the allowance to form the route for selfish nodes upto w number of
times (5 in our case) .
Successful Route Formation
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50
Malicious Node Percentage
Perc
en
tag
e R
ou
te F
orm
ati
on
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.2(a) Successful Route Formation with malicious nodes (Nodes = 60)
Successful Route Formation
0
20
40
60
80
100
120
0 10 20 30 40 50
Selfish Node Percentage
Perc
en
tag
e o
f S
uccessfu
l R
ou
tes
AODV
AODV- 0
AODV-1
AODV-2
AODV-3
Fig 6.2(b) Successful Route Formation with selfish nodes (Nodes =60)
105
Successful Route Formation
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50
Malicious Node Percentage
Perc
en
tag
e R
ou
te F
orm
ati
on
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.2(c) Successful Route Formation with malicious nodes (Nodes =50)
Successful Route Formation
0
20
40
60
80
100
120
0 10 20 30 40 50
Selfish Node Percentage
Perc
en
tag
e o
f S
uccessfu
l R
ou
tes
AODV
AODV- 0
AODV-1
AODV-2
AODV-3
Fig 6.2(d) Successful Route Formation with selfish nodes (Nodes = 50)
106
6.6.2 AVERAGE HOP COUNT
Fig. 6.3(a) and Fig 6.3(b) show the results for average hop count in case of malicious
node environment. It was observed that the average hop count showed no change for
plain AODV with the increase in malicious node concentration. There was marginal
increase in hop count in case of AODV-0 with the increase in malicious node
concentration. The trend in case of AODV-1, 2, 3 showed an increase initially but a
decrease later on as the malicious node concentration increased. A careful scrutiny of
the scenario showed that when the malicious node concentration was very high the
successful route formation was limited to immediate neighbors or their next neighbors
in most of the cases. Similar results were observed in case of selfish node scenario as
shown in Fig. 6.3(c) and Fig. 6.3(d).
Average Hop Count for Successful Routes
0
0.5
1
1.5
2
2.5
3
3.5
4
0 10 20 30 40 50
Malicious node Percentage
Avera
ge H
op
Co
un
t
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.3(a) Average Hop Count for malicious nodes (Nodes = 60)
107
Average Hop Count for Successful Routes
0
1
2
3
4
0 10 20 30 40 50
Selfish node Percentage
Avera
ge H
op
Co
un
tAODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.3(b) Average Hop Count for malicious nodes (Nodes = 60)
Average Hop Count for Successful Routes
0
1
2
3
4
0 10 20 30 40 50
Malicious node Percentage
Avera
ge H
op
Co
un
t
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.3(c) Average Hop Count for malicious nodes (Nodes = 50)
108
Average Hop Count for Successful Routes
0
1
2
3
4
0 10 20 30 40 50
Selfish node Percentage
Avera
ge H
op
Co
un
tAODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.3(d) Average Hop Count for selfish nodes (Nodes = 50)
6.6.3 THROUGHPUT
To measure the throughput the data was sent at the rate of 4096 bits/sec. The results for
the throughput are shown in Fig. 6.4(a) to Fig. 6.4(d). The plain AODV works well
when there is no malicious or selfish node. As the malicious node concentration
increases the throughput of plain AODV decreases very fast and reduces to 55% as the
malicious node concentration node reaches to 50%. However, the AODV-1, 2, 3
protocols manage to resist the impact of malicious nodes and the throughput drops to
the level of nearly 70%. Also it is worth noticing that when the malicious node
concentration is to the level of 30 to 40%, the AODV-1, 2, 3 protocols have the
throughput to the level of nearly 85 to 90%. In the selfish environment the throughput
of the AODV-1, 2, 3 protocols is nearly 95-100%. This is due to the fact that the nodes
are obligated to speak truth about their current power levels in these protocols. There is
a significant decrease in the performance of plain AODV with the increase in selfish
node concentration. The performance of AODV-0 was fluctuating in case of selfish and
malicious nodes environment.
109
Throughput
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 10 20 30 40 50
Malicious Node Percentage
Th
rou
gh
pu
t o
f R
eceiv
er
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.4(a) Average Throughput for malicious nodes (Nodes = 60)
Throughput
3300
3400
3500
3600
3700
3800
3900
4000
4100
4200
0 10 20 30 40 50
Selfish Node Percentage
Perc
en
tag
e T
hro
ug
hp
ut
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.4(b) Average Throughput for selfish nodes (Nodes= 60)
110
Throughput
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 10 20 30 40 50
Malicious Node Percentage
Th
rou
gh
pu
t o
f R
eceiv
er
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.4(c) Average Throughput for malicious nodes (Nodes= 50)
Throughput
3300
3400
3500
3600
3700
3800
3900
4000
4100
4200
0 10 20 30 40 50
Selfish Node Percentage
Perc
en
tag
e T
hro
ug
hp
ut
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.4(d) Average Throughput for selfish nodes (Nodes= 50)
6.6.4 END TO END DELAY
It indicates the time taken for a packet to travel from the CBR (Constant Bit Rate)
source to the destination. It represents the average data delay that an application
111
experiences while transmitting data. At low concentration level of malicious nodes, the
end to end delay is much lower in case of plain AODV and as the concentration of
malicious nodes increases the average delay is much lower in case of our protocol
AODV-n as shown in Fig. 6.5(a) to Fig. 6.5(d).
Average End to End Delay
0
0.01
0.02
0.03
0.04
0.05
0.06
0 10 20 30 40 50
Malicious Nodes
Dela
y in
seco
nd
s AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.5(a) Average End to End delay for malicious nodes (Nodes = 60)
Average End to End Delay
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0 10 20 30 40 50
Selfish Nodes
Dela
y in
seco
nd
s AODV
AODV- 0
AODV-1
AODV-2
AODV -3
Fig. 6.5(b) Average End to End delay for selfish nodes (Nodes = 60)
112
Average End to End Delay
0
0.01
0.02
0.03
0.04
0.05
0.06
0 10 20 30 40 50
Percentage of Malicious Nodes
Dela
y in
seco
nd
s AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.5(c) Average End to End delay for malicious nodes (Nodes = 50)
Average End to End Delay
0
0.01
0.02
0.03
0.04
0.05
0.06
0 10 20 30 40 50
Percentage of Selfish Nodes
Dela
y in
seco
nd
s AODV
AODV- 0
AODV-1
AODV-2
AODV -3
Fig. 6.5(d) Average End to End delay for selfish nodes (Nodes = 50)
The performance of plain AODV and AODV-n in the selfish environment was found to
be fluctuating with no consistent change in one direction, on analysis it was found that
as the selfish node concentration increases most of the routes formed were of low hop
count of the order of 1 or 2.
113
6.6.5 JITTER
In a communication scenario stream line flow of data packets is necessary where in all
the data packets follow their preceding packets with the same speed. In such a scenario
the output will be smooth without any turbulence. If such a situation doesn’t exist then
the output is jerky and the jerks can be felt in the video/audio output. Flow of data
packets will be streamline if each data packet takes equal time for traveling from source
to destination. If there is a variation between the traveling times of different packets
then it will cause a jitter. The jitter can be computed by measuring the average traveling
time of each packet on a particular path and applying the standard deviation on the
traveling time. Larger the standard deviation more prominent is the jitter effect [133-
135]. Fig. 6.6 (a) to Fig 6.6 (d) show the jitter encountered by the control/data packets
in different protocols.
Jitter
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0 10 20 30 40 50
Malicious Nodes
Jit
ter
AODV
AODV w ith Priority level =
0
AODV w ith Priority level =
1
AODV w ith Priority level =
2
AODV w ith Priority level =
3
Fig. 6.6(a) Average Jitter for malicious nodes (Nodes = 60)
114
Jitter
0
0.01
0.02
0.03
0.04
0.05
0.06
0 10 20 30 40 50
Selfish Nodes Percentage
Sta
nd
ard
devia
tio
n
AODV
AODV-0
AODV-1
AODV- 2
AODV-3
Fig. 6.6(b) Average Jitter for selfish nodes (Nodes = 60)
Jitter
0
0.005
0.01
0.015
0.02
0.025
0 10 20 30 40 50
Malicious Nodes Percentage
Sta
nd
ard
devia
tio
n
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.6(a) Average Jitter for malicious nodes (Nodes = 50)
115
Jitter
0
0.01
0.02
0.03
0.04
0.05
0.06
0 10 20 30 40 50
Selfish Nodes Percentage
Sta
nd
ard
devia
tio
n
AODV
AODV-0
AODV-1
AODV- 2
AODV-3
Fig. 6.6(d) Average Jitter for selfish nodes
(Nodes = 50)
6.6.6 PACKET DELIVERY RATIO (PDR)
It is the ratio of number of data packets successfully received at the destinations to the
total number of data packets sent by various sources. At low concentration level of
selfish and malicious nodes, the PDR of plain AODV is nearly same as that of AODV-
1, 2, 3 but as the concentration of selfish and malicious nodes increases the PDR for
plain AODV was found to be much lower in comparison to our protocol AODV-n as
shown in Fig. 6.7. On analysis it was found that as the concentration of selfish and
malicious nodes increases the nodes begin to drop data packets due to low battery
power or due to their rogue behavior. The performance of AODV-0 is the poorest of all
the protocol where time is taken for identification of malicious and selfish nodes but the
information remains unused. This type of the situation is bad in both the cases as the
time is wasted in identification and both malicious and selfish are inserted in the route.
116
Packet Delivery Ratio
0
20
40
60
80
100
120
0 10 20 30 40 50
Malicious Node Percentage
Perc
en
tag
e P
acket
Delivery
Rati
o
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.7(a) Average Packet Delivery Ratio for malicious nodes (Nodes = 60)
Packet Deliver Ratio (PDR)
0
20
40
60
80
100
120
0 10 20 30 40 50
Selfish Node Percentage
Perc
en
tag
e P
acket
Delivery
Rati
o
AODV
AODV- 0
AODV-1
AODV-2
AODV-3
Fig. 6.7(b) Average Packet Delivery Ratio for selfish nodes (Nodes = 60)
117
Packet Delivery Ratio
0
20
40
60
80
100
120
0 10 20 30 40 50
Malicious Node Percentage
Perc
en
tag
e P
acket
Delivery
Rati
o
AODV
AODV-0
AODV-1
AODV-2
AODV-3
Fig. 6.7(c) Average Packet Delivery Ratio for malicious nodes (Nodes = 50)
Packet Deliver Ratio (PDR)
0
20
40
60
80
100
120
0 10 20 30 40 50
Selfish Node Percentage
Perc
en
tag
e P
acket
Delivery
Rati
o
AODV
AODV- 0
AODV-1
AODV-2
AODV-3
Fig. 6.7(d) Average Packet Delivery Ratio for selfish nodes (Nodes = 50)
6.6.7 NETWORK LIFETIME
The result shows the impact of selfish node concentration on network lifetime as the
percentage of selfish nodes increases. Fig 6.8(a) shows the network lifetime on the
basis of getting down of first node. Fig. 6.8(b) show the network lifetime on the basis
118
of getting all nodes dead. Table 6.2 shows the simulation parameters used in the
scenario. The battery was taken to be 2 mah for the normal node and for the selfish
node was taken to be 0.012 mah.
Table 6.2
Set Up Parameters for Network Lifetime
Default battery Power 2mah
Selfish node Battery Power 0.012mah
Energy model Mica- Motes
Battery Dissipation Model Linear
6.6.8 PROBABILITY OF REACHABILITY (PoR)
The results show the impact of selfish nodes concentration on the PoR value as the
percentage of selfish nodes increases. It can be easily seen from the graph of Fig. 6.9
(a) and Fig. 6.9(b) that as the percentage of selfish and malicious nodes increases the
number of reachable paths becomes quite low for both the routing protocols (AODV,
AODV-n). It is important to note here that even at 100% of selfish and malicious node
concentration there is still communication between nodes. The underlying reason for
the same is malicious and selfish behavior of nodes is for others and not for themselves.
Network Lifetime
0
200
400
600
800
1000
1200
1400
1600
0 20 40 60 80 100
Selfish Nodes
Tim
e a
t w
hic
h F
irst
no
de g
ets
Dead
(Seco
nd
s)
AODV w ith hello enabled
AODV w ith Priority Level
= 0
AODV w ith Priority level =
1
AODV w ith Priority Level
= 2
AODV w ith Priority level =
3
119
Fig. 6.8(a) Network Lifetime for first node
Network Lifetime
0
200
400
600
800
1000
1200
1400
1600
0 20 40 60 80 100
Selfish Nodes
Tim
e a
t w
hic
h a
ll t
he n
od
es g
et
Dead
(S
eco
nd
s)
AODV w ith hello enabled
AODV w ith Priority Level
= 0
AODV w ith Priority level =
1
AODV w ith Priority Level
= 2
AODV w ith Priority level =
3
Fig. 6.8(b) Network Lifetime for all nodes
Probability of Reachability
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
Percentage of Selfish Nodes
Percen
tag
e P
ro
bab
ilit
y o
f
Reach
ab
ilit
y
AODV with Hello Disabled
AODV with Priority level = 3
AODV with Priority Level = 2
AODV with Priority Level = 1
AODV With Priority Level = 0
Fig. 6.9 (a) Probability of Reachability for selfish nodes
120
Probability of Reachability
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
Percentage of Malicious Nodes
Percen
tag
e P
ro
bab
ilit
y o
f
Reach
ab
ilit
y
AODV with Hello Disabled
AODV with Priority level = 3
AODV with Priority Level = 2
AODV with Priority Level = 1
AODV With Priority Level = 0
Fig. 6.9 (b) Probability of Reachability for malicious nodes
6.7 ANALYSIS OF SIMULATION AND RESULTS
The analysis of simulation results indicate that in an environment devoid of malicious
and selfish nodes plain AODV performs much better than AODV-n. As the
concentration of malicious/ selfish nodes increases AODV-1, 2, 3 outperform the plain
AODV. Many times, the performance of the AODV-1, 2, 3 protocols are better in an
environment with large concentration of malicious/selfish nodes (40-50%) than the
performance at the lower concentration of malicious/selfish nodes (10-30%). A careful
scrutiny of the data showed that at high concentration of malicious/selfish nodes most
of the routes formed were of the low hop count which allowed for easy passage of data
packets leading to comparatively small amount of end to end delay and jitter. The
performance of AODV-0 was the poor most where in the resources were used to collect
data and compute the trust class but this information was not used to select the trust
worthy intermediate nodes. Another note worthy feature of the simulation result was
that there was not much difference between the performance of AODV-1, 2, 3
indicating that the choice of lower trust class works in the equally effective manner as
in case of higher trust class. The network life time is very high when no node in the
121
network is selfish but as the concentration of selfish nodes it decreases very fast as
shown in Fig 6.7(a), (b). On analysis it was observed that the energy dissipation in the
nodes was mainly due to hello packets and not because of data packets sent from source
to destination.
top related