Jh2516461655

Sharnjeet Kaur, Dr. Gurpreet Singh Josan / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1646-1655
Performance Evaluation Of Topology Based Routing Protocols In
Vanet
Sharnjeet Kaur*, Dr. Gurpreet Singh Josan**
*(Department of Computer Science, Punjabi University, Patiala)
** (Assistant Professor, Department of Computer Science, Punjabi University, Patiala)

ABSTRACT
Vehicular Ad-hoc Networks (VANET) telecommunication and computer networks began
are formed between moving vehicles equipped with a strong emphasis on wires, both for the
with wireless interfaces, which are attracting a communications infrastructure and for the last hop
great deal of interest. VANET is a new where the actual connection towards the users'
communication paradigm that enables the terminals takes place [1]. In the last decade this trend
communication between mobile nodes having has shifted towards wireless networks, especially at
dynamic topology on roads. There are still the user side.VANET (Vehicular Ad-hoc Network)
several areas of VANETS, such as congestion is a new technology that has to be taken enormous
control, security, traffic engineering, traffic attention in the recent years. Due to rapid change in
management, dissemination of emergency topology and frequent disconnection makes it
information to avoid hazardous situations and difficult to design an efficient routing protocol for
routing protocols, which lack large amounts of routing data among vehicles, called V2V or vehicle
research. Here in this research we have to use to vehicle communication" [2]. VANET is a
OMNeT++ i.e. freely available simulator is used technology that uses moving cars as nodes in a
with traffic simulator (SUMO) that are uses the network to create a mobile network. It turns every
TraCI (Traffic control interface) module to participating car into a wireless router or node,
couple the simulators which works in sync. It uses allowing cars approximately 100 to 300 meters of
the UDP Basic Burst Notification application. In each other to connect and, in turn, create a network
this paper basically to evaluate the proactive and with a wide range. As cars fall out of the signal
reactive routing protocols that are commonly range and drop out of the network, other cars can
used in mobile ad-hoc networks, which will apply join in, connecting vehicles to one another so that a
to VANETs. Optimized Link State Routing Mobile Internet is created. It is estimated that the
(OLSR), Dynamic Source Routing (DSR) and first systems that will integrate this technology are
Dynamic MANET On-demand (DYMO) are police and fire vehicles to communicate with each
initially simulated in a city, main road, and other for safety purposes. Other purposes include
country environment in order to provide an essential alerts and accessing comforts and
overall evaluation. entertainment. VANETs are a kind of MANETs
provide vehicle to vehicle (V2V) and vehicle to
Keywords: OLSR, DSR, DYMO, VANETs, roadside wireless communications, this means that
OMNeT++, SUMO, IEEE 802.11b. every node can move freely within n/w coverage and
stay connected. Vehicles are equipped with wireless
1. INTRODUCTION transceivers and computerized control modules are
A vehicular network is a kind of wireless used.
networks that has emerged thanks to advances in
wireless technologies and the automotive industry
[1]. Vehicular networks are formed between moving
vehicles equipped with wireless interfaces that could
be of homogeneous or heterogeneous technologies.
These networks, also known as VANETs (Vehicular
Ad-hoc Networks) are considered as one of the ad-
hoc network real-life applications, enabling
communications among nearby vehicles as well as
between vehicles and nearby fixed equipment
(roadside equipment).

The development of communication
networks was a significant step for mankind,
undoubtedly facilitating everyday's tasks and
improving the quality of life. Both Figure 1: Vehicular Ad hoc Network [3]

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receiver is an important factor; the more the
VANETs integrates multiple Ad hoc networking distance, the smaller the probability of reception of
technologies such as Wi-Fi IEEE 802.11 b/g, packets. In infrastructure-based technologies,
WiMAX 802.16, Bluetooth, IRA, ZigBee for easy, handoff between base stations is also relevant [5].
accurate, effective and simple communication  Permanent access: Permanent access to
between vehicles on dynamic mobility[3]. the network is one of the main drawbacks of
vehicular communications. In VANET designs, a
Vehicular networks consist of large no. of physical infrastructure is not necessary, due to the
nodes, approximately no. of vehicles exceeding 750 inherent decentralized design.
million in the world today, these vehicles will  Location Awareness: Next generation
require an authority to govern it, and each vehicle vehicles are expected to exchange information not
can communicate with other vehicles using short only beyond their immediate surroundings and line-
radio signals of 2.4 GHz at bit rate of 11Mbps. This of-sight with other vehicles, but also with the road
communication is an Ad Hoc communication that infrastructure and Internet databases [6].
means each connected node can move freely, no  Time Awareness: Vehicular applications
wires required, the routers used are called road side often require a reliable communication channel that
unit (RSU). supports time-critical message transmissions.
 Penetration rate dependency: This rate is
2. BACKGROUND defined as the percentage of vehicles equipped with
The growing mobility of people and goods the necessary on board data unit (OBU) on the road.
incurs in high social costs: traffic congestion,
fatalities and injuries. Around the globe each year 3. ROUTING PROTOCOLS IN VANET
about 1.2 million people die because of traffic Routing is the act of moving information
accidents [4]. The traffic accidents place as the across an internetwork from a source to a
fourth cause of mortality by this statistics in the destination. Along the way, at least one intermediate
world. Also, this high number of fatalities and node typically is encountered. Routing occurs at
injuries high healthcare costs, more than any other Layer 3 (network layer) of the OSI model. The
type of injury or disease. routing protocols are further divided into number of
categories but here focus onto the OLSR, DSR,
It is in this context that Vehicular Ad-hoc DYMO protocols mainly, that are evaluated on the
Networks (VANETs) have emerged. A VANET is behalf of throughput and latency.
based on smart cars and base-stations, which share
information via wireless communications. This The topology based routing protocols are
interchange of data may have a great impact on further divided into two different categories for ad-
safety and driving, reducing the number of accidents hoc data networks, according to [7]: Proactive and
and helping to optimize transport. While the original reactive.
motivation for VANETs was to promote traffic
safety, recently it has also become increasingly The first is a proactive routing protocol,
obvious that VANETs open new vistas for Internet which relies on the periodic broadcast of data
access, distributed gaming, and the fast-growing network topology. Popular proactive protocol is
mobile entertainment industry. The importance and OLSR (Optimized Link State Routing) and FSR
potential impact of VANETs have been confirmed (fisheye state routing). The second category,
by the rapid proliferation of consortia involving car reactive routing protocols, can be viewed as a
manufacturers. solution to proactive routing protocols because they
only search for a route when one is needed. Some
2.1 Factors that affect communication in VANET popular reactive protocols are DSR and DYMO.
 High velocity of the vehicles.
 Environment factors: obstacles, tunnels, 3.1 Optimized Link State Routing (OLSR)
traffic jams, etc. Protocol
 Determined mobility patterns that depend OLSR is the proactive routing protocol that
on source to destination path and on traffic is evaluated in this paper. OLSR achieved RFC
conditions. status in 2003 (T. Clausen (Ed.), and P. Jacquet (Ed.)
 High congestion channels (e.g. due to high Oct. 2003) [8]. Basically OLSR is an optimization of
density of nodes). the classical link state algorithm adapted for the use
in wireless ad hoc networks. First, few nodes are
2.2 Network requirements in VANETs selected as Multipoint Relays (MPRs) to broadcast
applications the messages during the flooding process.
 Mobility: Wireless network technologies
allow devices to move freely. In 802.11 Second level of optimization is achieved by
transmissions the distance between the sender and using only MPRs to generate link state information.

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This results in minimizing the “number” of control further, To avoid the possibility of infinite route
messages flooded in the network. As a final level of discoveries, node E will piggyback the route reply
optimization, an MPR can chose to report only links on the new route request [9].
between itself and those nodes which have selected
it as their MPR. MPRs play a major role in the In the event that a node cannot successfully
functionality of the protocol[8]. transmit a packet to the next hop, it needs to perform
route maintenance. When forwarding packets along
OLSR is designed to support large and a source route, each node on the route is responsible
dense wireless networks. It is also suitable for for the successful transmission of the packet to the
scenarios, where the communicating pairs change next hop. This reply can either be done by using an
over time. Once the communicating pair changes, a existing part of the MAC protocol in use or done
route to new pair is readily available, and no control passively [10]. In the Figure 2, node B can confirm
traffic or route discovery process is needed as in the the receipt of the packet to node C by listening to see
case of reactive protocols. This can be beneficial for if node C tries to forward the packet again. A route
situations where time critical or safety related data error message is sent out in the event that a node
needs to be delivered with minimum possible delay. does not receive a successful transmission.

3.2 Dynamic Source Routing 3.3 Dynamic MANET On-Demand Routing
Dynamic Source Routing (DSR) is another (DYMO)
routing protocol that was specifically designed for The final routing protocol that was used for
use in multi-hop mobile ad-hoc networks [9]. Like evaluation is the Dynamic MANET On-Demand
OLSR, DSR is a completely self organizing (DYMO) routing protocol [11]. The DYMO routing
protocol. protocol is another protocol that is designed for use
It has two mechanisms: in mobile wireless ad-hoc networks. Unlike the work
in [12], this implementation will be integrated right
into the network layer and not as part of the
application layer. Just like DSR, DYMO consists of
two main operations: Route Discovery and Route
Maintenance.
Figure 2: Route Discovery for DSR Routing DYMO route discovery is performed
Algorithm similar to the DSR routing algorithms. When a node
• Route Discovery: The process of discovering a needs to send out a packet to another node it will
route from a source to a destination. first search its route cache or routing table to see if
• Route Maintenance: Allows for the topology of an up to date route exists. If one does, the source
the network to change and a nodes routing table to node uses that route to send the packet to its
remain fresh. DSR does not use any type of periodic destination. However, if a route does not exist the
packets or messages at any level. The purely on node must go through a process to find a path to the
demand behaviour of the DSR routing algorithm destination, called route discovery. The source node
allows it to cut down network overhead that do use it creates a route request (RREQ) message to send out
[9]. to all neighbouring nodes. The RREQ contains the
following information [13]:
Route discovery is the process that the DSR • Destination Address
algorithm uses to find a route to send a packet from • Sequence Number
source to destination. When no route is present the • Hope Count
source node transmits a route request (RREQ). Each • Next Hop
node broadcasts the message until it reaches the • Valid Timeout
destination. Once at the destination node, that node • Delete Timeout
will send back a route reply (RREP) to the source. The source node will then send out the
As shown in the Figure 2 node A wishes to send a RREQ via broadcast to all of the surrounding nodes.
packet to node E. Node A initiates a route discovery The receiving node will look at the packet to make
with an RREQ that contains node A as the initiator, sure that it has not seen it before and if it has the
an empty route record list, and a unique request ID. packet will be discarded. If it has not been seen
As each node broadcasts the request to all nodes before, the node will then start to look at the
within range, the route is built because each node information contained inside of the RREQ. Lastly, if
appends itself to the route record in the RREQ. the sequence number indicates there is new
information in the RREQ then the data in the routing
Once the route request reaches the table is updated and the RREQ is passed on. Once
intended destination, the destination node will send a the RREQ reaches the destination node, that node
route reply back to the initiator. Node E request

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will then form a route reply (RREP) that contains the created in order to fully simulate a VANET. The
new route and is sent back along the reverse path. wireless network was setup to be based on the
IEEE802.11b standard, which is commonly used in
4. IMPLIMENTATION FRAMEWORK VANET simulations [19]. The maps for the traffic
4.1 OMNeT++ simulation consisted of three different environments
OMNeT++ [14] is an open-source (city, main road, country). The purpose of using
simulation environment. The primary simulation multiple traffic environments was to expose the
applications are Internet simulations, mobility, and routing protocols to a variety of topologies rather
ad-hoc simulations. This simulator has a component- than just one. Figure 3 shows an example of how the
based design, meaning that new features and client and server interact.
protocols can be Performance evaluation of
vehicular ad-hoc networks over high speed
environment supported through modules.
In order to provide large scale simulations
with reusable models, OMNet++ uses modules to
develop the different components of the simulation.
Simple modules are the most basic modules as they
provide extremely basic functionality. Compound
modules are created by grouping simple modules
together to create an object with a complex
functionality, such as a vehicle equipped with an
IEEE802.11b radio. Modules in Omnet++ are
connected to each other’s input and output gates Figure 3: TraCI Connection Example
with the use of simple ’connection’ modules. These
connections are all defined in a file that uses the 4.4 simulations
NED language. NED models are reusable and The simulations were run in different sets.
designed to work with other NED files to create For each set of simulation runs there was up to 10
much larger models. A nice feature of Omnet++ is TxRx pairs. The number of TxRx pairs was varied
that it uses a two way editor to create and modify the between one and ten sync pairs. Also, the traffic
NED files that make up the environment. density change. The only difference between each
We use a text editor or the IDE’s graphical set was the number of transmit and receive (TxRx)
editor to create the network. Modules in the network pairs that existed in the network. The reason to vary
contain a lot of unassigned parameters, which need the number of TxRx pairs is to create more data
to be assigned before the simulation can be run. The network traffic that would have in impact on how
name of the network to be simulated, parameter quickly a message could get delivered between
values and other configuration option need to be TxRx pairs. The final parameter that changes in the
specified in the omnetpp.ini file. simulations was the density of the traffic, which
varied from high density (460 vehicles) to medium
4.2 Traffic Simulation density (250 vehicles) and to low density (165
Simulation of Urban Mobility (SUMO) is a vehicles).
C++ application developed to simulate the From each simulation, the average
movement of objects along a road network. It is a throughput and latency were recorded for
free and open sourced simulator. Along with being comparison of the protocols. This will allow for an
able to model small areas, SUMO is also capable of in-depth analysis of the strengths and weaknesses of
modelling traffic in large networks, such as cities or the routing protocols based on scenario and traffic
highway networks, without any changes. SUMO density.
simulations are considered to be multimodal,
meaning that every object in the simulation is 5. RESULTS AND ANALYSIS
simulated [16]. The routing protocols used for evaluation
are OLSR, DSR, and the DYMO protocols. All these
4.3 Simulator Coupling protocols are designed by various technologies;
To accurately model a VANET, Omnet++ however, they are all designed for mobile ad-hoc
and SUMO are connected with a technique to networks that have to play an important role within a
synchronize node movement between two VANET. Tables 1, 2, 3 consists the evaluated values
simulators. The Traffic Control Interface (TraCI) is of throughput using three different environments.
used to couple the simulators. VANET simulation
approaches used mobility traces that the network 5.1 Throughput
simulator read in [17, 18]. TraCI works in a client- The throughput is calculated by monitoring
server manner [17]. A wireless network, traffic map, the channel that determines the speed at which the
and obstacles in the wireless environment were packets are successfully transmitted by the

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application. The results for each simulation scenario 1 3 0 50 2 0 98 2 0 731
are shown in figures 4(A, B, C), 5(A, B, C) and 6. 3 2.9 1 9.9 0 .99
High Traffic Density- Throughput (bits/sec) 3 5 0 5 5
OLSR DSR DYMO 8. 5. 4.
Tx Ci C Ma Ci C Ma Ci C Ma 2 2 4
3 5 1
Rx ty ou in ty ou in ty ou in
pai nt Ro nt Ro nt Ro 2 3 0 50 2 0 49 2 0 652
r ry ad ry ad ry ad 0 4.1 7 2.5 4 .98
1 23 38 58 18 38 67 19 19 34 5 5 1 5 9
7. 5. 1.
39 5. 2.5 51 0. 1.9 23 0. 5.8
9 3 5
.6 25 2 .2 55 5 .1 15 5
5 1 4
5 3 1
3 2 0 73 2 0 52 2 0 905
2 19 36 66 22 14 76 20 15 36
5 5.1 3 3.4 1 .15
99 5. 5.5 65 5. 5.0 15 6. 6.5
5 5 2 5 4
.3 15 9 .7 52 2 .5 46 5
6 9 5 0. 5. 5.
4 1 7
3 19 31 86 18 39 11 13 13 57
2 2 4
35 3. 9.5 15 9. 65. 65 7. 7.3
4 2 0 95 1 0 47 1 0 902
.4 55 3 .3 77 55 .6 28 7
5 1 5 0 9.2 7 5.6 5 .56
8 3 9 5 4
4 13 90 67 22 18 46 18 38 12
5. 1. 2.
95 .0 8.4 19 9. 5.4 45 5. 35.
6 2 1
.6 3 6 .7 18 5 .9 65 56
5 6 2
1 8 9
5 1 0 66 1 0 53 1 0 882
5 16 36 75 17 45 10 13 16 71
7 5.5 6 5.3 5 .11
85 6. 3.8 45 8. 36. 22 5. 2.9
5 5 0 2 5
.4 55 5 .3 95 35 .1 23 8
4. 1. 6.
5 5
1 1 2
6 17 31 76 19 20 93 12 27 11
2 2 5
35 3. 5.1 01 1. 6.9 53 5. 20.
6 1 0 43 1 0 42 1 0 965
.0 18 8 .4 28 9 .9 12 35
8 8.6 1 3.5 3 .95
5 3 2
3 5 3 4 6
7 16 17 46 14 41 93 11 29 12
9. 9. 5.
65 0. 5.8 63 4. 5.6 17 4. 88.
1 2 2
.3 52 5 .2 65 5 .5 12 12
5 1 5
5 1 3
7 2 0 54 1 0 56 1 0 102
8 14 33 66 18 30 10 12 30 14
0 5.4 2 5.1 1 9.1
86 3. 5.2 35 9. 12. 67 8. 38.
6 6 3 2 6 2
.3 65 3 .2 65 96 .5 35 32
1. 8. 9.
8 5 6
0 1 2
9 16 23 82 18 38 10 13 33 15 1 2 4
81 2. 3.8 15 9. 55. 05 4. 85.
8 1 0 60 1 0 49 1 0 108
.8 68 4 .9 95 11 .2 02 05
5 2.4 1 8.5 2 5.1
9 8 3
9 5 8 5 8 6
10 13 19 82 17 32 10 13 34 17
8. 1. 8.
58 5. 2.5 80 0. 98. 85 4. 51.
5 3 6
.4 12 5 .9 31 53 .9 32 95
6 5 5
8 1 9
9 1 0 69 1 0 48 7 0 113
Table 1 Throughput results for high traffic density 4 5.3 1 4.1 8 5.4
scenario 0 4 7 1 5. 6
5. 4. 1
Medium Traffic Density- Throughput (bits/sec) 3 1 6
OLSR DSR DYMO 5 5
Tx Ci C Ma Ci C Ma Ci C 10 1 0 73 1 0 51 5 0 118
Rx ty ou in ty ou in ty ou Ma 4 5.2 3 2.5 7 5.9
pai nt Ro nt Ro nt in 7 4 5 6 5. 6
r ry ad ry ad ry Ro 8. 8. 5
ad 5 1 3
5 4

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Table 2 Throughput results for Medium traffic
density scenario
Low Traffic Density- Throughput (bits/sec)
OLSR DSR DYMO
Tx Ci C Ma Ci C Ma Ci C Ma
Rx ty ou in ty ou in ty ou in
pai nt Ro nt Ro nt Ro
r ry ad ry ad ry ad
1 18 0 23 17 0 44 17 0 12
23 8.6 85 5.1 05 35. (A)
.1 5 .1 3 .5 74
2 6 5
2 15 0 52 15 0 97 16 0 37
28 8.1 28 7.2 62 4.1
.9 3 .9 3 .8 4
9 9 5
3 11 0 61 24 0 80 11 0 90
85 8.6 35 3.9 55 5.6
.1 5 .1 9 .1 5
5 5 5
4 16 0 55 13 0 46 24 0 43
03 1.1 45 2.1 56 5.1 (B)
.0 5 .2 2 .1 2
2 9 4
5 11 0 70 15 0 59 23 0 66
12 6.9 39 8.1 12 8.6
.0 9 .1 6 .0 5
5 2 2
6 16 0 79 15 0 59 24 0 70
99 1.1 23 2.4 65 5.1
.1 5 .1 4 .5 4
1 4 8
7 14 0 87 14 0 57 18 0 71
85 5.1 55 2.9 63 6.2 (C)
.9 1 .1 9 .6 5
9 2 4 Figure 4(A, B, C): Throughput in bits/second for
8 11 0 88 17 0 55 16 0 68 city environment
31 8.0 35 5.7 42 5.6
.0 1 .6 2 .9 5
2 1 9
9 93 0 88 13 0 52 16 0 64
2. 2.1 21 3.9 31 3.5
13 6 .1 6 .1 6
2 4
10 85 0 86 12 0 51 15 0 70
5. 5.1 53 2.1 99 6.1
16 5 .1 3 .1 8
4 7 (A)
Table 3 Throughput results for Low traffic density
scenario

(B)

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The graphs in figure 5(A, B, C) show the
results obtained from the high, medium, and low
density main road simulations. The DYMO protocol
shows as the number of TxRx pairs increases the
throughput is increases. As to increase in number of
TxRx pairs that cause to increase the number of
nodes communicate with each other, which cause to
increase the chances to make the new routes. When
the less dense spacing of the vehicles, OLSR
actually gains throughput as the number of TxRx
pairs increase.
(C)
5.2 Latency Results
Figure 5(A, B, C): Throughput in bits/second for The latency results are important for
main road environment applications that are time sensitive, such as collision
avoidance or emergency vehicle warning. These
latency measurements are calculated by determining
the time between the message send by the sender
and to receive by the receive node. Tables 4, 5, 6
consist the values of latency by using three different
environments.

High Traffic Density-Latency (s)
OLSR DSR DYMO
Tx C C Ma C C Ma C C
Figure 6: Throughput in bits/second for Rx it ou in it ou in it ou Mai
Country environment pai y nt Ro y nt Ro y nt n
r ry ad ry ad ry Roa
The DSR routing protocol is to prove the d
higher throughput transmitting protocol in the city 1 0 1. 0.8 3 10 23. 0 0. 0.09
simulations of high traffic density. The high traffic . 53 66 . .0 24 . 00 1
density simulations in the country environment results 1 8 9 22 9 0 4
in some communication between the nodes. The 7 2 7
DYMO protocol remains the most stable throughout 1 4 5
the all 10 TxRx pairs, rather the DSR routing protocol 2 0 0. 0.7 1 14 14. 0 0. 0.00
has the highest throughput with 5 TxRx pairs. OLSR . 00 83 . .0 85 . 02 8
do not remain as stable as DYMO because over the 10 5 5 5 54 6 0 5
simulations the throughput oscillations from high to 3 2 0
low. The high traffic density city simulations, the all 1 2 5
three routing protocols with only one transmit receive 3 0 0. 0.8 1 9. 15. 0 4. 0.04
pair started with the throughput of 1850 bits/sec to . 01 78 . 71 02 . 24 4
2350 bits/sec. The OLSR routing protocol had the 3 5 8 7 5 0 5
most drastic change, about 100-150bits/s, as the 0 8 0
number of TxRx pairs were increased. The DSR 5 8 3
routing protocol remains stable with throughput 4 0 0. 2.0 2 20 11. 0 0. 0.00
between 1700 and 2250 bits/s. The DYMO protocol . 06 51 . .1 52 . 00 8
also shows the dip in throughput as on the OLSR that 1 6 0 05 8 0 6
are of 900bits/sec drop. The similar effects show the 6 2 0
medium traffic density environment for each of the 2 5 2
routing protocols. In figure 4 look at the second line 5 0 0. 1.0 2 2. 8.9 0 0. 0.01
graph, each of the routing protocols with the single . 00 12 . 55 03 . 00 1
TxRx pair started at a high throughput between 3 5 5 4 0 4
2000bits/sec to 3500 bits/sec and finished with 10 0 5 6
TxRx pairs at a level that are between 400 and 1600 2 4 5
bit/s. DSR showed the lower loss in throughput for 6 0 0. 0.4 2 8. 5.1 0 0. 0.00
the 10 simulations. The DYMO protocol has an . 85 81 . 59 42 . 05 9
increase in throughput from six to seven TxRx pairs. 3 3 8 5 0 5
However, the throughput drops to similar level as the 5 1 2
number of TxRx pairs increased. 5 4 5

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7 0 0. 1.0 3 7. 6.4 0 0. 0.14 5 6 1
. 05 28 . 55 65 . 89 5 6 6 7
1 8 1 1 0 1 9 0. 0 0.6 5. 0 4.1 0. 0 0.0
8 3 2 7 16 1 88 0 61
8 8 4 8 6 1
8 0 0. 0.9 2 6. 7.2 0 0. 0.07 8 4 7
. 65 99 . 80 13 . 15 4 10 0. 0 0.5 3. 0 3.9 0. 0 0.0
6 8 2 9 0 6 6 95 5 15 0 63
5 1 4 5 4 1
5 6 5 4 5 8
9 0 0. 0.7 2 5. 5.4 0 0. 0.04 Table 5 Latency results for Medium traffic density
. 58 35 . 76 85 . 06 5 scenario
6 1 7 1 0 5
6 8 2 Low Traffic Density-Latency (s)
1 5 8 OLSR DSR DYMO
10 0 0. 0.6 3 8. 5.9 0 0. 0.05
. 65 95 . 42 95 . 12 4 Tx Ci C Ma Ci C Ma Ci C Ma
7 3 1 4 0 2 Rx ty ou in ty ou in ty ou in
7 3 3 pai nt Ro nt Ro nt Ro
3 6 8 r ry ad ry ad ry ad
Table 4 Latency results for high traffic density 1 0. 0 0.0 3. 0 16. 0. 0 0.0
scenario 06 05 62 78 00 02
25 81 98 00 45 25
2 1 1 2
Medium Traffic Density- Latency (s)
2 0. 0 0.4 3. 0 10. 0. 0 0.0
OLSR DSR DYMO 34 88 75 12 00 06
Tx C C Ma C C Ma C C Ma 07 91 99 16 15 89
Rx it ou in it ou in it ou in 5 9 3 6
pai y ntr Ro y nt Ro y nt Ro 3 0. 0 0.2 3. 0 7.9 0. 0 0.0
r y ad ry ad ry ad 67 78 45 06 00 04
1 0. 0 1.1 3. 0 6.4 0. 0 0.0 04 82 60 05 50 28
2 45 6 45 0 93 5 1 1
5 6 0 4 0. 0 0.5 3. 0 8.2 0. 0 0.0
1 5 4 53 24 06 36 00 13
2 0. 0 0.4 1. 0 14. 0. 0 0.0 53 15 55 01 26 52
0 55 9 877 0 05 5 2 5
6 4 0 5 0. 0 0.8 3. 0 7.1 0. 0 0.0
5 5 2 35 32 21 25 00 04
3 0. 0 0.5 2. 0 4.5 0. 0 0.0 86 95 89 52 83 28
3 52 9 08 0 12 5 2 4
1 1 3 6 0. 0 0.7 2. 0 6.5 0. 0 0.0
5 2 8 46 57 96 78 00 09
4 0. 0 1.0 5. 0 8.1 0. 0 0.0 55 61 50 12 65 55
5 65 7 75 0 18 75 2 1
5 3 0 7 0. 0 0.9 2. 0 6.5 0. 0 0.0
1 5 9 28 45 08 67 00 10
5 0. 0 3.2 4. 0 3.9 0. 0 0.1 70 95 55 14 70 56
2 95 8 95 0 06 2 1 5
9 0 0 8 0. 0 1.0 2. 0 6.1 0. 0 0.0
5 9 6 28 65 65 86 00 11
6 0. 0 1.0 2. 0 5.0 0. 0 0.0 05 99 39 42 75 57
2 65 6 05 0 53 02 9 8
6 3 1 9 0. 0 1.1 2. 0 5.7 0. 0 0.0
5 1 5 42 91 45 87 00 12
7 0. 0 0.6 4. 0 6.8 0. 0 0.0 82 00 34 86 81 67
2 12 8 22 0 55 05 2 5 3
0 1 1 10 0. 0 1.3 2. 0 5.4 0. 0 0.0
1 6 6 43 21 32 07 00 13
8 0. 0 0.5 4. 0 4.4 0. 0 0.0 95 56 29 02 86 75
9 98 1 58 0 58 65 5 1

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Table 6 Latency results for low traffic density
scenarios

(b)

(a)

(c)

Figure 8(a, b, c): Latency in seconds for main road
environment
(b)

Figure 9: Latency in seconds for country
environment

(c) In figures 7(a, b, c), 8(a, b, c), and 9 shows the
Figure 7(a, b, c): Latency in seconds for city results of latency in different traffic environments
environment with multiple TxRx pairs. The most noticeable thing
in all plots the DSR routing protocol has the highest
latency between 5 to 10 seconds in all simulations,
and the DYMO having the lowest latency between
0.005 to 0.1 seconds. The city environment resulted
in some of the lowest latencies for DSR that ranged
between 2 seconds and 7 seconds. The main road
and country scenarios are the environments that
resulted in much higher latencies. For each of the
main road environments, when the TxRx pair’s
remains low the DSR routing protocol has the
highest latency. The OLSR is the second lowest
latency protocol under 3 to 3.5.
(a)
6. CONCLUSION AND FUTURE WORK
In this conclusion, the DYMO routing
protocol is to be the best choice for a routing
protocol because of its very low latencies and

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