performance analysis of vehicular ad hoc (2)

8/7/2019 Performance Analysis of Vehicular Ad hoc (2)

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Abstract — In the past, many researchers depend

highly on various network simulation tools such as Ns2,

OMNET++, Jist, Glomosim, OPNET, and JSim [1] to

analyze the performance of various routing algorithms.

However many of these simulation tools have limited

mobility models such as the Random Waypoint and fixed

direction movement. Recent ever-increasing interest in

vehicular ad hoc networks communication (VAC)

necessitates vehicular movement performance study via

realistic mobility pattern simulation. Unlike many other

works, we present a study on the performance of various

routing algorithms using Ns2 except that the mobility

pattern is generated using a microscopic vehicular

movement simulation tool called SUMO. We then

investigate and attempt to quantify the effects of the

vehicular traffic parameters such as the average speed,

vehicle density and road topology on the overall VANET

performance.

Index Terms —vehicular ad hoc networks (VANET),

intervehicle communication (IVC), routing.

I. I NTRODUCTION

ver increasing recent interest growth in VAC were initially

aimed to provide comforts and safety for passengers on

the road. Indeed, VAC has benefited us in many ways such as

higher reliability telecommunication service at a more

affordable cost. Many people aren’t aware of this special

variation of network service is free compared to the costly 3G

and WiMAX network services. Hence, VAC able to offers a

very attractive low-cost network services. This criteria

benefited many parties especially telecommunication service

provider in extending their coverage area without substantial

investment of wireless infrastructure. Unfortunately, the

performance of VANET highly depends on its routing

protocols. Many existing routing performance deteriorates as it

runs within the versatile vehicular environment. VAC demands

for a highly self-organized enabling extension of coverage area

via adaptive routing mechanism such as path extension. Such

F. A. Author is with the National Institute of Standards and Technology,

Boulder, CO 80305 USA (corresponding author to provide phone: 303-555-

5555; fax: 303-555-5555; e-mail: author@ boulder.nist.gov).

requirement necessitates a study of routing performance and its

behavior across various mobility patterns in vehicular

environment. In this article, we will study of routing protocol

performance under realistic vehicular mobility environment.

We model the vehicle movement using Sub-Urban Mobility

Simulator (SUMO) [2], a microscopic vehicular simulator.

This movement model is then ported into network simulator

ns2 and verified using network animator (NAM) [5]. We then

attempt to quantify the effect of the vehicle movement

parameters (speed, density, and topology). Using these

parameters, we conduct an extensive simulation and examinehow they affect the performance of VANETs.

Arguably, simulation results are not a total representation of

actual vehicular network performance, but it is a key to study

and verify the vehicular routing algorithm performances and

its behavior. Such study is essential to aid the design of

VANET routing algorithm.

II. SIMULATION NETWORK MODEL

We modeled a few typical vehicular environments namely

high-speed highway, variable vehicle speed (VSS) region, and

bus in a city environment. Then we investigate the routing

protocol performances using the realistic mobility patterns.

Fig. 1. Mobility Pattern for Highway

Performance Analysis of Vehicular Ad hoc

Networks with Realistic Mobility Pattern

Wai Foo Chan, Moh Lim Sim, and Sze Wei LeeThird C. Author, M er , I EEE

E

318

Proceedings of the 2007 IEEE International Conference on Telecommunications andMalaysia International Conference on Communications, 14-17 May 2007, Penang, Malaysia

1-4244-1094-0/07/$25.00 ©2007 IEEE.

Wai Foo Chan, Moh Lim Sim, and Sze Wei Lee.Faculty of Engineering, Multimedia University, 63100 Cyberjaya, Selangor, MALAYSIA



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a. High-speed Highway Environment

A typical high-speed highway movement with dual lane

opposite directions across a stretch of 4 kilometers distance is

considered within the simulation. This highway vehicle model

consists of 4000 nodes. Each 2000 nodes pairs are moving on

opposite direction. Each vehicle is assigned with varying speed

with various maximum speeds ranging from 10km/h to

120km/h. This vehicular environment model is simulated on

SUMO simulation tools. The mobility model is ported into ns2simulator. Figure 1 illustrates the vehicular model portrays

within the microscopic mobility simulator. The ported mobility

pattern is tested and verified with NAM that comes with ns2

simulator. In this simulation we attempts to quantify typical

movement parameters as such vehicle speed and examines the

effects on ad hoc network performance.

b. Variable speed scenario (VSS) Environment

VSS is a typical vehicular environment where all vehicles

slows down at certain point of the road due to presence of

external factor along the road such as road constructions,

accident, and submergence of lanes, pedestrian crossing and

school children zone. Such condition reduces distance between

vehicles and increases vehicle network density. To model VSS

scenario, we consider a 4km stretch road with a slight curve

corner slowing down the vehicle and caused accident. 500

vehicle nodes are considered for 534 seconds in this

simulation using SUMO. The accident car is labeled in red

color as illustrated in Figure 2b. At time t0, the vehicle queue

in lane 1(L1), before the curve corner were relatively short. Atlane2 (L2), The vehicles were separated relatively sparse as

illustrated in Figure 2a. Here the communication route path

can be formed from node within L1 and node within L2.

At t1, the accident occurred at the curve corner. This

accidents block vehicle from going through the road and

lengthen the queue in L1. The increases of the queue length

introduce higher density network at L1. Meanwhile, at lane2

(L2) the vehicles were sparsely as illustrated in Figure 2b.

Hence, the communication between vehicles from L1 to L2

becomes impossible.

At t3, the accident has been cleared. Such scenario causes

the network topology changed. L2 is accommodated withvehicles nodes enabling the communication between vehicles

from L1 to L2 again.

c. Public Bus within City Environment

We use grid to represent a city environment. Figure 3

depicts the modeled city. Figure 3 is just the overview of the

modeled grid city. The zoom in view of the city model Figure

4 illustrates the details of the modeled city. In the city, each

empty cell is 200 meter-square spaces represents building

spaces between the roads. Between each junction, we have

modeled traffic lights within the road. Within the model, bus

stops are placed on 3 spots within the city. Each bus stop is

configured with varying bus line. In this model we imitate

realistic bus stop for different bus line service within the city.

Presence of traffic lights and bus stops enables us emulates

natural vehicular environment as such “stop and go”

condition. Each node is labeled with different color to

indicate with different route path as shown in the Figure 4.

Each node is being assigned vehicle type, ranging from bus

with 5 meters length (long yellow vehicle) and car (triangle

green, pink or yellow node) with an average length of 2

meters as illustrated in Figure 4. For bus node, the bus

services available for every 1 minutes interval. A total of 90

buses are simulated for about 1200 seconds in this modelusing SUMO. Using this mobility model, we manage to

simulate and study the network performance under realistic

mobility traces.

Using this mobility pattern, we examine the network

performance of a heterogeneous wireless cum wired network

in urban city environment. We consider simple wireless

application such as Internet that requires city bus to

communicate with base-station on peak hour and non-peak

hours. On peak hours, the frequency of bus trip is higher Fig. 2a-c. Accident Environment

2b. During Accident, t1

2c. Right After Accident Clearance, t2

L1

L2

L2

L1

L1

L2

2a. Before Accident, t0

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within the urban city. The packet throughput from bus to the

base-station is then calculated to measure the network

performance. Based on this statistic, a study on network

performance and behavior is then conducted.

Fig. 3. City Model as Grid

Fig. 4. Public Bus within City

Simulation Parameters

a. Highway

The realistic mobility model is then imported into ns2

network simulator mobility model. Assuming not all vehicle in

the highway were equipped with WiFi equipment, we

simulated model consists of 1 percent random sampling of the

generated 4000 nodes within the highway. Each of therandomly sampled vehicles is assumed to be equipped with

IEEE 802.11b network adapter with a transmission range/

radius of 250 meters moving within the highway. In the

simulation we study and compare the performance of Dynamic

Source Routing Algorithm (DSR) [3], Ad hoc On-Demand

Distance Vector (AODV) [6] and Optimized Link State

Routing Algorithm (OLSR) [4]. OLSR is classified as a

proactive routing algorithm while DSR can be classified as

reactive routing algorithm. We sampled 50% of the overall

vehicles in the model as communicating pairs for 200 seconds.

For each simulation run, the source node and destination were

randomly picked to imitating the typical random connection

establishment within highway. An assumption of not all

vehicles were involved in the communications within the

highway is made here. Network performance is analyzed in

terms of packet throughput, delay and jitter. For varying

vehicle speed setting, generally the simulation was repeated

for many times.

TransmissionRange (m)

250 m

No of nodes 40

Area (mxm) 12100x100Average Speed(km/h)

10,30,50,70,100,120

Mobility Pattern Highway

Simulation time(s)

920-1100

Routing Algorithm DSR, OLSR, AODV

MAC 802.11 b

Table 1. Simulation Parameter for Highway

b. Variable Speed Scenario

This network simulation model consists of 60 vehicle nodes.

Each vehicle is assumed to be equipped with IEEE 802.11bnetwork adapter with a transmission range/ radius of 250

meters moving across different section of roads with different

speed. We modeled in such way where each vehicle attempts

to communicate with others vehicle imitating the traffic

scenario of information sharing between vehicles from one end

to another end of the road to accessing Internet services/base-

station at the other end of road. Upon accident took place,

vehicles slow down their car and stops at lane L1 separating

the vehicles in lane L2. At this point of time, there would be a

sudden increased of access of network service for the

Vehicle

Bus Stop

Bus

Traffic Lights

ZOOM VIEW

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passenger to seek for infotainment info while waiting for the

accident to be cleared. Many calls or message to be triggered

to inform the others party on the delay of trip during this time.

Having this scenario, vehicles are required to be connected to

the base station at the other end of the road, however the base

station along the road is considerably costly and installed

distance apart. Hence vehicles required behaving in a self-

organized manner to form ad-hoc network to extend the

network between vehicles from right end to left end of lane L1.We assume that the base station to be at the left end of lane

L1. Network performance is analyzed in terms of packet

throughput from one end of the road to another end. The

behavior of network performance is then investigated.


250 m

No of nodes 40

Area (mxm) 4000x100

Average Speed(km/h) 30

Mobility Pattern VSS

Simulation time(s)

534

Routing Algorithm DSR

MAC 802.11 b

Table 2. Simulation Parameter for VSS

c. Urban City

This network simulation model consists of 95 busses node

and a base station node. Each bus is assumed to be equipped

with IEEE 802.11b network adapter with a transmission range/radius of 250 meters travels at an average speed of 30 km/h

within an area 900 square-meter of urban city.


250 m

No of nodes 95, 1 base station

Area (mxm) 900x900

Average Speed(km/h)

30

Mobility Pattern Urban City

Simulation time(s)

1200

Routing Algorithm AODV, OLSR

MAC 802.11 b

Table 3. Simulation Parameter for Urban City

Within this simulation, we modeled in a way where each bus

attempts to communicate with others bus and an access-point

(modeled as base-station) imitating real-life traffic scenario of

information sharing between busses and user accessing

Internet services. This variant of application is inherently

observable from our daily life such as passenger accessing

Internet from a bus. For varying vehicle environment in urban

city, generally the simulation was repeated for 20 times using

both peak hour and non-peak hour mobility model. Network

performance is analyzed in terms of packet throughput from

the bus to the base station. The behavior of network

performance is then investigated. In the simulation we also

study and compare the performance of both proactive

Optimized Link State Routing Algorithm (OLSR) [4] andreactive routing algorithm, Ad hoc On-Demand Distance

Vector (AODV) [6].

III. SIMULATION RESULT

We executed 50 – 60 runs of the network simulator

for each varying vehicle speed in each time simulation. Each

simulation last about 900-1200 seconds. The ad hoc network

in the simulation consists of 40 mobile nodes with movement

pattern that mimic a vehicle movement pattern within highway.

What we present here is the average packet throughput, delay

and jitter over 50 – 60 runs of the simulator for each of the

different speeds; standard deviation for all cases is within the

range of 5% - 20% of the average value. Figure 5, 6, 7 shows

the average packet throughput, delay and jitter of each node

for both OLSR and DSR routing algorithms.

Throughput vs Speed

0

5

10

15

20

25

0 50 100 150

Speed (km/h)

T o t a l T h r o u g

h p u t

( k b p s ) DSR

AODV

OLSR

Fig. 5. Packet Throughput vs. Vehicle Speed

We observed that averagely DSR routing algorithm

conceives higher throughput than any other compared routing

algorithm. All the routing algorithm’s performance (DSR,ODV and OLSR) deteriorates over node speed as illustrated in

Figure 5. Frequent and high network fragmentation causes this

trend of packet throughput ratio declination. Packet end-to-end

delay and jitter on the other hand increases over node speed as

illustrated in Figure 6 and 7.

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5

Packet Delay vs Speed

-1000

0

10002000

3000

4000

5000

6000

7000

0 50 100 150

Speed (km/h)

D e l a y ( m s )

Delay DSR

Delay AODV

Delay OLSR

Fig. 6. Packet Delay vs. Vehicle Speed

Packet Jitter

-500

0

500

1000

1500

2000

0 50 100 150

Speed (km/h)

J i t t e r ( m s )

Jitter DSR

Jitter AODV

Jitter OLSR

Fig. 7. Packet Jitter vs. Vehicle Speed

To study the effect of vehicle density, we measure the

performance of packet throughput when number of vehicle

increases in the queue of lane L1 after the accident as

illustrated in Figure 2b. The packet delivery ratio performance

due to the effects of network density is presented at Figure 8

and 9.

Total Throughput

010

20

30

40

0 10 20 30

Number of Vehicle per Lane

Communicating

T o

t a l P a c k e t

T h r o u g h p u t ( k b i t )

TotalThroughput

Fig. 8. Packet Throughput vs. No of vehicle per lane.

From Figure 8, we observed that the total packet throughput

increases when the number of total vehicle communicating per

lane increases after the accident. This appreciation of

throughput value is due to the number of communicating

within the same lane in a near distance. However when

vehicle density increases, the throughput portray some increase

of throughput but the throughput per node decline after that.

This declination is due to the average load of data transmission

within each vehicle.

Throughput per Node

0

0.5

1

1.5

2

0 10 20 30

Number of Vehicle per Lane

Communicating

T h r o u g h p u t ( k b p s )

Throughput

Fig. 9. Packet Throughput vs. No of vehicle per lane.

Graph in Figure 10 depicts the average packet throughput

for peak hours and non-peak hours. We observed that the

total packet throughput for peak hours is higher compared to

the non-peak hours. During peak hours, the bus trip

frequency is higher and vehicle moves slower in urban city

as people were rushing to work accommodating the roads

space. The increase of bus density within the city assists the

ad hoc network formation. Hence the average total

throughput ratio much higher to the throughput value

portray within the sparse network model.

Average Packet Throughput

050

100

150

200

250

300

350

400

450

Environment (Dense/Sparse)

A v

e r a g e P a c k e t T h r o u g h p u t

( k b i t )

AODV-Peak

AODV-OffPeak

OLSR-Peak

OLSR-OffPeak

Fig. 10. Average Packet Throughput.

During off peak-hours, bus trip frequency drops and

subsequently reduces the density of buses within the road.

This scenario contributes to the declination of the average

total packet throughput from bus to the base-station. The

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graph also indicates with the presence of ad hoc network

aids packet transmission, yielding higher packet throughput.

IV. SUMMARY

A realistic vehicular simulation model has been presented.

And yet for future, there are still ample of space for further

improvement remains for the research community to explore.

Realistic traffic model for instance have yet to be studied

thoroughly and modeled. Researcher may also consider on

expanding network and provide extensive simulation results on

various vehicle scenarios. Our future work aims to enhance

performance for existing routing algorithm for VANET.

R EFERENCES

[1] http://www.idsia.ch/~andrea/simtools.html

[2] SUMO - http://sumo.sourceforge.net

[3] Dynamic Source Routing (DSR), David B. Johnson,

David A. Maltz,1998, http://www.ietf.org/internet-

drafts/draft-ietf-manet-dsr-10.txt

[4] Optimized Link State Routing (OLSR), Adjih

Clausen, Jacquet Laouiti, Minet Muhlethaler,

Qayyum Viennot, 1998,

http://hipercom.inria.fr/olsr/draft-ietf-manet-olsr-

09.txt[5] NS2, http://www.isi.edu/nsnam/ns/

[6] AODV, Charles E. Perkins,Elizabeth M. Belding-

Royer,Santa Barbara,Ian D. Chakeres.

http://moment.cs.ucsb.edu/pub/draft-perkins-manet-

aodvbis-00.txt

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performance analysis of vehicular ad hoc (2)

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