A Comparative Study for the Choice of Vehicular Ad hoc ... · respectively, kjam is the vehicular...

©Journal of Applied Sciences & Environmental Sustainability 1 (2): 33-51, 2013

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Research Article

A Comparative Study for the Choice of Vehicular Ad hoc Networks

(VANETs) Simulator Abubakar Aminu Mu’azu1, Ibrahim A. Lawal1, Lawal Haruna2 and Shamsuddeen Rabiu3

1Department of Information & Computer Sciences, Universiti Teknologi PETRONAS, Malaysia,

2Department Mathematical & Information Technology, Federal University Dutsin-ma, Katsina Nigeria,

3 School of Computing, Universiti Utara Malaysia (UUM), Malaysia

ARTICLE INFO

Article history Received: 1/12/2012

Accepted: 01/08/2013

A b s t r a c t

The amount of automobiles continues to be increasing on the road in the last couple of years. As a result of high density of vehicles, wide ranging threats and road accident is increasing. Wireless technology therefore is geared towards equipping technology advancement in vehicles to reduce these kinds of issues by sending messages to each other. The vehicular safety application needs to be thoroughly tested prior to it being implemented in a real world to use. Simulation is economical simply because it can hold out experiments without the presence of actual hardware and present a good compromise between complexity and accuracy. Simulation can be achieved by utilizing the network simulators implemented in software which are valuable tools for researchers to develop, test and diagnose network protocols. Vehicular Ad-Hoc Networks (VANET) makes it necessary that a traffic and network simulators ought to be used together to accomplish this test. Simulation is commonly used to verify and evaluate the performance of networks. There are several network simulators available; it is extremely difficult to choose the proper tool for performance testing with no complete analysis of existing tools. This paper presents the comparative survey about some popular mobility models as well as network simulators for VANETs. It may help the researchers to choose a simulation model that could be reliable and efficient. © Journal of Applied Sciences & Environmental Sustainability. All rights reserved.

Keywords: Vanet, simulation, mobility

model

I. Introduction

The Vehicle Ad-Hoc Network or perhaps VANET is considered a subgroup of mobile ad-hoc

network (MANET) that are capable of providing communications amongst surrounding

vehicles as well as between vehicles and nearby fixed equipment, usually known as roadside

unit [1]. It is a computer network on wheels. Each vehicle is regarded as a "node" in the

VANET topology. These nodes are equipped with DSRC (Dedicated Short Range Radio

Communication) with capable transceiver and processors which communicate with each

other within each other's radio range [2]. However, the unique property VANETs posture

from MANET is usually that the nodes are vehicles like cars, trucks, buses and motorcycles

[3]. VANET has some special nodes, the road side units which are static nodes that could

provide access to the internet to the VANET. Figure 1 shows a typical VANET scenario.

Mu’azu et al. / Journal of Applied Sciences & Environmental Sustainability (JASES)1 (2): 33-51, 2013

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Figure 1: Wireless technologies for future vehicular communication [4]

As a high complicated dynamics of real implementation of VANET application, there's

requirement for Simulation. This is due to it is economical and experiments can potentially

carried out without the presence of actual hardware and present an excellent compromise

between complexity and accuracy. [5]Simulation can be accomplished by utilizing the

network simulators implemented in software which have been valuable tools for researchers

to develop, test and diagnose network protocols. Simulation is commonly used to validate

and evaluate the overall performance of networks.

Usually, a network simulator is designed to incorporate a variety of networking technologies

and protocols and enable users to produce complex networks from basic building blocks like

clusters of nodes and links. Making use of their help, one can possibly design different

network topologies using various kinds of nodes which include end-hosts, hubs, network

bridges, routers, optical link-layer devices, and mobile units.

II. Overview of Network Simulation

Simulation is an important modern technology that could be employed on different science,

engineering, or some other application domains for a variety of capabilities [6]. For network

simulation, more precisely, it means how the computer assisted simulation technology is

being applied to the simulation of networking algorithms or systems by utilizing software

engineering. The application field is narrower than general simulation as well as being natural

that more and more specific requirements will probably be placed on network simulations by


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means of synthetic models for mobility [7]. Hence describe the actual mobility pattern of

independent nodes by simple terms.

a. Mobility Model

Mobility models ascertain the actual movement of mobile users, and ways in which their

position, velocity as well as acceleration change with time. These kinds of models are usually

useful for simulation purposes whenever different communication or navigation techniques

tend to be investigated [8] [7] [9] [5]. Table 1.1 below summarized some network models

associated with aspire issues.

Table 1: Summary of some network mobility model Model Issues Drawback

Random Way Point Model [27]

Earliest mobility model for ad-hoc networks.

Shown the problem of velocity decay

Every node picks up a random destination and a random velocity at certain points called waypoints.

Extended to take into account more realistic movements

Random Waypoint City Model [3]

Combines aspects of the Random Waypoint Mobility Model with the vector street maps

Produce completely useless results

High granularity as exact user locations are available

User movement is independent of other users and past trips, so that individual homes and workplaces of users are not modeled

Cellular Automaton (CA) [4]

Discrete time model of the vehicular traffic Does not consider effect of random acceleration and deceleration on the traffic flow.

Contains important aspects of fluid dynamical approach to traffic flow such as the transition from laminar to start-stop traffic in a natural way

STRAW (Street Random Waypoint)

[24]

Constrains node movement to streets defined by map data for real US cities Model did not take into

consideration the lane changing. Provides reasonable runtimes and memory consumption that scales

fairly well with the size of the simulation.

City Section Mobility Model [12]

The simulation area is a street network that represents a section of a city where the ad hoc network exists.

Model is only for small simulation area

The mobility model is explained by means of the framework, consisting of topological maps

like lanes, roads, streets, obstacles in mobility as well as communication model, car

velocities, the attraction and repulsion points, depending on traffic densities in relation to


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how the simulation time could change, vehicular distribution on roads and intelligent driving

pattern. The illustration of this framework is given in the figure below;

Figure 2: Mobility Models [10]

Consequently, a number of these simulation software exploit ability of building up mobility

patterns from the co-operation of a trip generation module, which defines the sets of points of

interest, along with a path computation module, in which task is to compute the optimal path

between those points [7].

The combination of trip generation and path computation methods provides a wide variety of

possibilities, whenever the definition of vehicular movement paths is a factor of interest in

the mobility simulation.

b. Micro-Mobility Features

The idea of vehicular micro-mobility includes every aspect based on an individual car’s

speed and acceleration modeling. The micro-mobility description plays the main role in the

generation of realistic vehicular movements, as it is responsible for effects which include

smooth speed variation, cars queues, traffic jams and overtaking. The movement is random in

a sense that vehicles select one destination and move towards it along a shortest-length path,

ignoring (and thus possibly overlapping) other vehicles during the motion [11]. While these


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models may work for isolated cars, they fail to reproduce realistic movements of groups of

vehicles.

jamk

kSSS −= 1,max maxmin

Where S is the output speed, Smin and Smax are the minimum and maximum speed

respectively, kjam is the vehicular density for which a traffic jam is detected, and k is the

current vehicular density of the road the node, whose speed is being computed, is moving on.

This last parameter is given by k = n/l, where n is the number of cars on the road and l is the

length of the road segment itself.

Conversely, the IDM [13]characterizes drivers’ behavior according to their front vehicle,

thus slipping to the so-called car following models category. The instantaneous acceleration

of a vehicle is computed according to the following equations.

ab

vvvTss

s

s

v

va

dt

dv

2*

*

01

0

24

∆++=

−−=

In the left hand Equation, v is the current speed of the vehicle, v0 is the desired velocity, s is

the distance from preceding vehicle and S* is the so called desired dynamical distance. This

last parameter is computed as shown in the right hand equation, and is a function of the

minimum bumper-to-bumper distance S0, the minimum safe time headway T, the speed

difference with respect to front vehicle velocity v, and the maximum acceleration and

deceleration values a and b.

III. SIMULATION FOR VANETS

In this section, we review various publicly available VANET simulators that are currently in

use by the research community.

Most of the time, network simulators attempt to model the real world networks. The

primary idea is that if a system could be modelled, then attributes of the model might be

changed and also the corresponding results can be analyzed [11]. For the reason that process

of model modification is comparatively cheap compared to complete real implementation,


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numerous types of scenarios can be analyzed at low-cost (relative to making changes to a real

network). However, if well modelled, they will be close enough so as to give the researcher a

meaningful insight into the network under test, and how changes will affect its operation.

However, [14]classified existing VANET simulation software into three different

categories. They are

a) Vehicular mobility model (software environments that generate vehicle movement)

b) Network simulators ((used to test the performance of networking protocols), and

c) Integrated Simulators (integrate the traffic simulator and network simulator)

A. VANET Mobility Model

Vehicular mobility generators are necessary to improve the level of reality in VANET

simulations. They create realistic vehicular mobility traces to be used as an input for the

network simulator [10]. The inputs from the mobility generator include the road model,

scenario parameters (i.e., maximum vehicular speed, rates of vehicle arrivals and departures,

etc). The output of the trace details the location of each and every vehicle at every time

instantaneous for the whole simulation time and their mobility profiles [15] . Table 1.2 shown

below illustrates some popular and most widely use VANET mobility models;

Table 2: Some popular and most widely use VANET mobility models

Model Aims Language Type

VanetMobiSim [7] Achieve realistic simulation of vanet mobility Java Open source

SUMO [16] Traffic & road design optimization C++ Open source

STRAW Provides reasonable runtimes and memory consumption that Scales fairly well with the size of the simulation.

Java Open source

MOVE [12], Extension of sumo that add GUI for describing map Java-based Open source

B. Network Simulators

Network simulators enable researchers to analyze how the network would undoubtedly

operate under several conditions. Users are usually able to modify the simulator in order to

meet their specific analysis needs. Compared to the expense and time associated with

establishing an entire testbed that contains multiple networked computers, routers and data

links, network simulators are relatively fast and inexpensive.


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i. Network Simulator-2

NS-2 [17] is an open-source discrete event network simulator that supports both wired and

wireless networks, including many MANET routing protocols and an implementation of the

IEEE 802.11 MAC layer [18] . It is the most widely used simulator for academic networking

research. The core of NS-2 is written in C++ and users interact with NS-2 by writing TCL

scripts. The simplified user view of the NS2 is shown in fig. 3 below;

Figure 3: NS2 Simplified User View

An additional attribute of NS2 is the event scheduler. In NS2, the event scheduler keeps

track of simulation time as well as generate all the events in the event queue by invoking

appropriate network components. All the network components use the event scheduler by

issuing an event for the packet and waiting for the event to be released before doing further

action on the packet.

ii. OMNeT++ Network Simulation

OMNeT++ [19] is an object-oriented modular discrete event network simulator.

OMNeT++ has a component-based design, new features and protocols can be supported

through modules. OMNeT++ supports network and mobility models through the

independently developed Mobility Framework and INET Framework modules as shown

below;


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Figure 4: GUI of OMNET++ simulator

C. Integrated Simulators

Integrated simulators usually consist of two sub-simulators: mobility simulator and

network simulator which communicate with each other like shown in Fig. 5. This simulator

offers a high level of maturity in both areas: mobility simulation and network simulation.

Integrated simulators have the advantage of being able to modify the TraCI parameters

depending on the information TraCI among the vehicles and vice versa. This can provide a

higher level of realism for VANET simulations which focus on response to accidents or

collisions and the improvements for those situations.

Figure 5: Composition Integrated Simulators

i) National Chiao Tung University Network Simulator (NCTUns)

NCTUns [20][21] stands for National Chiao Tung University network simulator. NCTUns

can simulate 80.211a, 802.11b, 802.11g and 802.11p technologies. NCTuns can simulate

multiple wireless interfaces inside one node including 802.11.p interface. After the release of

version 5 [21], NCTuns enhanced its usability for ITS. It uses a distributed architecture to

support remote simulations and concurrent simulations. It also uses open-system architecture

to enable protocol modules to be easily added to the simulator. During the simulation, each


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node is allowed to send either a UDP or TCP packet. Due to this novel methodology,

NCTUns provides many unique advantages that cannot be easily achieved by traditional

network simulators such as ns-2.

NCTUns supports parallel simulations on multi-core machines. By using an innovative

parallel simulation approach, it supports parallel simulations for fixed networks on multi-core

machines. It also provides a highly-integrated and professional GUI environment that can

help a user to quickly: (1) draw network topologies, (2) configure the protocol modules used

inside a node, (3) specify the moving paths of mobile nodes, (4) plot network performance

graphs, (5) play back the animation of a logged packet transfer trace, etc. All of these

operations can be easily, intuitively, and quickly done with the GUI.

Vast majority of Network simulators allow multiple TCP/IP versions (Tahoe and New

Reno) within single simulators whereas NCTUns allows merely a single instance of TCP/IP

version.

Functionally, it is broken into eight separate components some of most important

components are described: The first component stands out as the fully integrated GUI

environment in which a user can easily edit a network topology, configure the protocol

modules used inside a network node, specify mobile nodes' moving paths, plot performance

curves, play back animations of logged packet transfers, etc. The component topology editor

is used to generate topology [27], which is shown in Figure 3. The nodes are created using

the node editor. A node in the NCTUns represents a network device such as a switch or an

IEEE 802.11 (b) wireless LAN access point. This is shown in the figure 6. The node editor

provides a convenient environment.


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Figure 6: NCTUns Node editor

NCTUns posses some advantages over some simulator in such a way that it provides easy

to use GUI Environment. More so it is distributed and open-system architecture design

supports remote simulations and concurrent simulations, and allows new protocol modules to

be easily added to its simulation engine [22], thus, providing a better functionality and

performance evaluation. However, NCTUns can support a maximum of only 4096 nodes

inside a single simulation and the manipulation at every node has to be done node by node, or

all the nodes to the same time.

ii) GrooveNet

GrooveNet [23] is an integrated simulator that facilitates numerous models which define

communication, travel and traffic control make it possible for large scale simulations in street

maps of any US city. The current limitations are that map database does not indicate one-way

streets and the altitude of the street. GrooveNet is implemented in C++ and Qt graphics cross-

platform library in Linux. GrooveNet is based on the US Census Bureaus TIGER/Line 2000+

database format and is able to dynamically load counties at run-time. On startup GrooveNet

reads map database text files and converts the topology data into a binary encoded file with a

graph structure.


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Figure 7: Real and simulated vehicles interact in the GrooveNet integrated simulator

GrooveNet’s modular architecture take advantage of mobility, trip and message broadcast

models spanning a variety of link and physical layer communication models. It is possible to

run simulations of thousands of vehicles in any US city and to add new models for

networking, security, applications and vehicle interaction. GrooveNet supports multiple

network interfaces, GPS and events triggered from the vehicle’s on-board computer.

iii) TraNS (Traffic and Network Simulation Environment)

TraNS (Traffic and Network Simulation Environment) [24] is a simulation environment

that integrates both a mobility generator and a network simulator, and it provides a tool to

build realistic VANET simulations. TraNS provides a feedback between the vehicle behavior

and the mobility model. For example, when a vehicle broadcasts information reporting an

accident, some of the neighbouring vehicles may slow down. TraNS is an open-source

project providing an application-centric evaluation framework for VANETs.

TraNS v1.2 has several features, including: (a) support for realistic 802.11p, (b) automated

generation of road networks from TIGER and Shapele maps, (c) automated generation of

random vehicle routes, (d) mobility trace generation for ns-2, SUMO and ns-2 coupling

through the TraCI [WPR+08] interface, and (e) possibility to simulate road traffic events,

e.g., accidents.


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Figure 8; Graphical Interface of TraNS

iv) VEINS—Vehicles in Network Simulation

The developed simulation framework Veins [28] incorporates all the benefits from state-

of-the-art simulation techniques of both the network simulation and the road traffic

microsimulation domains. The Veins is an open source Inter-Vehicular Communication

(IVC) simulation framework composed of an event-based network simulator and a road

traffic microsimulation model.

Figure 9: event-based network simulator and a road traffic microsimulation model.

Veins is made up of two distinct simulators, OMNeT++ for network simulation and

SUMO for road traffic simulation. To perform IVC evaluations, both simulators are running

in parallel, connected via a TCP socket. The protocol for this communication has been

standardized as the Traffic Control Interface (TraCI). This allows bidirectionally-coupled

simulation of road traffic and network traffic. Movement of vehicles in the road traffic

simulator SUMO is reflected in movement of nodes in an OMNeT++ simulation. Nodes can


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then interact with the running road traffic simulation, e.g. to simulate the influence of IVC on

road traffic as shown below.

Figure 10: GUI screenshot of Veins running a VANET scenario on a map of Erlangen, Germany

IV. VANET SIMULATORS EVALUATION

In this section, some of the most popular VANET simulators were review taking into

consideration the mobility features as well as associated attributes that are freely obtainable

for VANET’s community.

Typically, network’s performance could be best evaluated with the implementation of

a simulation. A reasonable simulation can establish noticeably similar results to that of real

world. Construction of a simulation consequently seems predictable for VANET. There are

two main aspects of simulating VANET: one is the traffic simulation and other is network

simulation. The traffic simulation aids in creating traces of urban mobility model; this

information is fed into the network simulation. The network simulation builds topologies

between the nodes and vice versa. Surprisingly there seems no direct link between the two; it

is like two people talking in two different languages without understanding each other’s

conversation.


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A. Potential Candidates

All these models contributed toward various node mobility capabilities like velocity

variation, random movement within a topology boundary etc. Among all these

aforementioned models, the Random Waypoint model was widely used but the patterns it

generated had no match to the real world behaviour .The efforts vested in such project did not

prove worthy. Hence the scientific community geared their way towards other projects,

starting from simpler to more complex for the generation of mobility patterns. Unfortunately

these projects were more inclined towards the traffic side; only a minute amount of work had

been done in the network area.

To become qualified as an applicant for VANET simulator, the candidates must fulfill the

needs produced in section 3.2. Accordingly, the following simulators are available in the

course of VANET simulators.

I. MOVE

II. Trans

III. SUMO

IV. NCTUns

V. OMNET++

VI. GrooveNet

B. Comparison of Vanets Simulators

In this section, we present a comparison of the studied VANET simulators as well as

mobility models. As shown in the table 3 below, TraNS uses SUMO and ns-2 as well as

Veins 2.0. All simulators support different mobility models and provide microscopic traffic

simulation. NCTUns provides random speed models, while the others model street speed

instead. Currently, all simulators support trip and intersection models. So far, only TraNS and

NCTUns have an implementation of 802.11p, and only GrooveNet and TraNS provide built-

in VANET applications. In terms of ease of setup, OMET++ Veins and NCTUns are

considered the hardest ones. In terms of ease of use, TraNS and GrooveNet are preferred.

Since these simulators were developed with different focus, results obtained when

simulating similar VANET scenarios can differ greatly. TraNS and GrooveNet were


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developed to simulate VANETs. NCTUns was created for more general network simulation

purposes, while Veins 2.0 was designed for simulating VANETs.

Table 3: Comparison of the studied VANET simulators and mobility models by [25]

More so, table 4 presents a comparison of the Graphical User Interfaces (GUIs) provided

by the studied VANET simulators. All simulators provide both alphanumeric and

configuration files input, and console message output; nevertheless, the user interface for

TraNS appears more sophisticated than the others. A lot of manual parameter inputs are

needed for TraNS. All simulators provide street-level topology view. So far, only TraNS can

support visualization using Google Earth.


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Table 4: Comparison of VANET Simulators Graphical User Interfaces (GUIs) [25]

V. CONCLUSION

Due to the increasing popularity, attention in VANETs has prompted researchers to

develop accurate and realistic simulation tools. In this paper, we make a comprehensive

survey of several publicly obtainable VANET mobility generators, network simulators, as

well as VANET simulators. In depth comparison of the studied VANET simulators and

mobility models were also presented along with the GUI for each simulator.

However, in terms of VANET simulators, we studied TraNS, GrooveNet, NCTUns, and

Veins. TraNS and OMNET++ both involve the coupling of a VANET mobility generator

with a network simulator. GrooveNet and NCTUns, however, are self-contained simulators

with GrooveNet capable of supporting hybrid simulations, i.e., communications between

simulated vehicles and real vehicles.

Lastly, according to the study, simulators have many its features, but none of them offer

good support for all features for VANET simulation. Therefore we have searched a balanced

simulator that would offer a good user experience for VANETs. NCTUns and OMNET++ are

the best choices for the VANETs.

References


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Authors’ Biographies

Abubakar Aminu Mu’azu received the BSc. degree in computer science from

Usmanu Danfodiyo University Sokoto, Nigeria, in 2003, and the MSc. in

Information Technology Management from the Universiti Teknologi Malaysia

(UTM), in 2010. Currently pursuing his PhD studies at Universiti Teknologi

PETRONAS, Malaysian. He is an assistant Lecturer of computer science at the Umaru Musa

Yar’adua University Katsina Nigeria. His current research interests include VANET simulation,

intelligent transportation systems, quality of service, routing protocol design, and

implementation. He is also a member of the IEEE computer society. [email protected]

Ibrahim Alhaji Lawal ([email protected]) is a Ph.D. Candidate in IT

at the Universiti Teknologi PETRONAS Malaysia, Department of Information

and Computer & Information Science. He received his B.Sc. in Mathematics,

Postgraduate Diploma in Computer Science and M.Sc. in Computer Science from

Bayero University Kano Nigeria. He is also a Cisco Certified Network Associate from Cisco

Networking Academy U.S.A. He is now a Lecturer in the Department of Computer Science,

Federal College of Education Kano, Nigeria. His research interests are in the area of Data

Communication &Networking, mainly in Wireless Networks (WiMAX, WLAN, Ad hoc

Networks and Mesh Networks).

Lawal Haruna graduated with honors in Computer Science at the Usmanu Danfodiyo University

Sokoto, Nigeria in 2003. He received his MSc. in Computer Science from the Bayero University

Kano Nigeria in 2010. A working Lecturer at Federal University Dutsinma Katsina Nigeria. His

research interests include mobile and Pervasive computing, security and QoS on wireless

networks, as well as video coding and streaming.

Shamsuddeen Rabiu is a staff of Federal College of Education Katsina, Computer

Science Department. He received the BSc. degree in computer science from

Usmanu Danfodiyo University Sokoto, Nigeria, in 2003, and the MSc. in

Information Technology from the Universiti Utara Malaysia (UUM), in 2013. His

research interest include; Intelligent Transport System, Security and Trust over wireless network

and wireless modelling.

A Comparative Study for the Choice of Vehicular Ad hoc ... · respectively, kjam is the vehicular...

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