Modeling and Simulation of VANET In Traffic City

Modeling and Simulation of VANET In Traffic City Younes Regragui

Najem Moussa

LAROSERI, Department of Computer Science Chouaib Doukkali University, El ladida Morocco. Email: [email protected]

LAROSERI, Department of Computer Science Chouaib Doukkali University, El ladida Morocco. Email: [email protected]

Abstract-Vehicle-to-vehicle communication is a very actual and challenging topic which attracts a considerable attention from there search community. Vehicles are able to exchange information within these networks without the need of installing any infrastructure along the road side. To evaluate a Vehicular Ad Hoc Networks (VANETs) in traffic city, we present a simple model of simulation based on a Cellular Automaton technique specified by the rules which control communication exchange and the mobility of vehicles. Moreover, in a Cellular Automaton, space and time are divided into discrete cells and steps which can reduces the complexity of the system and cost of deployment. The model used here is based on the cellular automata(CA) model proposed by Biham-Middleton-Levine(BML). It considers two kinds of drivers which obeys the traffic light rules. Each vehicle generates an amount of data and hope to send it to the base station (BS) located in the middle of the network. In this work, we examine the performance of VANET in terms of average throughput. Simulation results show that the traffic patterns have an important impact over data communication at the base station as well as in the whole capacity of the communication network.

Keywords-Throughput, cellular automata, BML model, wire­ less communication, traffic light. I.

INTRODUCTION

Vehicular Ad Hoc Networks(VANETs) [1] are a special case of Mobile Ad Hoc Networks(MANETs) and consist of a number of vehicles traveling on urban streets, capable of communicating with each other without a fixed infrastructure. Each vehicle is not only responsible for network traffic related to itself but also can forward unrelated traffic such as a router. For that a number of protocols have been developed, such as DSR, AODV, TORA. Vehicular Ad-hoc Networks(VANETs) have recently attracted a great attention of research community due to their wide range of features and the technical challenges they exhibit [2]-[7]. The demand for Vehicle-to-Vehicle (V2V ) and Vehicle-to-Roadside (V RC) or Vehicle-to-Infrastructure (V2I) is always growing because of the need of entertainment services on board of vehicles to improve traveling comfort, and/or road safety. Many VANET problems are studied in the literature. In [8] proposes a simple and efficient distributed broadcast algorithm for warning delivery services to be used in inter-vehicular ad hoc networks. Knorr et al [ 9] presented a method to reduce congestion and improve traffic flow based on the use of vehicle-to-vehicle conununication, solely based on periodic beacon messages and using only velocity and position as a source of traffic state estimation the proposed method makes minimal requirements to the technical implementation. Due to its discrete nature, the CA model allows very fast implementation and can simulate a very large network micro-

scopicaUy in real time. Various models have been proposed to study and solve the problem of traffic flow in cities. The two-dimensional CA traffic model was firstly proposed by Biham-Middleton-Levine(BML) in 1992 [2]. Although the model is extremely simple, it displays extraordinarily complex behaviors, e. g. , phase transition and self-organization. It is generally believed that the original BML exhibits either freely flowing phase or jamming phase. Since then, many extensive researches, based on this model, have been done and it serves as a theoretical underpinning for physicists to modeling urban traffic [10]-[12]. In this paper, we present a theoretical framework for studying the data communication over VANET using Cellular automata models. All vehicles are constraint to move on the lattice with respect to traffic light rules. Their mobility is then modeled using BML model. The proposed model evaluation is based on Ad Hoc On-Demand Distance Vector Routing Protocol (AODV ), and User Datagram Protocol (UDP) transport pro­ tocol. We develop a wireless communication model in which each vehicle generates an amount of data and hope to send it to the base station (BS) located in the middle of the network. The paper is organized as follows. In section 2, the model is presented in detail. In section 3, the numerical results were reported and the occurrence reason is explained. The conclusions are given in section 4. II. A.

MODEL

Wireless communication network model

Consider a network of N autonomous nodes (see Fig. 1 ), 0, that are able to generate packets. A finite queue of size m is associated with each node, while the nodes throughput is constrained by the wireless channel capacity. In this model, each node is defined as a vehicle. If it has data in its queue, the vehicle can attempt to send packets to the base station through CSMAICA model.

N

>

1) Traffic generation model: The generation rate of packets is provided at each mobile vehicle and controlled for each (t + T) with probability A E [0,1]. In this paper, We consider that each time step is equivalent to one seconde. The Traffic generation algorithm can be written formally as:

Sv : set of vehicles, A : parameter E [0,1]. each t ::; T do for all each vehicle E Sv do if A > random value in [0,1] then return Generate a packet for the vehicle

Require:

1: for all 2: 3: 4:

978-1-4799-7054-4/14/$31.00 ©2014 IEEE

5:

transmission range of (A) are considered as hidden nodes and aren't allowed to send a packet, they must wait for the next step of time (see Fig. 4).

end if end for

6:

7: end for

Fig. 1.

If the selected sender (A) attempts to send a packet towards its destination (B), then all senders located in transmission range of (A) and haven't a destination located in transmission range of (A) are considered as exposed nodes and can send a packet during the same step of time (see Fig. 4).

3)

For each node in the network, there is one action that is allowed at the same time: sending or receiving of data (see Fig. 4).

The illustration of vehicles in the square lattice.

2) Propagation model: In this work, we use a free space propagation model which assumes the ideal propagation con­ dition that there is only one line-of-sight path between the transmitter and receiver. For any given vehicle v at time t, the model uses the Moore neighborhood (see Fig. 2). The Moore neighborhood of a vehicle is a next ended Von Neumann neighborhood which includes, in addition, the four diagonally neighboring cells Northwest, North-East, South­ West and South-East. 3) CSMAICA model:

The goal of CSMAICA is to provide for each node, an equal opportunity, to access the medium, thus avoiding the hidden and exposed node problems (see Fig. 3). This is done via contention, giving every node the same probability to send a packet on the network. In addition, the implementation model developed for 802.11 RTS/CTS mechanism which is the simplest possible model that can controls actions of sending and receiving data packets between vehicles in the network. This can be written formally as: •

2)

Selection of a random sender from a list of senders ordered by a probability E [0,1], the others senders keep their probability value for getting a priority for next attempt of sending. This list is updated for every time step. The next step is to avoid the hidden and exposed node problems. 1)

If the selected sender (A) attempts to send a packet towards its destination (B), then all senders located in receiving range of (B) and all senders which attempt to send a packet towards its destination located in

Fig. 2. The Moore neighborhood of a vehicle. The symbol R1 represents the radius of Moore neighborhood.

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... . Hidden node problem .

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.

Ex posed node problem

Fig. 3. The illustration of hidden node and exposed node problem. The symbols Mo Po No Ko represent wireless mobile nodes.

Fig. 4. Representation of CSMA/CA principle: the arrows represents sending and receiving actions. The symbols Ao Eo Co Do Eo Fo Go Ho represent wireless mobile nodes.

B. Mobility model In the BML model, there are two kinds of vehicles, eastbound and northbound, distributed in a two dimensional

square lattice which models the network of streets [13]. The system dynamics is controlled by the traffic light which is divided into two phases:red and green. On each site of the lattice, there can be a car moving from south to north, or a car moving from east to west, or no car at all. In each odd time step, care moving from south to north will attempt a move; in the same way, in each even time step, cars moving from east to west have a chance, to move forward a site. The consequence of this rule corresponds to that of controlling the traffic at the crossings with traffic lights. However, if the forward site has been occupied by another car, then the car should wait on the original site and could not move forward. The state of system is randomly determined at the beginning of the simulation. The model is defined on a square 2 lattice of L sites with periodic boundary conditions. Due to the periodic boundary conditions the total number of arrows of each type is conserved. Moreover, the total number of up arrows in each column and the total number of right arrows in each row are conserved. In addition, the global density is defined by P = ( Nv Number of vehicles).

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Fig. 5.

The illustration of vehicles mobility scenarios in the square lattice.

T hroug hput

=

2:= Number of all packets received by

BS

T

(1)

BS : The base station. T : The simulation time. Mean

velocity :Mean velocity simulation time, is defined as :

of vehicles during the

1 T Nam'J Velocity = T L j=l NsmJ

(2)

N amj

: The number of attempted moves of vehicles in the square lattice. Nsmj : The number of successful moves of vehicles. III.

SIMULATION RESULTS

A. Average velocity versus density

In Fig. 6, we plot the average velocity < v > of the cars as a function of the cars density. We observe that < v > remains almost equal to one at low densities; indicating the existence of the free flow state where all cars move freely. We say that the system is in the moving state. As exceeds some value Pel (Pel � 0.13) the mean velocity decreases smoothly with density until it reaches some critical value Pc2 (Pc2 � 0.35). This means that the system comes into the saturated state. As exceeds Pc2 value the mean velocity decreases rapidly with density until it reaches some critical value Pc3 (Pc3 � 0.48). This means that the system comes into the congested state. Beyond Pc3, < V > is usually equal to zero. We say that the system comes into the global deadlock state. Average velocity versus density 1,2

C. Simulation Parameters

Moving state

The simulation parameters which have been considered in this work are given in Table I below.

Satureted state

Congested state

Deadlock state

1

0,8

Parameter

SIMULATION PARAMETERS Value used

II

Propagation model

The free space propagation model

Queue Model

queue with packets forwarding priority

Simulation time The lattice size Moore neighborhood radius

3000

(8)

2

64 packets

Transport protocol

UDP

Routing Protocol

AODV

Packet size

0,6

0,4

0,2

15xl5

Queue size

Packet generator interval

V

TABLE I.

I

(8)

0 0,04

0,08

0,13

ρc1

Fig. 6.

0,17

0,22

0,26

0,31

0,35

Density

ρc2

0,4

0,44

0,48

0,53

0,57

ρc3

Average velocity versus density.

125 bytes

Initial velocity for nodes

I (iattice site)/8

.\

[0.1]

D. indicators of system performance Average throughput at the base station: Total received packets by the base station during the simulation time, is defined as :

B. Average throughput at the base station versus density In Fig. 7, we show the performance analysis of the achieved average throughput at the base station versus the density of vehicles. The result shows that there are four phases variation of the average throughput. In the moving state, all the cars move freely with respect to traffic signals at crossing. Thus, in this regime, the throughput

increases with the node density, reaches a maximum and then decreases. Indeed, the increase of vehicle density is responsible for increasing the access routes establishment through the neighboring nodes towards the destination. Furthermore, the decrease of the throughput is due principally to the perfor­ mance of CSMAICA scheme, which ensures that the closed cars do not emit simultaneously. Hence, the presence of several cars at the same local region limits the use of the wireless channel. In the saturated state, the throughput decreases slightly with increasing the density. This decrease of the throughput is mainly due to the appearance of some physical phenomenon. That is the instability of routes between the source vehicle and the base station due to increasing routes breaking as a result of increasing of route lengths. Surprisingly enough, we found that at the congested state the throughput undergoes a second increase. We can explain that by the following: as the density becomes high, the velocity becomes very low, routes become more stable, resulting to strengthen access routes to the destination. In the deadlock state, with increasing the density, the deadlock spreads to the system and causes global deadlock, in which no vehicle can move. Thus, the throughput continues to grow but at a low rate until it stabilizes when the global deadlock occur. Average throughput versus density λ=0,05 λ=0,1 λ=0,25 λ=0,5

Packet loss ( bits/s )

600

500

400

300

200

100

0 0

0,04

0,08

0,13

0,17

0,22

0,26

0,31

0,35

0,4

0,44

0,48

0,53

0,57

Density Fig. 7.

Average throughput at the base station versus density.

IV.

CONCLUSION

In this paper, we provide a framework for the study of vehicular connectivity in traffic city, and we show the use of cellular automata for that purpose. We provide an extensive set of simulations that reveals the impact of traffic states and vehicular transportation parameters, such as vehicles density, traffic lights on the exchange of communication data towards the base station. The simulation results show that the average throughput of cars behaves differently depending on the system state. Our results show that the throughput increases with cars density in the moving phase and begin to decrease in the saturated state. Surprisingly, our numerical results show a quite a non-trivial behavior in the congested state and the deadlock state. REFERENCES [I]

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