Performance Analysis of V2V Dynamic Anchor Position-based Routing Protocols

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Wireless NetworksThe Journal of Mobile Communication,Computation and Information ISSN 1022-0038 Wireless NetwDOI 10.1007/s11276-014-0825-8

Performance analysis of V2V dynamicanchor position-based routing protocols

Mohammad Reza Jabbarpour, AlirezaMarefat, Ali Jalooli, Rafidah Md Noor,Rashid Hafeez Khokhar & Jaime Lloret

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Performance analysis of V2V dynamic anchor position-basedrouting protocols

Mohammad Reza Jabbarpour • Alireza Marefat •

Ali Jalooli • Rafidah Md Noor • Rashid Hafeez Khokhar •

Jaime Lloret

� Springer Science+Business Media New York 2014

Abstract Recently, vehicular ad hoc networks (VA-

NETs) have received more attention in both academic and

industry settings. One of the challenging issues in this

domain is routing protocols. VANETs’ unique character-

istics such as high mobility with the constraint of road

topology, fast network topology changes, frequently dis-

connected networks, and time-sensitive data exchange

makes it difficult to design an efficient routing protocol for

routing data in vehicle-to-vehicle (V2V) and vehicle-to-

infrastructure communications. Designing routing proto-

cols for V2V commutations are more challenging due to

the absence of infrastructure nodes in the communication

procedure. They become even more challenging, when

they get benefit from dynamic anchor computation method

in which the anchor nodes (junctions or basic nodes for

routing) are dynamic in their routing procedure. Position-

based routing protocols have been proven to be superior

and outperform the other protocols since there is no

requirement to establish and save a route between source

and destination during the routing process which is suitable

for dynamic nature of vehicular networks. In this paper, the

performance of V2V dynamic anchor position-based rout-

ing protocols, which are proposed for the most challenging

condition of packet routing in VANET, are investigated

and evaluated under two different scenarios (i.e. various

vehicle densities and velocities) through NS-2. The

obtained results are then illustrated based on average delay,

packet delivery ratio and routing overhead as routing per-

formance indicators. Our objective is to provide a quanti-

tative assessment of the applicability of these protocols in

different vehicular scenarios. The comparison provided in

this paper makes the research contribution of this survey

paper quite higher than a regular survey paper only with

explanations.

Keywords Vehicular ad hoc networks � Position-based

routing � Anchor-based � Vehicle-to-vehicle

1 Introduction

Informing drivers about abnormal and hazard conditions on

the road such as accidents, work zones, weather conditions,

special events (e.g. sports), and emergencies (e.g. bushfire,

flood), is crucial to safety and regulate the vehicle traffic

flow. Having real-time and accurate information related to

traffic mobility (e.g. vehicle density, direction, accident and

velocity as well as weather condition) is essential for

obtaining this goal. VANETs can gather and provide this

information using vehicle-to-vehicle (V2V) and vehicle-to-

infrastructure (V2I) communication technologies [1–3].

M. R. Jabbarpour (&) � A. Marefat � A. Jalooli � R. M. Noor

Faculty of Computer Science and Information Technology,

University of Malaya, 50603 Kuala Lumpur, Malaysia

e-mail: [email protected]

A. Marefat


A. Jalooli


R. M. Noor


R. H. Khokhar

School of Computing and Mathematics, Charles Sturt

University, Wagga Wagga, NSW 2678, Australia


J. Lloret

Department of Communications, Polytechnic University

of Valencia, Camino Vera s/n, 46022 Valencia, Spain


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DOI 10.1007/s11276-014-0825-8

Author's personal copy

Vehicular ad hoc networks (VANET) is a subclass of

mobile ad hoc network (MANET) that integrates the

potentials of new generation wireless networks into vehicle

[4–7]. These kinds of networks have attracted automobile

industry and researchers due to rapid increase in number of

vehicles on the roads. The use of VANETs can increase the

drivers’ and passengers’ safety and comfort. VANET

devices (e.g. on-board unit) enable vehicles to send/receive

messages between each other via radio propagation. By

delivering these messages on time, drivers can make better

decisions and avoid mishaps before any incident. Proper

message delivery needs appropriate routing mechanism.

Although VANET is similar to MANET in terms of self-

organization, low bandwidth and unreliable communica-

tion media, it has its own unique characteristics such as

high mobility with the constraint of road topology, fast

network topology changes, abundant energy and storage

source, frequently disconnected networks, and time-sensi-

tive data exchange [8–11]. These characteristics have

introduced several research challenges to the design of a

proper routing protocol for VANETs.

For decades, several routing protocols have been pro-

posed for vehicular networks that we addressed and clas-

sified them in Sect. 2. Among them, position based

protocols have been proven to be superior and outperform

the other protocols since there is no requirement to estab-

lish and save a route between source and destination during

the routing process which is suitable for dynamic nature of

vehicular networks [12, 13]. In addition, they improve

scalability and control message overhead as compared with

others routing protocols [14]. Moreover, the performance

of various routing protocols (such as AODV [15] and DSR

[16]) is compared against the position-based routing pro-

tocols in a number of studies such as [17, 18], which

confirm that position-based routing protocols are the most

promising routing paradigm for VANETs.

Vehicles velocity, position, direction, density and dis-

tance are the main factors that have predominant role in

VANET routing protocols and finding the next-hop nodes

for forwarding the packets [19]. Various combinations of

these factors are considered in a number of studies in order

to propose suitable position-based routing protocols for

vehicular networks. Position-based routing protocols are

classified into two categories, namely, V2V and V2I.

Navigation system contains pre-loaded digital maps

through which street level navigation information can be

extracted. By the aid of navigation systems, different

junctions or anchors are identified by position-based rout-

ing protocols and are used to apply different routing

algorithm [4, 20]. Position-based routing protocols for V2V

commutations are more challenging than the protocols that

proposed for V2I communications due to the absence of

infrastructure nodes in the communication procedure [21].

They even become more challenging if they use dynamic

anchor computation method in which the anchor nodes are

dynamic. Therefore, the main contribution of this paper is

analyzing and evaluating various position-based routing

protocols that are proposed for V2V communication mode

and utilize dynamic anchor computation method. These

algorithms are evaluated based on different contributing

factors such as average delay packet delivery ratio, and

routing overhead.

Moreover, we present a deep survey on position based

routing protocols for vehicular environments. Although

some other researchers such as [22–26] that attempted to

give an overview over VANET position based routing

protocols, our overview is different from them due to the

following reasons: (1) A new taxonomy is proposed for this

type of protocols. (2) A specific group of position-based

routing protocols (i.e. V2V Dynamic Anchor Protocols) are

compared with each other instead of comparing all posi-

tion-based routing protocols. (3) Each of these protocols is

discussed in more technical details considering their rout-

ing strategy. (4) These protocols are evaluated based on

different contributing factors such as average delay packet

delivery ratio, and routing overhead. (5) Simulation envi-

ronment with real road map is used for evaluation purpose

in order to increase simulation results’ validity and reality.

The rest of this paper is organized as follows. An

overview of VANET routing protocols is presented in

Sect. 2. Section 3 discusses our proposed taxonomy for

position-based VANET routing protocols considering V2V

and V2I communication modes. In addition, V2V position-

based routing protocols that utilize dynamic anchor com-

putation method in their packet forwarding procedure are

represented in more details. Section 4 explains the simu-

lation setup, scenarios, evaluation metrics as well as an

illustration of the acquired results. Finally, Sect. 5 con-

cludes the paper.

2 VANET routing protocols

In this section, the VANET routing protocols are classified

into six categories named topology-based (ad hoc) routing,

position based routing, cluster based routing, geo-cast

based routing, multicast-based routing and broadcast based

routing on the basis of the strategies used to route the

packet from source to destination. The following context

briefly discusses each category.

2.1 Topology-based protocols

This type of protocols find the links information between

nodes and place them in neighboring table for further

sending of data. Topology based protocols can be further

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classified into proactive, reactive and hybrid protocols.

Proactive routing also known as table-driven primarily

relies of the shortest path algorithm. Although this class of

topology-based routing do not have discovery delay, but it

suffers from high overhead due to its periodic topology

updates. Destination-sequenced distance vector (DSDV)

[27], Global state routing protocol (GSPR) [28], Fisheye

state routing (FSR) [29] and Optimized link state routing

(OLSR) [30] are different kinds of proactive routing.

Reactive protocols also known as on-demand protocols

employ a flooding method for route discovery. However,

this method causes routing overhead and makes this class

of routing inappropriate for security applications in VA-

NET [31]. Temporally ordered routing algorithm (TORA)

[32], Ad hoc on-demand distance vector (AODV), Pre-

diction-based AODV (PRAODV) [33], Dynamic source

routing (DSR) and preferred group broadcasting

(AODV ? PGB) [34] are some examples of this class of

routing. The last class of topology-based routing, as its

name suggests, merges reactive and proactive routing

protocols to reduce network overhead. However, the hybrid

routing protocols are unsuitable for conditions with the

highly dynamic nodes such as VANET. Different kind of

hybrid routing protocols include Zone routing protocol

(ZRP) [35] and Hybrid ad hoc routing protocol (HARP)

[36].

2.2 Position-based protocols

This type of protocols utilize geographic positioning

information in order to find the successive forwarding

nodes, thus, there is no requirement to establish and keep a

route between source and destination as all nodes in these

routing protocols are aware of locations of their own and

their neighbors [37]. This study deeply concentrates on

position-based routing protocols by dividing them into two

chief categories under V2V and V2I. Therefore, in sub-

sequent section we propose taxonomy for position-based

routing protocols and discuss on each of them in extensive

details.

2.3 Cluster-based protocols

In this type of protocols, the vehicles which are in vicinity

of each other try to create cluster of nodes and choose on

vehicle as the cluster header which has the responsibility to

manage the cluster by using the intra/inter cluster com-

munications. Scalability is the advantage of cluster based

protocols, while, high overhead and delay are their disad-

vantages in high mobility networks like VANETs. Cluster-

based routing (CBR) [38], Cluster-based directional rout-

ing protocol (CBDRP) [39–42], Location routing algorithm

with cluster-based flooding (LORA-CBF) [43], Clustering

for open IVC network (COIN) [43], Traffic infrastructure-

based cluster routing protocol with handoff (TIBCRPH)

[44], are some examples of this class.

2.4 Geocast-based protocols

This type of protocols sends packets to a group of vehicles in a

specific geographical area called zone of relevance (ZOR) [45]

thus nodes that are located outside the ZOR are not received

any packet [46]. This type of routing protocols is position-

based multicast routing employed to send packet to all the

nodes in a stationary topological area. Identification of the

forwarding zone leads to reduction in message overhead and

network congestion. However, the disadvantage of this kind of

routing protocols is separation of network and the presence of

undesirable neighbors that may discard the forwarding packets

[47]. Different kinds of geocast-based routing protocols are

Inter-vehicle geocast (IVG) [48], Cached geocast routing

(CGR) [49], Abiding geocast routing (AGR) [50], Robust

vehicular routing (ROVER) [51] and Mobicast [52].

2.5 Multicast-based routing protocols

This type of protocols intends to forward packet from a

single source to multiple destinations in a particular area.

The transmission process is generally carried out through

geocast routing. Traditional multicast protocols are not

suitable for VANET environment as they were merely

designed for wired networks [53]. Therefore, multicast

protocols should be enhanced in a way that can support

high node mobility and frequent topological changes which

is required for networks such as VANET. In VANET,

vehicles can perform complex computational tasks as they

do not require maintaining power as vehicles offer con-

siderable power supply for long period. Various multicast

protocols are proposed for MANET that the majority of

them are also suitable for VANETs [54, 55]. Multicast ad

hoc on-demand distance vector (MAODV) [56], Adaptive

demand-driven multicast routing (ADMR) [57], Multicast

with ant colony optimization for VANETs based on

MAODV (MAV-AODV) [58], The multicast optimized

link state routing (MOLSR) [59], On-demand multicast

routing protocol (ODMRP) [60] and Destination driven on

demand multicast routing protocol (D-ODMRP) [61], are

the most common kinds of multicast routing protocols.

2.6 Broadcast-based protocols

This type of protocols uses flooding of the packet on the

network area in which each node forwards the received

message to other nodes. Broadcast-based protocols are

frequently used in VANETs for different purposes such as

data or traffic information sharing, advertisements and

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emergency news. The chief advantage of using this kind of

protocols is the assurance of delivering the messages to all

destinations. The broadcast-based protocols are mostly

proper for the network with small number of nodes in the

network as flooding method resulted high overhead cost.

Flooding in a network with great number of nodes requires

more message broadcast which causes collisions and higher

bandwidth utilization. BROADCOMM, Distributed vehic-

ular broadcast (DV-CAST) [62], Edge-aware epidemic

protocol (EAEP) [63], Hybrid data dissemination (HyDi)

[64] and Density-aware reliable broadcasting (DECA) [65],

are some example of broadcast-based routing protocols.

3 Position-based VANET routing protocols

This paper classifies the position-based routing protocols

based on vehicular communications into two main cate-

gories namely V2V and V2I as depicted in Fig. 1. Each

mentioned communication mode comprises the numerous

routing protocols. Further, V2V communication position-

based routing protocols are categorized into three types for

computing the anchor path, consisting dynamic anchor,

static anchor and no anchor computations. Navigation

system contains pre-loaded digital maps through which

street level navigation information can be extracted. By the

aid of navigation systems, different junctions or anchors

are identified by position-based routing protocols and are

used to apply different routing algorithm. In anchor-based

routing, the source node includes into each packet a route

vector composed of a list of anchors or fixed geographic

points, through which packets must pass.

On the other hand, V2I communication for position-

based routing protocols divided into two types of dynamic

and static Road Side Units (RSUs). This means that RSUs

are either dynamic or fix in terms of mobility. Every single

routing protocol performs routing action through a specific

forwarding strategy. Accordingly, variety of forwarding

strategies including greedy forwarding, improved, direc-

tional and predictive directional greedy forwarding strate-

gies are considered for each position-based routing

protocol in our proposed taxonomy in Fig. 1 and are dis-

cussed as follows:

• Greedy forwarding: In this strategy, source node, S,

always selects the closest node in its communication

range to the destination, D, as next-hop node.

RoutingProtocols

Forwarding Strategy

Position-basedRouting

Communication

No anchor computation

Static anchor computation

Dynamic RSUs

Static RSUs

V2V

V2I

GyTARAMAREBGRB-MFRMOPR

GPSR

DGR

PDGR

Dynamic anchor computation

GSR

A-STAR

MGRP

MIBR

SADV

IAGR

Improved Greedy forwardingDirectional greedy forwardingDirectional greedy forwarding

Greedy forwardingPredictive directional greedy

Forwarding

Greedy forwarding

Directional greedy forwardingPredictive directional greedy

forwarding

Greedy forwarding

Greedy forwarding

Greedy forwarding to mobile gateway

Greedy forwarding to buses

Directional greedy forwarding &Greedy forwarding

Greedy forwarding to RSUs

Fig. 1 Taxonomy of position based routing protocols

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• Improved greedy forwarding: In this strategy, source

node, S, initially searches its neighbouring table and

estimates the future position of all nodes in its

neighbouring table according to their velocity and

direction. Afterwards, it selects the closest node in its

communication range to the destination, D, as next-hop

node.

• Directional greedy forwarding: In this strategy, source

node, S, always selects closest node in its communi-

cation range which is moving towards the destination,

D, as next-hop node. In other words, direction is the

only factor that is considered in this strategy.

• Predictive directional greedy forwarding: In this strat-

egy, source node, S, considers the information of its

two-hop neighbours. Then, it searches its neighbouring

table and estimates the future position of all its

neighbours (one-hop and two-hop) according to their

velocity and direction. Afterwards, it selects the closest

node in its communication range which is moving

towards the destination, D, as next-hop node.

As mentioned earlier, our main concentrate is on posi-

tion based routing protocols that are proposed for V2V

communication mode and utilizes dynamic anchor com-

putation method. Each of these protocols is discussed in

more details in the next subsection, while, the other men-

tioned routing protocols in Fig. 1 are discussed in the fol-

lowing paragraphs.

Greedy perimeter stateless routing (GPSR) [28] is a

well-known position based routing protocol for highways

where the nodes are uniformly distributed. In GPSR each

node maintains the knowledge of its hop neighbours. The

routing approach of GPSR depends on greedy and perim-

eter modes [13]. Greedy mode is used when the sender

node finds a node in its routing table which is closer to the

destination than the sender node, while, perimeter mode,

also known as recovery mode, is utilized when there is not

any neighbouring node in the routing table which is closer

to destination node. This condition is known as local

maximum problem. GPSR is not desire for urban envi-

ronment due to the lack of direct communication between

nodes as well as using recovery mode which routes the

packets through the extended path instead of shortest path,

which leads more delays [66, 67].

Geographic source routing (GSR) [68] combines the

position-based routing and topological information. To

achieve information regarding the destination nodes’

location, GSR uses the reactive location service (RLS)

[69]. Basically, the querying node floods the network with

a position demand for a particular node identifier. Once the

node obtains the position demand, it replies the position to

the querying node. This protocol employs digital maps

which enables the source to be aware of all the links to the

destination. GSR utilizes the Dijkstra shortest path algo-

rithm [70] to calculate the shortest path from the source to

destination. Simulation results showed that the packet

delivery ratio is improved in GSR in comparison with

AODV and DSR [18]. However, since this routing protocol

uses hello beacons as a control message, it suffers from

high routing overhead.

Anchor-based street and traffic aware routing (A-STAR)

[71] is exclusively designed for inter-vehicle communica-

tion for an urban environment. A-STAR uses street map to

measure the series of junctions through which a message

pass to arrive at the destination. As a position based routing

protocol, A-STAR employs static or dynamic rated maps to

determine an anchor route with high packet delivery con-

nectivity ratio. In other words, Using statistically rated

maps by considering the quantity of city bus paths on every

street to determine the anchor paths for providing optimum

connectivity, or using dynamically rated maps derived

from dynamically analyzing the state of the latest traffic to

recognize the best anchor routs [13]. A-STAR provides a

new recovery technique which computes a new anchor path

from the local maximum through where the packets are

routed. Consequently, A-STAR routing protocol over-

comes the drawbacks of GPSR and GSR by applying traffic

awareness and mentioned recovery technique, it finally

becomes time consuming as the routing path might not be

optimal since its existence along with the anchor path.

Directional greedy routing (DGR) [72] intends to min-

imize end-to-end delay by reducing the number of hops

packet passing to reaches its destination. DGR uses posi-

tion first forwarding and direction first forwarding as its

forwarding strategies. The first strategy attempts to find the

closet node towards destination as the next hop. However,

using singly this strategy results routing loop as it is not

able to recognize the direction of nodes movement. For

instance, node A intends to forward a packet to destination

which is along its moving direction while node B is moving

in the opposite direction. Node A forwards its packet to

node B regardless of its direction. Node B then forwards

the received packet to node A as it is found A as the

suitable next hope. The second forwarding strategy how-

ever selects the nodes moving toward destination. Among

those nodes, the one closet to the destination is chosen as

the next hope. This strategy attempts to reduce the routing

loop in the forwarding process. Therefore, DGR takes both

position and direction into consideration when choosing

next hope.

Prediction direction greedy routing (PDGR) [72]

enhances DGR with prediction and proposes a predictive

approach. Although a further prediction by considering the

packet carrier’s possible future neighbours can make

routing more efficient, DGR merely considers the packet

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carrier’s current neighbour. In DGR, next hop is selected

by calculating a value called weighted score which is

performed for the packet carrier and its current neighbours.

In PDGR the calculation is performed not only for the

packet carrier and its current neighbours but also for its

possible future neighbours. Beacon messages are used to

achieve the knowledge of possible future neighbours as the

packet carriers require the information of its two-hop

neighbours.

Static node-assisted adaptive (SADV) [73] is proposed

to enhance the data delivery performance in sparse vehic-

ular networks by taking advantage of deployed static

infrastructures (e.g. RSUs) at intersections. Packets are

forwarded to these static nodes when there is no vehicle

available to deliver the packets through the optimal path.

The packets are stored in static nodes until the optimal

delivery path becomes available. It is worth noting that

each vehicle is aware of its location via GPS and each

static node has access to its surrounding digital map (i.e.

roads and streets). SADV encompasses three parts, namely

Static Node Assisted Routing (SNAR), Link Delay Update

(LDU), and Multi-Path Data Dissemination (MPDD). Road

maps are converted to graphs where the intersections and

the streets play the role of nodes and edges of the graph,

respectively. SNAR utilizes optimal paths, which are found

from the extracted graphs, for packet forwarding. The

measured delay of packet delivery between static infra-

structures is stored in LDU to update the delay matrix.

Finally, multi-path routing mechanism is performed by

MPDD. The delay caused by static infrastructures is the

main drawback of this protocol.

Infrastructure assisted geo-routing (IAGR) [74] is

another position based routing protocol which uses static

and fixed nodes at intersections to enhance the operation,

performance and range of multi-hop geo-routing protocols.

IAGR uses GSR to investigate the potential advantage of

V2I over V2V communication. Similar to SADV, GPS is

used to find the vehicles’ location and each static node has

access to its surrounding digital map. A weight will be

assigned to each edge of the extracted graph from the road

map based on the distance between its two nodes. This

distance among static infrastructure units should be omitted

from routing procedure since these units are connected

through reliable and high bandwidth wired networks. In

order to achieve this issue in IAGR, all nodes on the graph

that represent static infrastructure can be considered as

only one node referred as backbone gate [75]. As a result,

vehicles consider all static infrastructures as one node and

compute the optimal paths for packet forwarding by using

this unique characteristic.

Mobile infrastructure based VANET routing protocol

(MIBR) is a new position-based reactive routing protocol

which is suitable for urban VANETs which buses play an

essential rule during route selection and data transfer.

Therefore, MIBR estimates every road segments density,

based on the bus line information to select road segment

[76]. MIBR assumes every bus as the forwarding node to

ordinary nodes. To do so, all buses are equipped with two

wireless interfaces, and a single interface comprises other

vehicles. Throughout routing, the protocol approximates

next road segment and hop counts, then stores the infor-

mation in a routing table [13, 77, 78]. Whilst development

of such a routing protocol both the transmission quality of

each road segment and different transmission abilities of

numerous vehicles are considered in the algorithm of

MIBR [76]. In addition, this routing protocol contains two

main parts. Feature of street awareness is the first part that

leads to select the most efficient path through a sequence of

road segments with the best estimated transmission quality.

The second part is optimal forwarding of the packets hop

by hop through every road segment. The only drawback

here refers to the connectivity which is limited by their

scheduling time and the area covered by the bus routes.

Mobile gateway routing protocol (MGRP) assumes the

presence of a GPS and a digital map similar to other

position based routing protocols. In MGRP, each vehicle

builds its neighbor table which includes neighboring

vehicles, headings, and speeds that would have essential

contribution in the task of routing. Similar to MIBR routing

protocol which utilized buses as a mobile gateway, MGRP

also is based on the concept of mobile gateways but it uses

vehicles as mobile gateways (like taxis). Mobile gateways

substituted of fixed RSUs to guarantee larger connectivity

coverage. In this particular, mobile gateways consist of two

interfaces; IEEE 802.11 for V2 V and 3G for V2I com-

munications. The IEEE 802.11 is utilizing while V2V

communications like communicating with neighboring

vehicles which are missing 3G interface or are not mobile

gateways. In addition, whilst mobile gateways received the

packets from IEEE 802.11 interface, they forward the

packet to the base station via 3G and base station forwards

the packets to the gateway controller in turn [13]. Conse-

quently, Pan et al. in [79] believe that MGRP increase the

packet delivery ratio as well as decrease the average hop

count by exploit both inter-vehicle-based and infrastruc-

ture-based communication to route packets.

4 Overview of V2V dynamic anchor position-based

routing protocols

In this section, we discuss the routing procedure of V2V

dynamic anchor position-based routing protocols, namely

Movement prediction-based routing (MOPR), Improved

Greedy Traffic Aware Routing (GyTAR), Border-node

based Most Forward within Radius (B-MFR), Edge node

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Based Greedy Routing (EBGR) and Adaptive Movement

Aware Routing (AMAR) in more details. At the end of this

section, we compare all the V2V dynamic anchor position-

based routing protocols.

4.1 Movement prediction-based routing (MOPR)

Famous position-based routing protocols such as Greedy

perimeter stateless routing (GPSR) [28] and Movement-

based Routing Algorithm (MORA) [80], benefit from a

vehicle current position along with its movement direction

in order to select a next hope for forwarding data. Although

these contributing factors are necessary for a robust posi-

tion-based routing process, they are not enough. Menouar

et al. [81–83] believe that a vehicle speed also play a

dominant role in the process of selecting a vehicle as the

next hope for sending data. The so-called Movement Pre-

diction-based Routing (MOPR) concept has been proposed

by them in order to improve the prediction strength of

existing position-based routing protocols [84]. They stip-

ulated that a vehicle velocity can be used for a precise

estimate of link stability. In other words, MOPR brings a

vehicle speed, position, and movement direction into

account to estimate the probability of link failure during a

communication session. Thus, it can be guaranteed that a

vehicle, which will be out of the effective communication

range, is not selected as the next hope for sending/for-

warding data. Moreover, the retransmission overhead in

each node of the network will be reduced drastically. All in

one, MOPR is capable of improving the position-based

routing protocols by considering a vehicle speed as an

influencing factor on predicting the possible prospective

VANET topology.

Another issue to be addressed in the procedure of

MOPR is that employing MOPR will lead each vehicle to

appraise communication link stability before selecting next

hop for sending data among other vehicles nearby. Firstly,

the link lifetime will be indicated in connection to the total

estimation time. In other words, it will be estimated how

long it takes for two neighbour vehicles to get out of their

effective communication range. Then, by dividing the

estimated communication lifetime by the route validity

time, which is a constant, the link stability will be out as

the result.

In terms of formula, it can be assumed that two vehicles

are moving in two different directions from time t0 to time

t1 and with the speed of v1 and v2 respectively. D0 repre-

sents the distance between the vehicles in the time t0 with

their coordinate of A0 (xi0, yi0) and B0 (xj0, yj0), while D1

denotes the distance in the time t1 with the vehicles’

position of A1 (xi1, yi1) and B1 (xj1, yj1). The lifetime of the

communication link (LifeTime [i,j]) is consistent with

estimated time Dt = t1 - t0 and explains how long it takes

for D1 to be equal or greater than communication range.

Equation 1 is used to forecast the future position of each

vehicle (i.e. A1 and B1) from its current position (i.e. A0 and

B0) and speed (i.e. v1 and v2).

Bx1 ¼ Bx0 þ Vbx:Dt

By1 ¼ By0 þ Vby:Dt

Ax1 ¼ Ax0 þ Vax:Dt

Ay1 ¼ Ay0 þ Vay:Dt

8>><

>>:

9>>=

>>;

ð1Þ

By having the position of each vehicle, it is possible to

calculate the future distance between them, D1, via Eq. 2:

D21 ¼ Ax1 � Bx1j j2þ Ay1 � By1j j2 ð2Þ

By combining Eqs. 1 and 2, we have Eq. 3 as follows:

Figure 2 illustrates a schematic feature which can be

used for communication lifetime prediction for two

neighbor vehicles.

4.2 Improved greedy traffic aware routing (GyTAR)

GSR [15] and A-STAR [85] are two well-known proposed

greedy position-based routing protocols for VANETs.

i

j

j

i

Y t0 t1 time

Yj1

VyjYj0

Yi1

VyiYi0

X

Y Xi0 Vxi Xj0 Vxj Xj0 Xj0

X

D1

Vi

Vj

D0

Fig. 2 Link lifetime estimation

D1 ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

j Ax0 � Bx0Þ þ ðVax � Vbxð ÞDtj2 þ j Ay0 � By0Þ þ ðVay � Vby

� �Dtj2

q

ð3Þ

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Different ways are used to improve the routing issue in

vehicular networks by these approaches including simple

greedy forwarding, amalgamating the topology-based proto-

cols with position-based protocols and city bus route infor-

mation. However, vehicle density, speed and direction are

three main criteria which play an important role in data rout-

ing, they are being neglected by above mentioned protocols.

Therefore, Jerbi et al. in [81] offered a new vehicular ad hoc

routing protocol, so-called Improved Greedy Traffic Aware

Routing (GyTAR), based on vehicle density and distance from

destination node. GyTAR includes two phases: (1) Junction

selection and (2) Sending data between junctions.

In the first phase, GyTAR uses anchor based routing

algorithm like other position based protocols for data for-

warding. Dynamic junction selection instead of a static one

is the main difference between GyTAR and other proto-

cols. In dynamic junction selection, intermediate junctions

between source and destination are selected one by one

based on vehicles’ density and distance to destination.

While in static one, all of the intermediate junctions are

selected in the source node regardless of vehicle density in

the future. Jerbi et al. used following notations in Table 1

for finding the junctions’ score in multi junction condition.

Therefore, score of each junction (Sj) is calculated by

using Eq. 4:

Sj ¼ a � 1 � Dp

� �þ b � Nv½ � ð4Þ

Figure 3 illustrates junction selection phase based on

vehicle direction and density at real environment (e.g.

road, street). In the second phase, an improved greedy

algorithm is used for data forwarding among desired

junctions. Each vehicle broadcasts beacon messages to its

neighbors, periodically. Beacons include the information

such as vehicle’s speed, direction and position. Therefore,

each vehicle creates its own neighboring table based on

this information. Thus, in this protocol, the sender/for-

warder vehicle can predict the co-vehicles topology and

position for the next Dt seconds, then select the next

nearest hop to destination, similar to MOPR protocol. If

Table Used notation in GyTAR protocol

Variable Definition

J The subsequent nomination junction

I The current junction

Dj The distance between nomination junction J and

destination

Di The distance between current junction and destination

Dp = Dj/

Di

It represents the closeness of the nomination junction to

the destination

Nv Total number of nodes (vehicles) between two junction

I and J

a, b Utilized to score criteria for distance and vehicle density,

respectively

J3

J2

J1

Highly dense Moderately denseJunction Lightly dense

Current junction

Destination vehicle

Fig. 3 Junction selection phase

in GyTAR

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the sender be the nearest vehicle to the next junction (local

optimum problem), it should carry the data until it reaches

that junction.

4.3 Border-node based most forward within radius (B-

MFR)

Border-node based most forward within radius (B-MFR)

[86] is a position-based routing protocol that enhances

Most Forward within Radius (MFR) [87] in terms of end-

to-end delay. B-MFR employs the concepts of boarder-

node of sender’s communication range to reduce the

number of hops between sender and receiver. In B-MFR,

nodes are categorized into interior, border and outer nodes.

Figure 4 depicts this layout. Nodes in B-MFR achieve

information from their neighbours via the periodic beacon

messages that are exchanged. All nodes that are located in

the transmission range of the source called one-hope

neighbour. Once the one-hop neighbours are recognized by

the source, it chooses the next hop for forwarding its

message to convey to the destination. Source node chooses

the border node as the next hop since it is the only node

that is closest to the destination and also furthest node to

the source. Figure 4 shows how source node selects the

border node by projecting all border nodes on the direct

line which connects the source and destination. The algo-

rithm of this process is also shown in Fig. 5. In this case, S

attempts to send a message to D. Border A and B are

projected on the line segment SD which links source and

destination. By observing the projection, A is selected as

the border node as it is obviously closer to the destination

and forwards its packet to A. Likewise, by observing the

projection of E and F, node A chooses node E as its border

node and forwards the received packet to it. This greedy

method carries on until the receiver node is in the trans-

mission range of the current sender. As it is shown in the

same figure, node D is now in the transmission of node E

thus node E conveys the packet to node D.

The B-MFR protocol suffers from nodes conflict where

projected nodes have the same distance from the source to

destination. This brings the chief drawback of this routing

protocol which is in decision making of the next for-

warding node.

4.4 Edge node based greedy routing (EBGR)

Edge node based greedy routing (EBGR) [88], is a position

based routing protocol which employs greedy forwarding

as routing strategy. EBGR algorithm provided both unicast

and broadcast forwarding strategies in a mobile ad hoc

network [89]. In situations for forwarding message from a

single node to another it uses unicast, while it broadcasts

the packet in case of sending message to all other nodes in

the network. The principle of proposing EBGR refers to

optimize the packet delivery in mobile ad hoc networks in

which increasing message delivery reliability in high

mobility scenarios. In EBGR, packets sent to the selected

edge node with consideration of the nodes that move

towards destination.

Fig. 4 Forwarding method in B-MFR

Fig. 5 Pseudo code of border node selection in B-MFR

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EBGR routing protocol follows three essential algo-

rithms key functions. In the following a detail description

prepared for Neighbor Node Identification (NNI), Node

Direction Identification (NDI) and Edge node Selection

(ENS) which is aforementioned algorithms that EBGR uses

in packet transmission procedure.

1. Neighbor node identification (NNI)

Neighbor node identifier algorithm proposed to iden-

tify any other vehicles within its transmission range as

a neighbor. Neighbors hold details of each other in a

neighbor set table and update it frequently owing to

high mobility. EBGR sends beacon message periodi-

cally neighbor node identification. These beacon

messages contain variety of information such as node

ID, node location, and timestamp.

In case of sending a message, source node informs

others in the transmission range be sending beacon

message every l seconds. Then each receiver node

updates the neighbor set table. In case of time out

occurrence after a * l seconds for a known node (A is

the number of beacon messages that a node is allowed

to miss) and, it will be removed from the neighbor set

table.

2. Node direction identification (NDI)

Node direction identification algorithm is responsible

to determine the node’s (vehicle’s) direction which is

moving towards destination. In this particular, poten-

tial score (PS) calculates to identify the direction of all

vehicles and present into the limited transmission

range (LTR). Accordingly, the appropriate edge node

with which contain largest PS considered as moving

towards the destination node (D) and that specific node

selected as next hop for forwarding packet to D. PS is

calculated through Eq. 5 and the notations are as

follows.

PSi Potential score of node i

q, x Two potential factors

Let q 1 x = 1 and q > xDi Shortest distance from edge node i to

destination D

Dc Shortest distance from packet forwarding

node c to destination D

Vi Vector for velocity of edge node i

li, d Vector for the location of the edge node i to

the location of destination node D

Cos(Vi, lI,d) Cosine value of angle made by these vectors

PSi ¼ q � 1� Di

Dc

� �

þ x � cosðVi; li;dÞ ð5Þ

3. Edge node Selection (ENS)

As it is clear from the name of this algorithm, it

chooses the edge node for packet forwarding which is a

node with shortest distance to the destination node D in

comparison with others within LTR. The edge node is

responsible to store received packets in forwarding

table and transfer while the nodes gain new neighbors.

4.5 Adaptive Movement Aware Routing (AMAR)

Adaptive Movement Aware Routing (AMAR) [90] is a

greedy position-based routing protocol which uses move-

ment aware greedy forwarding scheme to find the suitable

next-hop node in packet routing procedure. AMAR solve

the problem of B-MFR by taking advantage of more

information about the vehicle movement comprising the

position, speed and direction for assisting the source to find

the suitable node in its propagation range which is close to

the destination. AMAR obtains this information via GPS or

navigation system. It computes a weight score, Wi, for

neighbor node i considering three mentioned metrics (i.e.

position, speed and direction) as follows (Eq. 6):

Wi ¼ aPm þ bDm þ cSm ð6Þ

where Pm, Dm and Sm represent neighbor node’s (vehicle’s)

position, direction and speed, and a, b, c weight the

importance of these metrics with a ? b ? c = 1.

The procedure of considering position metric, Pm, in

finding the neighbor node is depicted in Fig. 6. The

neighbor nodes i and j are projected on the line segment SD

which connects source and destination nodes. The position

function that measures the progress of node i toward the

destination node is defined as Pr and calculated via Eq. 7.

Pr ¼D2

c þ d2 � d02

2Dc

ð7Þ

where Dc is the shortest distance between source and

destination nodes, d and d’ are the distances from inter-

mediate node i to source and destination, respectively.

Thus, position metric can be computed by Eq. 8.

Fig. 6 Impact of position metric in finding next-hop forwarding node

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Pm ¼Pr

Dc

ð8Þ

The direction metric, Dm, is utilized to find the optimal

direction of forwarding by selecting nodes moving towards

the destination node and is obtained by Eq. 9.

Dm ¼ cos2 h; if h\p2

0; otherwise

(

ð9Þ

where h is the angle between intermediate node movement

direction and the straight line connecting this node to the

destination node. Finally, the intermediate node with

higher speed is more favorable to be selected as next-hop

node. The speed metric for a node wit moving speed of Vi

is calculated by Eq. 10.

Sm ¼ Normðdiff ðViÞÞ ¼diff ðviÞ �Min

Max�Minð10Þ

where Min and Max represent the minimum and maximum

values of speed metric for all neighbor nodes of i, the

difference of speeds between node i and source node is

defined as diff and is computed via Eq. 11.

diff ðviÞ ¼ vi � vs ð11Þ

Norm function in Eq. 10 is used to normalized the speed

metric’s value (i.e. Sm 2 [0, 1]) similar to the other two

metrics (i.e. Pm and Dm).

A sorted list of next-hop candidate nodes is created

according to the calculated weight score, Wi, and the node

with the highest score is selected as the next-hop node for

packet forwarding. Although AMAR overcomes the prob-

lem of B-MFR, its next-hop selection procedure faces with

difficulty whenever the weight score of two nodes are equal.

5 Simulation and results

5.1 Simulation setup

A 2,000 m 9 2,000 m area is extracted from OpenStreet-

Map [91] to simulate different scenarios. The selected area

is shown in Fig. 7 while its statistical specifications are

demonstrated in Table 2. SUMO [92] is highly portable

microscopic traffic simulator which is selected to create the

vehicle traffic and movement patterns on the extracted

map. SUMO Netconvert tool is used to convert the map

into a net.xml which is the proper format for SUMO.

Furthermore, Krauß mobility model [93] is employed to

create the movements for the simulated vehicles in order to

take advantage of collision avoidance vehicular mobility

model. Using this model allows us to avoid collisions along

the road where vehicles traveling at the safest maximum

speed. In this paper, we do not measure the initial 60 s of

SUMO output in order to achieve more accurate and reli-

able results. We also take advantage of using TraNSLite

for converting the generated traffic scenario into a proper

format for NS-2.33.

Table 3 indicates the parameters used for network

operation. All the parameters for PHY and MAC are

Fig. 7 Layout of the study road map

Table Statistic specifications of road map

Specification Value

Dimension 2,000 m 9 2,000 m

Map area 4 km2

Streets/km2 240.25

Junction/km2 178.5

Avg. street length 105.3 m

Avg. lanes/street 1.8

Table Configuration parameters in the simulation

Parameter Value

Simulation time 450 s

Packet sending rate 5 s

Size of message 256–300 bytes

Vehicle speed 50–150 km/h

MAC/PHY IEEE 802.11p

Mobility model Krauß model

Max. transmission range 400 m

SBR distance threshold 250 m

Channel capacity 3 Mbps

Traffic model 15 CBR connections

Propagation model Nakagami

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configured as IEEE 802.11p spectrum. Since the lower data

rates offers superior resistance against interferences and

noises, we use data rate of 3 Mb/s for packet broadcasting

in terms of physical layer. Moreover, the street broadcast

reduction (SBR) scheme [94] is used to minimize the

broadcast storm problem in the simulations. Two types of

packets are defined in this study; one is the beacon packets

which are transmitted by all the vehicles in specific time

interval of five packets per second, while another, which is

warning packets, are merely transmitted by 10 % of the

vehicles in every 1 s. Focusing on warning packets for

evaluation of routing protocols allows us to have more

realistic results. In other words, beacon packets are con-

tinually sent in real world, thus considering these types of

packets for evaluating a routing protocol can merely

increase the inconsistency of the results. The simulation

results are averaged over 25 runs in order to obtain more

accurate results.

Table 4 compares aforementioned V2V dynamic anchor

position-based routing protocols based on different

characteristics.

5.2 Simulation scenarios

This paper evaluates dynamic position-based routing pro-

tocols performance in two different scenarios realizing

which algorithm has the most efficient performance in

terms of average delay, routing overhead ratio (ROR) and

packet delivery. Particularly, these parameters evaluates in

two conditions of various vehicles density and various

vehicle velocity to turn into a realistic urban environment

scenario. These two simulation scenarios are chosen to

work on due to their predominant role in VANETs routing

algorithms [19, 95].

1. Various vehicle density

The precision of acquired results influences by various

key factors principally vehicle density, speed and

roadmap topology. As a realistic urban environment

scenario, this paper considered a complex real road

map of Kuala Lumpur city. In this particular, an area of

2,000 m 9 2,000 m allocated as road map topology.

In various vehicle density analyses, the performance of

dynamic position-based routing protocols such as

GyTAR, B-MFR, EBGR, MOPR and AMAR were

evaluated in different vehicle densities of 100

(25 vehicles/km2), 150 (37.5 vehicles/km2), 200

(50 vehicles/km2), 250 (62.5 vehicles/km2) and 300

(75 vehicles/km2) which each assessed with the aver-

age vehicle speed of 80 km/h.

2. Various vehicle velocity

As mentioned above, the other essential factor which

affects the accuracy of obtained results is vehicle

speed. In this scenario, performance evaluation of

aforementioned routing protocol in a same size road

topology map of 2,000 m 9 2,000 m of Kuala Lumpur

real road map is considered with vehicles speed

variation. To do so, the evaluation has based on

various vehicle velocities of 50, 75, 100, 125 and

150 km/h with considering the average vehicle number

of 200 nodes.

5.3 Evaluation metrics and results

Packet Delivery Ratio (PDR), Delay (D) and Routing

overhead ratio (ROR) are three evaluation metrics which

are used to evaluate and compare the performance of the

discussed routing protocols in Sect. 4 (i.e. GyTAR, MOPR,

EGBR, B-MFR and AMAR).

• Packet delivery ratio (PDR): is the number of success-

fully received packets by destination vehicle over the

whole number of sent packets by source node (vehicle).

• Delay (D): is the average time that takes for a packet to

pass the intermediate nodes in the network from source

node until reaching the destination node.

• Routing overhead ratio (ROR): defined as the ratio of

number of packets correctly received at the destina-

tion, to the total number of the packets send in the

network using the routing protocol, during the simu-

lation time.

Table Comparison among V2 V dynamic anchor position-based routing protocols

Position-

based

routing

Forwarding strategy Recovery strategy Position

information

Mobility model Network

simulator

Simulation

scenario

MOPR Predictive directional greedy

forwarding

Right hand rule Packet forwarding Realistic mobility model NS-2 Urban

GyTAR Improved greedy forwarding Carry and forward Packet forwarding Realistic mobility model NS-2 Urban

B-MFR Greedy forwarding End node awareness Packet forwarding Realistic mobility model NS-2 Urban

EBGR Directional greedy forwarding End node awareness Packet forwarding Manhattan mobility model NCTUns 5.0 Urban

AMAR Directional greedy forwarding Movement awareness Packet forwarding Unknown NS-2 Urban

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These algorithms are compared based on two above

mentioned scenarios (i.e. various vehicle densities and

velocities). Detailed analysis of the simulation results are

given in the following considering pre-defined evaluation

metrics.

5.3.1 Packet delivery ratio (PDR)

Figures 8 and 9 illustrate the obtained results for studied

protocols based on various vehicle densities and velocities,

respectively. After pondering the results in Fig. 8, GyTAR

obtained the higher delivery ratio in compare to other

protocols for the first scenario (i.e. various vehicle densi-

ties). This is because of two main reasons: (1) it considers

vehicle density in its packet forwarding process and (2) the

process of end to end route tracking before sending packets

is eliminated. In contrast, vehicle density is ignored by

other routing protocols.

PDR is increased gradually for all protocols by

increasing the vehicles density. This phenomenon is rea-

sonable since in higher densities, vehicles are well-con-

nected and messages are exchanged easily among them.

Whereas, communication among vehicles in lower densi-

ties is very difficult due to the wireless signal dis-connec-

tivity caused by far distances among the vehicles.

However, PDR is decreased for all protocols when the

vehicle density overtakes a specific value (i.e.[250) owing

to radio interference and high number of packet collision.

On the other hand, the obtained results in Fig. 9 for

second scenario (i.e. various vehicle velocities) shows that

MOPR outperforms the other approaches in terms of PDR

for high vehicle velocity ([100 km/h), while, for lower

speeds, GyTAR guarantees higher PDR. This is because,

MOPR and GyTAR consider vehicles velocity and density

in their packet routing procedure, respectively. Although

AMAR also considers vehicles’ velocity in its routing

procedure, its next-hop selection procedure faces with

difficulty whenever the weight score of two nodes are

equal. And this is the reason of lower PDR for AMAR

compared with MOPR. EBGR has the lowest PDR due to

considering only the direction of nodes for finding next-hop

node and ignoring the other factors such as nodes velocity,

density and position. Generally, GyTAR delivers more

packets in both defined scenarios and it is the best choice to

route infotainment or non-safety messages where the

number of receivers is important.

5.3.2 Delay (D)

In this part, we compare the performance of all studied

routing protocols in terms of average delay experienced by

data packets. As shown in Fig. 10, EBGR and B-MFR

achieve much lower delay than the other protocols con-

sidering various vehicle densities. In both B-MFR and

EBGR border nodes and edge nodes are selected for for-

warding packet that significantly reduces the number of

intermediate nodes in the packet routing procedure. Thus,

in B-MFR and EBGR, the time taken to deliver the packet

from source to destination (delay) is reduced in any traffic

scenarios. For lower densities (\200 nodes), AMAR has

the worst performance, while, for higher densities, GyTAR

obtained the highest delay among the others. This is

because of following reasons: (1) Although considering

vehicles speed, position and direction in packet forwarding

procedure increases PDR of AMAR, it also causes some

delay, and (2) Further, in GyTAR as the node density

increases, the number of intermediate node in routing

Fig. 8 Packet delivery ratio comparison between GyTAR, MOPR,

AMAR, B-MFR and EBGR considering various vehicle densities

Fig. 9 Packet delivery ratio comparison between GyTAR, MOPR,

AMAR, B-MFR and EBGR considering various vehicle velocities

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procedure increases as compared to the other approaches

which leads more delays, respectively. Similar results

obtained for scenario with various vehicle velocities. These

results are depicted in Fig. 11. Both Figures shows

ascending trend for all protocols considering delay as

evaluation metric. However, this slope is sharper in the

second scenario due to usage of store-and-forward mech-

anism instead of finding next-hop node by all protocol.

This is because; gaps between the vehicles increase and the

number of involved vehicles in the forwarding process

reduce as the vehicles velocity increase. It is worth noting

that packets are routed with lower delay by using EBGR in

both defined scenarios and it is the best choice to route

safety messages among vehicles where on-time delivery is

important.

5.3.3 Routing overhead ratio (ROR):

Figure 12 depicts the evaluation of five routing protocols

as function of vehicle density. As shown, the routing

overhead gradually increased for all the protocols with

increase in number of vehicles. This is rational as higher

vehicle density resulted less gap between vehicles which

causes more conflicts in data transmission. Consequently,

ratio of routing overhead is increased in all the protocols in

case of higher vehicle density. However, we have wit-

nessed this increment is less for some protocols in specific

vehicle density of 150. The reason is in such conditions the

gaps between vehicles are more sensible (not too far and

not too close), thus the procedure of delivering the packets

to their destination successfully perform. This avoids re-

transmitting the data packets to vehicles and therefore

routing overhead ratio is reduced. Same Figure shows

GyTAR had less routing overhead ratio in comparison with

others. This is owing to consideration of density for

selecting the next hop. Figure 13 confirms that the routing

overhead gradually reduced for all the protocols with

increase in speed. This is expected since higher speed of

vehicles means less vehicle density which leads to have

more gap between the vehicles on road. Some routing

protocols however retransmit the packet if they found that

the packet did not receive to the destination. In such pro-

tocols overhead is increased owing to packet flooding.

Contrary, protocols like GyTAR, MOPR, AMAR, B-MFR

and EBGR uses carry and forward method which prevent

of packet flooding in such conditions. This is the reason

Fig. 10 Average packet delay comparison between GyTAR, MOPR,

AMAR, B-MFR and EBGR considering various vehicle densities

Fig. 11 Average packet delay comparison between GyTAR, MOPR,

AMAR, B-MFR and EBGR considering various vehicle velocities

Fig. 12 Routing overhead ratio comparison between GyTAR,

MOPR, AMAR, B-MFR and EBGR considering various vehicle

densities

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that routing overhead ratio is reduced where vehicle speed

is higher. Result showed that GyTAR protocol has better

performance in terms of routing overhead considering

higher vehicle velocity ([80 km/h). This is because the

higher speed leads to have less vehicle density which

avoids the vehicles to be well-connected. In this condition

routing the packet through the path with higher density

increases the packet delivery and consequently reduces the

routing overhead.

6 Conclusions

This paper addresses packet routing, which is one of the

major challenges in VANETs. Packet routing is affected by

VANETs’ unique characteristics such as high mobility with

the constraint of road topology, fast network topology

changes, frequently disconnected networks, and time-sen-

sitive data exchange. A taxonomy is proposed for position-

based routing protocols based on vehicular communica-

tions, namely, V2V and V2I. The performance of V2V

dynamic anchor position-based routing protocols (i.e. Gy-

TAR, MOPR, AMAR, B-MFR and EBGR) are investigated

and evaluated under two different scenarios (i.e. various

vehicle densities and velocities) through NS-2. These

routing protocols are selected since they address the most

challenging condition of packet routing in VANET due to

the absence of infrastructure nodes as well as dynamicity of

anchor nodes in the communication procedure. Based on

the literature review, vehicles velocity, position, direction,

density and distance from distance from destination node

are the main factors that are used by these protocols in

routing and finding the next-hop nodes for forwarding the

packets. Various combinations of these factors are con-

sidered in a number of studies in order to propose suitable

position-based routing protocols for vehicular networks.

Performance of studied protocols are compared and

evaluated in two different scenarios (i.e. various vehicle

densities and velocities) considering average delay, routing

overhead ratio (ROR) and packet delivery as evaluation

metrics. Based on the obtained results, GyTAR and EBGR

outperform the other protocols in terms of packet delivery

ratio and average delay, respectively. As a result, GyTAR

and EBGR are the best choices to route non-safety and

safety messages, respectively, in VANET scenarios. The

comparison provided in this paper makes the research

contribution of this survey paper quite higher than a regular

survey paper only with explanations. Further studies might

focus on designing a smarter routing protocol which

combines all the mentioned factors including vehicles

velocity, position, direction, density and distance from

distance from destination node for existing protocols.

Besides, by examining these protocols in simulation envi-

ronment which considers obstacles (i.e. visibility model

along with the attenuation model) such as trees or building,

we can contribute to obtain more realistic experimental

results in our future work.

Acknowledgments This research is supported by UM High Impact

Research MoE Grant UM.C/625/1/HIR/MOHE/FCSIT/09 from the

Ministry of Education Malaysia.

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Mohammad Reza Jabbarpouris currently a Ph.D. candidate

and Research Assistant in the

System and Technology

Department, Faculty of Com-

puter Science and Information

Technology at University of

Malaya, Malaysia. He received

his Master of Data Communi-

cation and Networking in 2012

at the same university. He is the

author of various articles and

serving as member of editorial

board and reviewers in interna-

tional journals and conferences.

His current research interests include Vehicular and Mobile Ad Hoc

Network (Architectures, Protocols, Security, and Algorithms),

Vehicular Cloud Computing, Vehicle Traffic Routing Systems,

Artificial Intelligence and Swarm Intelligence.

Alireza Marefat is pursuing his

master’s degree in Computer

Science at University of

Malaya, Malaysia, Kuala Lum-

pur and in 2012 he received his

bachelor’s degree in Informa-

tion Technology with special-

ism in Networking from

Staffordshire University. His

current interests comprise

Vehicular Ad hoc Network, ITS

in road safety and vehicle traffic

routing systems.

Ali Jalooli received his bache-

lor’s degree in computer science

from the Staffordshire Univer-

sity in 2012. He is currently

completing his master’s degree

in computer science and works

as research assistant since May

2013 at University of Malaya,

Malaysia. His research interests

include Vehicular ad hoc net-

work, quality of service, vehicle

traffic routing and applications

of intelligent transportation

system.

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Rafidah Md Noor received her

BIT from University Utara

Malaysia in 1998, and M.Sc. in

Computer Science from Uni-

versity Technology Malaysia in

2000, and Ph.D. in Computing

from Lancaster University in

2010. She is currently a Senior

Lecturer at Computer System

and Technology Department at

the Faculty of Computer Sci-

ence and Information Technol-

ogy, University of Malaya. Her

research interests include net-

work mobility, vehicular net-

works, mobile IP, quality of service and quality of experience.

Rashid Hafeez Khokhar is

working as Lecturer in the

School of Computing and

Mathematics, Faculty of Busi-

ness, Charles Sturt University

(CSU). He received his Ph.D. in

Computer Science from Uni-

versity Technology Malaysia

(Malaysia) in 2011. He also

received his M.S. in Computer

Science (by research) from the

same university in 2005. Earlier,

he did his M.S. (Information

Technology) and M.S. (Statis-

tics) from Preston University

(Pakistan) and University of the Punjab (Pakistan) in 2003 and 1999

respectively. He is the author of various articles and serving as

member of editorial board and reviewers in international journals and

conferences. He is a member of IEEE and ACM. His areas of interest

are Vehicular and Mobile Ad Hoc Network (Architectures, Protocols,

Security, and Algorithms), Mobile Cloud Computing, Artificial

Intelligence and Real-Time Communications, and High Performance

Computing.

Jaime Lloret received his

M.Sc. in Physics in 1997, his

M.Sc. in electronic Engineering

in 2003 and his Ph.D. in tele-

communication Engineering

(Dr. Ing.) in 2006. He is a Cisco

Certified Network Professional

Instructor. He worked as a net-

work designer and administrator

in several enterprises. He is

currently Associate Professor in

the Polytechnic University of

Valencia. He is the head of the

research group ‘‘communica-

tions and remote sensing’’ of the

Integrated Management Coastal Research Institute and he is the head

of the ‘‘Active and collaborative techniques and use of technologic

resources in the education (EITACURTE)’’ Innovation Group. He is

the director of the University Expert Certificate ‘‘Redes y Comuni-

caciones de Ordenadores’’, the University Expert Certificate ‘‘Tec-

nologıas Web y Comercio Electronico’’, and the University Master

‘‘Digital Post Production’’. He is currently Chair of the Internet

Technical Committee (IEEE Communications Society and Internet

society). He has authored 12 books and has more than 240 research

papers published in national and international conferences, interna-

tional journals (more than 80 with ISI Thomson Impact Factor). He

has been the co-editor of 15 conference proceedings and guest editor

of several international books and journals. He is editor-in-chief of the

international journal ‘‘Networks Protocols and Algorithms’’, IARIA

Journals Board Chair (8 Journals) and he is associate editor of several

international journals. He has been involved in more than 200 Pro-

gram committees of international conferences and in many organi-

zation and steering committees. He led many national and

international projects. He is currently the chair of the Working Group

of the Standard IEEE 1907.1. He has been general chair (or co-chair)

of 19 International workshops and conferences (chairman of SEN-

SORCOMM 2007, UBICOMM 2008, ICNS 2009, ICWMC 2010,

eKNOW 2012, SERVICE COMPUTATION 2013, COGNITIVE

2013, and ADAPTIVE 2013, and co-chairman of ICAS 2009,

INTERNET 2010, MARSS 2011, IEEE MASS 2011, SCPA 2011,

ICDS 2012, 2nd IEEE SCPA 2012, GreeNets 2012, 3rd IEEE SCPA

2013, SSPA 2013 and local chair of MIC-WCMC 2013). He is cur-

rently co-chair of AdHocNow 2014, MARSS 2014, SSPA 2014, and

local chair of IEEE Sensors 2014. He is IEEE Senior and IARIA

Fellow.

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Performance Analysis of V2V Dynamic Anchor Position-based Routing Protocols

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