AN INTELLIGENT VEHICLE APPROACH TO MOBILE

VEHICULAR AD HOC NETWORKS

Clustering Optimisation in Dynamic Traffic Networks

James G. Haran, Peng Fan

University of Illinois at Chicago, Department of Computer Science,

851 S. Morgan (M/C 152), Room 1120 SEO, Chicago, IL 60607-7053 USA

Peter Nelson, John Dillenburg

University of Illinois at Chicago, Department of Computer Science,

851 S. Morgan (M/C 152), Room 1120 SEO, Chicago, IL 60607-7053 USA

Keywords: Mobile Ad Hoc Networks (MANETs), Vehicular Ad Hoc Networks (VANETs), Intelligent Transportation

Systems (ITS), Inter-Vehicle Communication (IVC), Dedicated Short Range Communications (DSRC),

Distributed Clustering Algorithm (DCA).

Abstract: The application of Mobile Ad Hoc Network (MANET) technologies to Vehicular Ad Hoc Networks

(VANETs) in the service of Intelligent Transportation Systems (ITS) has brought new challenges in

maintaining communication clusters of network members for long time durations. Stable clustering

methods reduce the overhead of communication relay in MANETs and provide for a more efficient

hierarchical network topology. During creation of VANET clusters, each vehicle chooses a head vehicle to

follow. Cluster stability in these simulations is measured by the average number of cluster head changes per

vehicle during the simulation. In this paper we analyse the effects of six different clustering methods in a

simulated highway environment to determine which method provides optimum stability over the simulation

timeline.

1 INTRODUCTION

Vehicular Ad Hoc Networks (VANETs), an

outgrowth of traditional Mobile Ad Hoc Networks

(MANETs), provides the basic network

communication framework for application to an

Intelligent Transportation System (ITS). The U.S.

Federal Communications Commission (FCC) has

recently allocated the 5.85-5.925 GHz portion of the

spectrum to inter-vehicle communication (IVC) and

vehicle-to-roadside communication (VRC) under the

umbrella of dedicated short-range communications

(DSRC). This has fuelled significant interest in

applications of DSRC to driver-vehicle safety

applications, infotainment, and mobile Internet

services for passengers.

Vehicles provide a robust infrastructure for the

creation of highly mobile networks. In addition to

providing a stable environment for the low cost and

robust wireless communication devices typical of ad

hoc networks, vehicles can easily be equipped with

the storage, processing, and sensing devices

necessary in any ITS implementation. A huge

opportunity exists to leverage VANETs to enable a

wide variety of service and societal applications.

VANETs have significant advantages over the

traditional MANET. Vehicles can easily provide the

power required for wireless communication devices

and will not be seriously affected by the addition of

extra weight for antennas and additional hardware.

Furthermore, it can be generally expected that

vehicles will have an accurate knowledge of their

own geographical position, e.g. by means of Global

Positioning Satellite (GPS). Thus, many of the

issues making deployment and long term use of ad

hoc networks problematic in other scenarios are not

relevant in MANETs.

In addition, there is a wealth of desirable

applications for ad-hoc communication between

vehicles ranging from emergency warnings and

224

G. Haran J., Fan P., Nelson P. and Dillenburg J. (2005).

AN INTELLIGENT VEHICLE APPROACH TO MOBILE VEHICULAR AD HOC NETWORKS - Clustering Optimisation in Dynamic Trafﬁc Networks.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 224-231

DOI: 10.5220/0001178002240231

 SciTePress

distribution of traffic and road condition information

to chatting and distributed games. As a consequence

many vehicle manufacturers and their suppliers are

actively supporting research on how to integrate

mobile ad-hoc networks into their products.

Vehicles in a VANET environment move within

the constraints of traffic flow while communicating

with each other via wireless links. Ah hoc networks

use less specialised hardware for infrastructure

support and leave the burden of network stability on

the individual nodes within the network. Without

routers, or other dedicated communication hardware,

a possible method to optimise communication within

the network is to develop a hierarchical clustering

system within the network. This clustering system

would identify certain lead vehicles that act as the

relay point of communication between vehicles local

to that node and other vehicle clusters. To support

the dynamic nature of the VANET environment, the

vehicles clustering must e periodically updated to

reflect topological changes and vehicle movements.

Clustering within the network must be very fast to

minimise time lost to clustering (Johansson, 2004).

A significant amount of research focuses on

optimal methods for clustering nodes in MANETs.

VANETs, however, pose new challenges in cluster

head selection and network stability. VANETs must

follow a tighter set of constraints than MANETs,

and therefore require specialised clustering

algorithms. First, nodes or vehicles cannot

randomly move within the physical space, but must

instead follow constraints set in place by the real

road network topology. Second, vehicle movements

follow well-understood traffic movement patterns.

Each vehicle is constrained by the movements of

surrounding vehicles. Third, vehicles generally

travel in a single direction and are constrained to

travel within a two-dimensional movement. Given

these movement restrictions and the knowledge of

position, velocity, and acceleration common

available to on-board vehicle systems it is possible

to approach clustering more intelligently and

possibly discover a better clustering methodology

for VANET environments.

The constrained environmental conditions of

VANETs warrant a constrained simulation

environment. Many simulation tools and

environments have been designed for MANET

implementations. These tools, however, fail to

adequately model the needs of a VANET network.

Compared to the random movements modelled in

MANET environments, VANET simulation

movements must behave according to traffic patterns

in terms of car-following, lane-changing, directional

movement, velocity, and acceleration among others.

Current MANET simulation environments cannot be

considered suitable for VANET simulations even in

the broadest sense. Therefore, simulation of the

network environment is best performed with traffic

micro-simulation tools. For the purpose of this

study, simulation and traffic modelling was

performed using a micro-simulation tool specially

modified to perform randomised vehicle-based

clustering under a number of algorithms and traffic

constraints. This approach also allows further

research on traffic statistics and flow improvements

as a result of network communication. Further

modifications to the environment were made to log

vehicle cluster, position, velocity, and acceleration

states during simulation activity.

This work outlines the performance of basic

MANET algorithms in the constrained micro-

simulation model and the further evaluation of

algorithms specifically designed to utilise vehicle

state information. In addition, a utility function

design is outlined for controlling the per-vehicle

clustering methods. Association with and

dissociation from clusters, as a result of the mobile

nature of VANET nodes (vehicles) perturb the

network and cluster selections. Cluster

reconfiguration and cluster head changes are

unavoidable. Therefore, a good VANET clustering

algorithm should seek to regulate rather than

eliminate cluster changes. This algorithm should

also maintain cluster stability as much as possible

during vehicle velocity and acceleration changes

and/or traffic topology shifts. Otherwise, the

overhead of cluster re-computation and the involved

information exchange will result in high

computational cost and negate the benefits of

VANET communication. The ideal VANET cluster

will maintain its cluster head and members over the

longest possible time range. This concept will be

explained and evaluated further later in this paper.

2 BACKGROUND AND RELATED

WORK

Figure 1: Clustering within a 12-node MANET

environment

AN INTELLIGENT VEHICLE APPROACH TO MOBILE VEHICULAR AD HOC NETWORKS - Clustering

Optimisation in Dynamic Traffic Networks

225

Communication network clustering organises the

network nodes into a hierarchical arrangement.

Figure 1 provides and example of the organisation of

twelve nodes into three clusters. The basic

communication capability between the twelve nodes

is outlines as connections between the lower tier of

the hierarchy. These twelve basic nodes are then

grouped into clusters using some algorithm. In the

upper tier of Figure 1, the three cluster head nodes

are displayed with connections between them

representing the possible message paths under the

cluster-constrained network (Bettstetter, 2002).

This clustered architecture reduces the

communication relay points for each node to a small

subset of the total network. Each cluster head

aggregates local member topology and acts as a

relay point for communication between its members

and members of other clusters. This reduces the

messages exchanged between individual network

nodes and the overhead of information stored within

those nodes (Garg, 2004).

Attention on clustering in MANETs has increased

considerable as wireless technologies improve and

MANET theories become practice

(Sivavakeesar,

2002, Basagni, 1999, Basagni, 1997). Most of these

approaches embrace the role of a cluster head that

maintains the cluster and provides the entry point of

that cluster into the broader network. Among

several proposed cluster head selection algorithms

the predominant approaches are the (i) Lowest-ID

algorithm and (ii) Highest-Degree algorithm.

Recent work has simulated the performance of these

algorithms using random placement in a square grid

with multi-directional node movement (Basagni,

1999, Gerla, 1995). As previously stated, this does

not translate well into the VANET environment.

2.1 Lowest-ID Algorithm

The Lowest-ID algorithm involves the selection of

cluster heads by means of an absolute ordering of a

fixed vehicle ID attribute. Cluster formation is

performed using node-level election of cluster heads.

During the clustering stage, each node within the

network broadcasts its ID to all other reachable

nodes. Each node, in turn, chooses as its cluster

head the node with the lowest ID. This method has

been discussed in great detail (Gerla, 1995,

Ephremides, 1987, Jiang, 1999) in a number of

works and is well known for its stability in general

MANET applications. In each cluster, the node

within range with the lowest ID becomes a cluster

head and maintains the cluster membership

information of all other nodes.

2.2 Highest-Degree Algorithm

This algorithm uses the degree of the nodes within

the network to determine the cluster heads. The

general idea that choosing high-degree nodes as

cluster head candidates tends to uncover larger

clusters. In MANET implementations, however,

small movements in network nodes can often lead to

a large number of degree changes throughout the

network. This, understandably, has a detrimental

effect on the stability of the clusters over time

(Gerla, 1995). So cluster heads in Highest-Degree

implementations are not likely to maintain cluster

head status for long.

Many additional clustering algorithms have been

defined to meet special-case purposes. This research

focuses on the Lowest-ID and Highest-Degree

algorithms because they have constant time

complexity and good scalability. For convenience,

these algorithms have been summarised in Table 1.

Table 1: Summary of Current Algorithms

Algorithm Strengths Weaknesses

Lowest-

FAST AND SIMPLE.

RELATIVELY STABLE

CLUSTERS

Small clusters,

long cluster

head duration.

Highest-

Degree

Most connected nodes

appropriately given

higher priority.

Relatively

unstable

clusters.

“The Lowest-ID clustering was generalised to a

weight-based clustering technique, referred to as the

DCA (Distributed Clustering Algorithm) in

(Basagni, 1999,). In DCA, each node is assumed to

have a unique weight (hence the weights are totally

ordered) instead of just the node ID or degree, and

the clustering algorithm uses the weights instead of

the IDs for the selection of cluster heads. However

the technique of assignment of weights has not been

discussed (Basu, 2001). ”

This document will outline a custom weighting

scheme incorporating traffic-related information in

next section. This implementation will not consider

network broadcasts requiring more than one hop in

communication. This simplifies the overall

communication and clustering strategy and reduces

the overall bookkeeping necessary to maintain the

clusters. This approach seeks to obtain optimal

results by adding traffic-specific information to the

clustering logic.

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3 TRANSPORTATION-SPECIFIC

CLUSTERING

METHODOLOGY

Review of current MANET research highlights the

need for a transportation-specific review of

clustering methodology and the discovery of traffic-

optimized clustering schemes. This research chose

to design a utility-based methodology for network

cluster formation. In this approach, each vehicle

implements some form of utility analysis of each

proximally located possible cluster head.

Periodically, each vehicle will broadcast general

network information such as ID and current degree

as well as vehicle-specific traffic statistics such as

position, velocity, and acceleration. Upon receipt of

this information, each vehicle chooses a cluster head

by evaluating the utility of each potential head. The

node with the highest utility is selected as the cluster

head.

3.1 Utility Function

A utility-based approach to clustering requires the

creation of a vehicle-specific agent model for

periodic cluster formation. This model was

implemented by augmenting each vehicle in a traffic

micro-simulation platform to periodically determine

and store cluster head information. The cluster head

determination algorithm was implemented in a

single weight method that produced a weight value

for each vehicle with which the current vehicle can

communicate. After implementation of this method,

the Lowest-ID and Highest-Degree methods were

implemented and tested. Once validation of these

algorithms was complete, four other algorithms were

designed and implemented to harness vehicle state

information. Rather than overcomplicate this initial

investigation with compound weighting logic, the

algorithms were chosen to use single parameter

weighting based on a) closest velocity to the current

vehicle, and b) closest position, c) closest velocity,

and d) closest acceleration to the average of all

proximal vehicles. The belief is that these traffic-

specific algorithms will be better predictors of the

common traffic situations that lead to cluster

dissociation.

As an important note on this investigation, an

exhaustive investigation of vehicle parameters and

parameter-specific cluster methods was not

performed or intended. Many other vehicle state

measurements exist and are equally predictors of

traffic movement, but have been fixed for this

experiment.

The four new clustering methods are summarized

as follows:

a) Closest Velocity: A vehicle attempts to join

with other vehicles in a cluster head to member

relationship in order of closest velocity to itself.

b) Closest Position to Average: A vehicle attempts

to choose as its cluster head in order of the

absolute difference of candidate’s position to

the average position of all proximal vehicles.

c) Closest Velocity to Average: A vehicle attempts

to choose as its cluster head in order of the

absolute difference of candidate’s velocity to

the average velocity of all proximal vehicles.

d) Closest Acceleration to Average: A vehicle

attempts to choose as its cluster head in order of

the absolute difference of candidate’s

acceleration to the average acceleration of all

proximal vehicles.

These steps outline the procedure for

implementation of this utility function:

1. Each vehicle determines the vehicles within

range by polling the local broadcast region and

tracking the candidate cluster head set C. All

vehicles with broadcast range are considered

candidate cluster heads.

2. Using candidate set C and the state information

received by broadcast, each candidate is

evaluated using the utility function.

3. The cluster head is chosen in decreasing order

of utility. The petition for cluster membership

is broadcast to the chosen vehicle. Should the

chosen vehicle deny the request the vehicle with

the next highest utility is selected and this step

repeated.

A vehicle may deny the selection as cluster head

if it has reached its maintainable limit of cluster

members or if the vehicle has already chosen to join

with another cluster head. Note, in all algorithms

but method a), a vehicle may elect itself as its cluster

head. Random selection of vehicles simulates

asynchronous cluster formation at fixed time

intervals.

3.2 Vehicular Considerations of

Cluster Formation

Due to the dynamic nature of traffic flow, the

member vehicles as well the cluster heads tend to

move in semi-related motion throughout the

roadway. This motion destabilizes the network

clusters and warrants periodic cluster reformation.

Re-clustering may result in transition of nodes from

one cluster to another, split of a cluster into more

than one cluster, or convergence of multiple clusters

into a single larger cluster. The frequency of cluster

AN INTELLIGENT VEHICLE APPROACH TO MOBILE VEHICULAR AD HOC NETWORKS - Clustering

Optimisation in Dynamic Traffic Networks

227

formation and cluster change is thus an important

consideration in algorithm evaluation.

Equally important is the size of each cluster.

Resource and relay algorithm performance

considerations may limit the manageable size a

cluster head’s cluster. For simplicity this research

used a common fixed upper bound on all vehicle’s

cluster size. The implication is that vehicles may

reject nodes within range due to resource

exhaustion.

The delicate balance between cluster size and

coverage has major implications in network

communication latency and throughput. Each

vehicle communicates with vehicles in other clusters

through the selected cluster heads. Care must be

taken to ensure that the head selection algorithm

does not have the unfortunate result of adding

network transmission bottlenecks. Alternately,

algorithms that yield too many cluster heads may

result in a computationally expensive system. An

important area of study is the selection of cluster

algorithms that balance high throughput and lowest

latency. The performance of the new algorithms

must be measured relative to previously analyzed

MANET algorithms. The objective of this research

is to evaluate the number of cluster changes and the

cluster size for each of our six algorithms.

As discussed, our simple utility functions are

actually one-dimensional weighting methods

considering only one attribute of each candidate

vehicle. MANET research covers many compound

or multi-dimensional clustering algorithms. In

general, these methods are presented to overcome

certain disadvantages of general MANET models

such as power consumption, low mobility, or

random multi-directional movement. These

algorithms have not been modeled because their

contributions to VANET implementations are not

immediately apparent.

4 SIMULATION STUDY

This study modified Traffic Simulation 3.0, an

Intelligent-Driver Model (IDM) (Treiber, 2000)

micro-simulation tool built to monitor traffic flow

under various basic highway configurations. This

environment simulates accelerations and braking

decelerations of drivers (i.e. longitudinal dynamics),

and uses the Minimized Overall Braking Induced by

Lane changes (MOBIL) lane change model. All

model parameters and the initial simulation source

code are available at (Treiber, 2005). This study

focused on the “on ramp” simulation environment.

4.1 Implementation

The source code for the aforementioned simulation

tool was modified to perform fixed interval cluster

formation using either of the six experimental

algorithms (Lowest-ID, Highest-Degree, Closest

Velocity, Closest Position to Average, Closest

Velocity to Average, and Closest Acceleration to

Average). To aid in algorithm visualisation, the

graphical display of the micro-simulation

environment was modified to display vehicle

clusters using contrasting colours.

4.2 Metrics

In addition to utility function and display changes,

periodic state logging was implemented. This data

provided the basis for the simulation result analysis

and algorithm comparison. To measure the system

performance, two metrics were identified: (i) the

average cluster head change per step and (ii) the

average cluster size. Metric (ii) alone does not

accurately depict system performance, so the relative

measurement (ii)/(i) was introduced to provide a

reasonable comparison metric between the analysed

algorithms. A method is considered relatively better

if it has either better stability using metric (i) or

larger average cluster size.

5 RESULTS

The simulation results represent the performance of

each algorithm across various wireless transmission

range values (0-300 meters) and maximum vehicle

speed (40-140 kilometers/hour) with a fixed

maximum cluster size of 50 vehicles. In addition,

the simulation time duration was held constant

across all tests. To minimise traffic flow variability

between simulations and enable repeatable test

results, the randomised features of the model were

seeded with the same value at each simulation run.

Figure 2 summarises the variation of the average

number of clusters with respect to the transmission

range. It illustrates the performance of all six

algorithms for a reasonably standard traffic flow

environment with a fixed maximum speed of

100km/h. Notably, the Lowest-ID and Closest

Position to Average algorithms show rapid initial

increase of cluster head changes as a result of

transmission range increase. These algorithms

quickly converge, however, in line with the uniform

distribution of the randomly generated ID values and

vehicles in the Intelligent Driver Model,

respectively. For small transmission ranges, most

ICINCO 2005 - ROBOTICS AND AUTOMATION

228

vehicles remain out of each other’s transmission

range. This leads to a severely disconnected

network. For the other four algorithms, the

likelihood of change in either of the metrics as a

result of increased transmission range results in a

steady increase in the number of clusters with

transmission range. The Lowest-ID algorithm

clearly performs better that the other five algorithms

and shows a convergence to a stable cluster count.

The Highest-Degree, Closest Velocity to Average,

and Closest Acceleration to Average algorithms

show almost equivalent performance characteristics.

Finally, the Closest Position to Average and Closest

Velocity algorithms show similar performance; a

result of common traffic patterns wherein similarly

Figure 2: Cluster Changes vs. Transmission Range.

0.1

0.2

0.3

0.4

0.5

0.6

25 50 100 150 200 250 300

T ransmission Range (met ers)

Avg Clusterhead Changes per time step

Highest-Degree

Lowes t-ID

ClosestVelocity

ClosestPositionToAvg

ClosestVelocityToAvg

ClosestAccelerationToAvg

Figure 2: Cluster Changes vs. Transmission Range.

Figure 3: Average Cluster Change vs. Speed Limit

0.1

0.2

0.3

0.4

0.5

0.6

40 80 100 120 140

Max Speed (km/h)

Avg Clusterdead Changes

Highest-Degree

Lowes t -ID

ClosestVelocity

ClosestPositionToAvg

ClosestVelocityToAvg

ClosestAccelerationToAvg

Figure 3: Average Cluster Change vs Speed Limit

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Optimisation in Dynamic Traffic Networks

229

located vehicles are more likely to share similar

velocities.

Figure 3 shows the effect of varying the

maximum speed on the average number of cluster

head changes with a fixed transmission range of

150m. Algorithm performance is consistent with

those of Figure 2. Speed limits are only useful only

in heavy-traffic situations (Treiber, 2000).

Figure 4 displays the performance of all but the

Lowest ID algorithm over various transmission

ranges. Higher curves indicate better overall

performance. Highest-Degree, Closest Velocity to

Average, and Closest Acceleration to Average again

show similar performance and better overall results

than the Closest Position to Average or Closest

Velocity. Figure 5 shows the overall performance

across various speed limits for this same algorithm

subset. Note that the Closest Velocity to Average

algorithm outperforms the Highest-Degree and

Closest Acceleration to Average as the maximum

speed nears 100km/h. At this speed, the overall

traffic flow performs optimally without any noise

(traffic slowdown or bottleneck).

6 CONCLUSION

The analysis performed in this research highlights

Figure 5: Clustering Ratio vs. Speed Limit

40 80 100 120 140

M ax Sp eed (km/h)

AvgSize/AvgHeadChang

H ig h e s t-D e g re e

ClosestVelocity

ClosestPositionToAvg

ClosestVelocityToAvg

ClosestA ccelerationToA vg

Figure 5: Clustering Ratio vs Speed Limit.

Figure 4: Clustering Ratio vs. Transmission Rang

25 50 100 150 200 250 300

Transmission Range (meters)

Avg Size/Avg Head Change

Highest-Degree

ClosestVelocity

ClosestPositionToAvg

ClosestVelocityToAvg

ClosestAccelerationToA vg

Figure 4: Clustering Ratio vs. Transmission Range.

ICINCO 2005 - ROBOTICS AND AUTOMATION

230

the performance of the Lowest-ID clustering

algorithms as optimal for the constrained MANET

environment provides by VANETs. As in MANET

studies, the Lowest-ID provides a stable cluster

topology over long time durations due to its nature

as an unbiased, uniformly distributed clustering

methodology.

Comparable in performance to the well-known

Highest-Degree algorithm, this research presented

the Closest Velocity to Average and Closest

Acceleration to Average algorithms. These

algorithms provided fairly stable clusters. Stability,

however, degraded as transmission range increased.

The Closest Velocity and Closest Position to

Average algorithms were also discussed in detail.

These algorithms showed somewhat stable

performance but were prone to cluster head changes.

One final note on the clustering implementation

is that each clustering step was performed using a

pure re-cluster. In other words, no previous state

information was reviewed prior to choosing the

cluster head. Additionally, no priority was given to

local nodes already assigned leadership during the

same cluster step. It is believed that cluster

performance can be greatly improved by performing

biased clustering in the utility function, i.e. give

priority to those nodes chosen as the cluster head in

either a previous clustering step or during the same

clustering step. These methods fall into the category

of compound clustering algorithms which were out

of the scope of this analysis.

7 FUTURE WORK

The results of this research provide an initial

approach to analysing parameterised VANET

dynamics from a traffic micro-simulation

perspective. The simulation results presented within

this paper represent a highly constrained traffic

simulation environment. Future studies should

apply the method of this research to larger scale

traffic micro-simulation environments under more

dynamic traffic situations.

In addition to the modelling of larger traffic

models using utility-based clustering, research

should be directed at the maximisation of network

communication within the VANET network in

relation to different clustering algorithms.

Multi-parameter utility functions also provide

another path for future discovery. VANETs are not

generally prone to the same problems that led to

compound clustering methods in MANETs.

Therefore a traffic-specific approach is needed to

handle these in VANETs.

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