Tra c Congestion Estimation in Vehicular Ad Hoc...

Traffic Congestion Estimationin

Vehicular Ad Hoc Networks

A thesis submitted in partial fulfillment of the requirementsfor the degree of

Bachelor of Technology (Honours)

in

Computer Science and Engineering

by

Rayman Preet Singh

06CS3023

advised by

Dr. Arobinda Gupta

Department of Computer Science and EngineeringIndian Institute of Technology, Kharagpur

May 2010

Certificate

This is to certify that the thesis entitled Traffic Congestion Estima-

tion in Vehicular Ad Hoc Networks submitted by Rayman Preet Singh

(06CS3023) to the Department of Computer Science and Engineering is a

bonafide record of research work carried out by him under my supervision

and guidance. This thesis has fulfilled all the requirements as per the reg-

ulations of the institute and, in my opinion, has reached or exceeded the

standard needed for submission.

Dr. Arobinda Gupta

Assosiate Professor

Department of Computer Science and Engineering

IIT Kharagpur

May 2010

Acknowledgment

I would like to express my gratitude towards Prof. Arobinda Gupta for

the supervisory role he played to utmost perfection. Taking time out of his

busy schedule, he ensured that the project work was carried out methodically

and meticulously. I especially thank him for his encouragement and his

accurate comments which were of critical importance, and am indebted to

him for extending out all the necessary support throughout the duration of

the project and for being a constant source of inspiration.

I would also like to thank Md. Mozaffar Afaque, Vinu Rajshekhar and

Gourav Khaneja for their invaluable help, guidance and motivation. Their

continuous support and encouragement has played a key role in the comple-

tion of this work.

Rayman Preet Singh

06CS3023

Department of Computer Science and Engineering

IIT Kharagpur

May 2010

Contents

Contents i

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Literature Overview 6

2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Background and Essential Notions . . . . . . . . . . . . . . . . 8

3 Beacon Characterization 10

3.1 Need for Characterization . . . . . . . . . . . . . . . . . . . . 10

3.2 Approach towards solution . . . . . . . . . . . . . . . . . . . . 11

3.3 Beacon Relevance . . . . . . . . . . . . . . . . . . . . . . . . . 13

4 Proposed Algorithm 14

4.1 Classes of Vehicular Congestion . . . . . . . . . . . . . . . . . 14

4.2 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . 15

4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

i

5 Simulation Study 20

5.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . 20

5.2 Parameter Initialization . . . . . . . . . . . . . . . . . . . . . 21

5.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 24

5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.4.1 Scenario 1 : Wide moving jam (J) . . . . . . . . . . . . 25

5.4.2 Scenario 2 : Synchronized Traffic Flow . . . . . . . . . 32

5.4.3 Scenario 3 : Low Congestion . . . . . . . . . . . . . . . 39

6 Applications 46

6.1 Application : Information Dissemination . . . . . . . . . . . . 47

6.1.1 Probability based Approach . . . . . . . . . . . . . . . 47

6.1.2 Deterministic Approach Using Vehicular Congestion . . 48

6.1.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . 49

7 Conclusion 54

7.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

ii

Chapter 1

Introduction

A mobile ad hoc network(MANET) is a wireless network consisting of mobile

nodes in which all the network-level activity is carried out by the nodes

themselves, without additional infrastructure support. Each node in such

a network plays the role of a router as well as an end-machine, and hence,

all nodes in the network participate actively in message forwarding. The

network topology is subject to change owing to the mobility in the nature of

the nodes.

Vehicular Ad-Hoc Networks (VANETs) are a special kind of Mobile Ad-

Hoc Networks (MANETs), where wireless-equipped vehicles form the under-

lying network. Communications in mobile ad hoc wireless networks bolsters

various distributed applications among mobile nodes in infrastructure-free

environments. However, characterized by relatively high mobility, communi-

cation in VANETs exhibits stronger challenges than that in any other class

of MANETs.

Among various communication applications in VANETs, there is a wide

range of applications aimed at providing a solution to problems experienced

by commuters due to modern day vehicular traffic. These include appli-

cations involving traffic safety, traffic management and traffic monitoring,

which not only provide for an elevated levels of on-road safety but also maxi-

mize on-road traffic flow leading to an improved driver experience. Commer-

1

Chapter 1: Introduction

cial applications are designed for providing an enhanced user experience, and

include communication and data-sharing applications for providing services

such as Internet access, audio/video streaming solutions etc.

Vehicular traffic congestion is a major problem associated with vehicular

traffic which has been attracting the extensive attention of research in the

field of VANETs. Vehicular congestion estimation shall not only provide a

basis for development of traffic monitoring application but also aid in devel-

opment of other applications which can exploit traffic congestion information

for other goals in VANETs such as disseminating information, suggesting al-

ternate driving route etc.

Although, vehicular congestion is very well defined qualitatively, a definite

quantitative measure for it exists only in terms of features such as average

commuting time, number of vehicles on the road etc. Mobility pattern asso-

ciated with the nodes in VANET bears certain characteristic features, owing

to its large similarity with fluid flow. This traffic flow pattern, coupled with

vehicle-to-vehicle communication in a VANET can be effectively used to de-

tect and estimate vehicular traffic congestion.

1.1 Motivation

Although vehicular traffic and congestion detection and estimation tech-

niques have been studied in much details, there exists a paucity of correlated

study of traffic flow and associated vehicular congestion in the light of Vehicu-

lar Ad Hoc Networks (VANETs). Vehicular traffic congestion is characterized

by the relative presence or absence of certain specific characteristics, but a

fixed measure or metric to represent them effectively is needed for facilitat-

ing congestion estimation in a distributed environment such as VANETs. In

order to develop and employ any such technique suitable for deployment in

VANETs , a demarcating notion of the “condition” of traffic surrounding a

node needs to be defined.

Also, an effective measure of the traffic condition would also allow us to

2


relate it to the state of the wireless network prevalent in any given area of

study, which can be exploited to achieve efficiency of the network governed

by certain network parameters such as channel load, channel availability and

many others. Further, determination of the network condition can, help to

enhance the existent network modality, and, help avoid occurrence of situa-

tions that are hazardous for effective and efficient car to car communications,

whose persistent and equitable state is vital for maintaining a level of vehic-

ular safety on the road.

Being a distributed system, all operations taking place across the VANET,

can be classified into : initial(a node joins the VANET), steady state and ter-

minating(a node leaves the VANET) processes. However, due to the highly

degree of mobility of the nodes only the initial and steady state processes are

of interest, as the duration of communication between two nodes is transitory

and varies across different nodes. Thus, here we concentrate mainly on the

initial and steady state processes, which need to occur at a node for estimat-

ing vehicular congestion in its surrounding. The study and development of a

traffic congestion estimation technique can quite reasonably be divided into

the following parts :

1. Analyzing node mobility and network parameters, along with their as-

sociated pattern of change with network and traffic topology. By dif-

ferentiating traffic and network topology features that are expected

to contribute significantly to any measure or metric(s) of congestion,

we can abstract out the problem of congestion estimation into definite

quantities.

2. Formulating effective and adaptive procedures/protocol(s) that help

measure the vehicular congestion in a distributed way at real-time.

The nodes can then use the measured values to effectively classify their

local ‘view’ of traffic congestion, which is needed for effective sharing

over the VANET and formation of an aggregated picture of congestion.

3. Developing a process for macroscopic measurement and estimation of

vehicular congestion at a global scope, taking into consideration the

3


dynamism of the mobile nodes in the VANET.

The thesis shall investigate all three divisions sequentially.

Note that, in this work we shall assume all nodes (vehicles) to be of non-

malicious nature, and that, all nodes adhere to the procedures/protocol(s)

devised herein.

1.2 Contributions

This thesis presents the following contributions :

1. An approach for classification of surrounding nodes for estimation and

classification of vehicular congestion is presented. The approach utilizes

both the traffic mobility patterns and the network topology character-

istics of a VANET.

2. An algorithm for obtaining the different classes of vehicular conges-

tion at real-time is constructed, and experimental results evaluating its

performance are presented.

3. Information dissemination in VANETs, is presented as an application

of the devised algorithm, and deploys the classes of measured vehic-

ular congestion in a i) probability based approach ii) threshold based

deterministic approach. Further, comparative analysis of performance

is described by means of experimental results.

1.3 Thesis Overview

The thesis is further organized as follows :

• Chapter 2 presents a survey of related work on vehicular congestion

and traffic flow in context of VANETs. Some background, essential

4


notions and system model details relating to our work are also incor-

porated.

Chapter 3 discusses the need the approach we adopt for quantify-

ing traffic congestion, and describes how beacon messages sent over a

VANET need to be distinguished in order of their importance to con-

gestion. We end the chapter with the description of characterization of

beacons and hence their sender nodes, along with a metric that can be

adopted suitably to implement the characterization.

Chapter 4 discusses the different hierarchical classes into which traf-

fic congestion can be divided so as to ease detection and estimation.

We formally describe our proposed algorithm, along with the data-

structures that the algorithm requires. Finally, we present a discussion

on the expected traffic congestion estimation of the algorithm.

Chapter 5 describes the system setup we have employed for simulation

and performance evaluation of the algorithm. Further, the initial values

and default value set assigned to different empirical parameters of the

algorithm are presented. We describe, in detail, the measures we have

adopted to evaluate the performance of the algorithm. Finally, we

present simulation results of sensitivity analysis of the algorithm, and

analyzes its performance.

Chapter 6 discusses VANET applications where traffic congestion es-

timation can be put to use. We explain information dissemination as

an example application and suggest two different - probability based

and deterministic approaches, governed by traffic congestion estimation

for it. We end the chapter with simulation results and performance

evaluation of the suggested approaches on a sample highway scenario.

Chapter 7 summarizes the contributions of this thesis, and identifies

possible directions for future research.

5

Chapter 2

Literature Overview

This chapter provides a brief insight into some well known vehicular traffic

modelling theories and related research work conducted on them. Further,

we present a brief overview of the mode of communication used in VANETs.

We end this chapter with description of certain essential notions about our

assumed VANET system model.

2.1 Related Work

Being not specific to VANETs, traffic congestion estimation and control has

been widely studied in the past, by mathematically modeling the flow of ve-

hicles. Traffic flow theory by Haberman et al. [1] explores the relationships

among the three main quantities associated with vehicular traffic: vehicle

density, flow and speed. The traffic flow q, measures the number of vehicles

that pass an observer per unit time. The density k, represents the number

of vehicles per unit distance. The speed u, is the distance a vehicle trav-

els per unit time. The units of these quantities are usually expressed in

(veh/sec/lane), (veh/m/lane) and (m/sec) respectively.

Traffic streams are not uniform and vary both over space and time. There-

fore, the quantities q, k and u are meaningful only as averages or as random

6

Chapter 2. Literature Overview

samples of random variables, as has been formalized by Hall et al. [9]. Flow-

Density relationship established in [1] aims to characterize traffic scenarios

into two distinct phases of congested and sparse traffic. This classification,

referred to as the ‘two-phase’ traffic theory, is based on the Fundamental

Traffic flow Relationship [1],

q = u.k (1)

which effectively models the two phases.

Kerner et al. [3] describe three phases of traffic flow, as opposed to

the two-phase traffic theory and the fundamental traffic flow relationship[1].

Their ‘three-phase’ traffic theory [3], characterizes the traffic scenarios into

three different sets of possible cases :

1. Free Flowing (F) : In free flow conditions, drivers can choose their own

speed, as the topology is sparse and vehicle density k is low.

2. Wide Moving Jams (J) : The traffic flow in this phase is distributed into

clusters of dense vehicular distribution on the road. A stretch of a road

segment occupied by (J) is terminated or bounded by segments of traffic

under (F), or a scenario with relatively dense traffic, but possessing a

uniform flow.

3. Synchronized Flow (S): The traffic flow in this phase remains close to

free flow, although the speeds of the vehicles are reduced. The vehicle

density k is higher than that in (F), but flow q is uniform, with speed

of vehicles varying to a negligible extent for short durations. It is also

evident that a stretch of a road segment occupied by (S) is bounded or

terminated by segments undermined by (J) or (F) [3] , thus forming a

scenario, juxtaposed by (S) and either of (J) or (F).

Although the three-phase traffic theory captures many of the insufficien-

cies of the two-phase theory, but Heling et al. [10] discuss some of the incon-

sistencies with the three-phase traffic theory, by exploring partially different

interpretations of its application. Several other theories attempt to define

7


the relationships among each pair of variables in (1), but no single theory

provides the complete picture.

Many studies in VANETs focus on free-flow traffic in their design and

analysis of new protocols (e.g. [12,11,13]). The studies that investigate

connectivity either analytically or using simulations also set the traffic con-

ditions to free flow [15,14]. This choice allows for the greatest flexibility

in controlling each of the vehicle traffic parameters (speed, flow, and den-

sity) independently. However, any vehicular congestion estimation technique

being developed for suitable deployment in modern-day traffic, needs to be

analyzed against an exhaustive set of possible traffic scenarios, and the three-

phase traffic scenario provides the same in a structured and systematic way.

A traffic congestion detection and estimation application is described by

Ghazy et al. [16] and by Padron et al. [17], however these approaches present

congestion detection at a macroscopic level of the VANET, using extensive

collaborative processes amongst the vehicles. Work on congestion estimation

at a global scope by use of locally gathered information has not evolved

as yet. As shall be shown in Chapter 3, the problem of traffic congestion

estimation using VANETs needs to be approached at in a manner different

from that in other typical VANET applications.

2.2 Background and Essential Notions

The mode of communication in VANETs involves broadcast of messages,

on the 5.9Ghz frequency band licensed for VANET applications. We assume

that these broadcast messages are delivered in single-hop, using IEEE 802.11p

(or Dedicated Short Range Communications, DSRC) technology pursued by

industry and governments [18].

In VANETs, there are two types of transmission carried out by nodes:

event-driven messages, whose transmission takes place on the occurance of

certain well-defined events, such as the transmission of safety messages, and

periodic transmissions called beacons used for providing mutual awareness.

8


SVA, RHCN and EEBL as described in [4] are some examples of event-driven

messages that are transmitted by nodes, which are used for ensuring safety

of vehicles and also for better coordination among nodes. However, certain

convenience applications also employ event-driven messages. A beacon mes-

sage (denoted BM(v)), is a periodic message broadcasted by each node (‘v’ ),

and contains positional information, speed and direction of movement of the

node, as at the time of broadcast.

9

Chapter 3

Beacon Characterization

3.1 Need for Characterization

As was noted in section 2.2, the two predominant types of transmission car-

ried out by nodes in VANETs are:

1. Event-driven messages, whose transmission is trigerred by the occu-

rance or non-occurance of certain well-defined events at a node. These

comprise mainly of messages relating to on-road vehicle safety.

2. Beacon messages, are periodic transmissions by nodes, containing in-

formation about position and velocity of the node at the time of broad-

cast. They are used for providing mutual-awareness amongst nodes.

Most networking strategies adopted in VANETs, such as routing, transmis-

sion power control, network traffic congestion control etc, follow from tradi-

tional networking protocols. Hence, these networking strategies process all

messages in a similar manner unto the respective classes into which the mes-

sages are divided by them. However in context of detection and estimation

of vehicular congestion, we observe that differentiation amongst messages for

mutual-awareness is required to a great extent, as evident from the following

examples :

10

Chapter 3. Beacon Characterization

1. At any node, beacons received from nodes in a close range, convey more

information with regards to vehicular congestion than the reception of

same number of beacons from any arbitrary set of nodes over the same

fixed time period.

2. A continuous stream of beacons being received at a node would signify

an active link in the network in general, however in context of vehicular

congestion, it is indicative of close proxity of the sender node to the

receiver.

3. A node neighboring another node and bearing a lesser relative velocity

to it is far more likely to be in close proximity to it for a considerable

duration, and hence would be expected to contribute to the vehicular

congestion around the node.

4. Reception of a beacon from a node transmitting at a higher transmis-

sion power signifies that the beacon reaches out to a greater number

of nodes in the vicinity, in effect conveying more vehicular congestion

information to the receiver. This also ensures that the congestion es-

timation strategy is resistant to nodes following different transmission

power control protocols.

Thus, we see that a need for categorizing and characterizing the beacons

in terms of the relevance they bear for estimation of vehicular congestion, is

certain.

3.2 Approach towards solution

Based on the need for classification of beacon messages received at a node,

realized in the preceding section, we present a categorization of beacon mes-

sages. The classification is based on certain characteristics of beacons, inher-

ently based on the fact that any neighboring sender node, would also be the

neighbor for other nodes in a radial distance range, which is at least equal to

the distance between the sender and the node receiving the beacon message.

11


Senderdistancedv

Transmissionpower levelpv

Relativevelocityvrel

BeaconRelevance

Characterization

Low Low Low Most Relevant True neighborLow High or Low High Not Relevant Passing trafficLow High Low Moderately

RelevantPotential informersabout distant traf-fic.

High High Low Less Relevant Provide informersabout very distanttraffic

High High High Not Relevant Passing TrafficHigh Low High or Low Inconsistent Inconsistent

Table 3.1: Beacon Characterization

Further, the signal strength of the beacon message helps in determining the

signal strength at which the message was broadcast,and is an effective mea-

sure of the number of other nodes that would possibly be in the receiving

range, and hence the neighborhood, of the sender node. Finally, sender nodes

bearing mobility characteristics close to that of the receiver are more likely

to be a source of vehicular congestion for the receiver.

Table 3.1 describes the categorization in detail, by quantizing the var-

ious factors into states ‘high’ and ‘low’. The characterization we present

here, considers a beacon sender node with a lower relative velocity vrel, less

transmission power pv and in close spatial proximity to be a node which is

expected to remain in that state with respect to the receiving node, and

thus the two nodes are said to be ‘true neighbours’ of each other. Whereas,

sender nodes with higher relative velocity vrel, are not expected to remain

in close spatial proximity of the receiver for a considerable amount of time,

hence are classified as ‘passing traffic’. However, sender nodes with a lower

relative velocity vrel, transmitting at a higher transmission power level pv

are differentiated according to their respective proximity to the sender. Such

nodes are expected not only to be stable contributors of vehicular congestion

of the receiver, but also be in close network proximity of a larger number of

other nodes and be able to provide information about them, owing to their

12


higher transmission power level pv. Note that, sender nodes with higher

sender-receiver distance dv, but with a lower transmission power level are

inconsistent in terms of the two parameters and hence not considered for

characterization.

3.3 Beacon Relevance

In order to classify the beacons received at a node, and hence the associated

sender nodes, according to the characterization described above in real-time,

we assign a static weight to each received beacon. This static weight is a

measure of the ‘relevance’ the beacon bears in context of vehicular conges-

tion, and is used by the receiving node to differentiate nodes, according as

their contribution to congestion around it. As noted above the factors which

govern the relevance assigned to each received beacon BM(v), received at a

node u, broadcast by node v, are as follows :

1. The distance dv of the sender v from u,

2. The relative velocity vrel, defined as the magnitude of the relative ve-

locity vector between the sender and the receiver.

3. The transmission power level or signal strength at which the beacon

was broadcast, denoted as pv.

Formally, the relevance of a beaconBM(v) at a node u, denotedRel(BM(v)),

is defined as follows, and holds direct intuitive correlation with the charac-

terization as explained in section 3.2 :

Rel(BM(v)) =pv

vrel × du(2)

13

Chapter 4

Proposed Algorithm

4.1 Classes of Vehicular Congestion

In order to be able to detect and estimate vehicular congestion proficiently,

we classify the traffic congestion experienced at a node u into a set of classes

as follows :

• Instantaneous Congestion : The instantaneous picture of the traffic

in the vicinity at any instant, as perceived by node u. However, speed-

ing/overtaking vehicles are not to be considered as proficient members

of the instantaneous congestion , as they would have little or no con-

tribution to mobility of node u, in the actual present traffic scenario.

• Stabilized Local Congestion : Comprises of the neighboring vehi-

cles of node u, which have been stable members of the instantaneous

congestion at node u, for a considerable amount of time. Thus at any

instant the stabilized local congestion would always be a subset of the

instantaneous congestion.

• Global Congestion : Captures an estimate of the traffic scenario

around node u at a global scope. The estimate is further dependent

upon the stabilized congestion at that instance and the category, (F),

14

Chapter 4. Proposed Algorithm

(J) or (S) into which the present stabilized local congestion falls. We

assume that, an aggregation of local traffic scenarios at nodes in the

stabilized local congestion would be indeed the best possible first degree

estimate for global congestion at node u.

We assert that distribution of vehicular congestion into hierarchical classes,

as above, would aid in systematic measurement of congestion. Since the three

classes describe congestion at successive larger scopes, they can be suitably

combined to present an effective estimate of vehicular congestion surrounding

any node u.

4.2 Algorithm Description

The algorithm aims to characterize the beacons received at any node u by

means of assigning certain static relevance to the message received, as defined

in section 3.3. By means of this algorithm we intend to measure contribution

of congestion towards the different hierarchical classes as specified in the

preceding section.

In accordance with the distributed nature of the VANET system, the

algorithm, as formally described below, provides an autonomous computation

at each node in the network. In order to record the information received with

each beacon, each node maintains a data structure ‘D’, which stores the

content of the received beacon along with the time at which the message was

received. Also, with each such beacon record appended to D, it also stores

the relevance of the beacon. Thus each unit entry in D, can be stereotyped

as:

{Sender ID, Time of receipt, Beacon Relevance}

where, ‘Sender ID’ is the unique identity of the node sending the beacon,

‘Time of receipt’ is the time at which node u acknowledges the receipt of

the beacon, and associates with it its beacon relevance, computed with the

contents of the beacon and present state of the node.

15


Algorithm 4.1 Rx BM(BM(v)) : On receipt of beacon BM(v) at node u

Sv ← BM(v).speedXv ← BM(v).positionX

Yv ← BM(v).positionY

~Qv ← BM(v).direction

vrel ← |Sv. ~Qv − Su. ~Qu|dv ←

√(Xv −Xu)2 + (Yv − Yu)2

pv ← BM(v).(signal strength)

Rel(BM(v))← pvvrel × dv

if Rel(BM(v)) ≥ RelTH thenD ← D ∪ {v, t, Rel(BM(v))}

end ifreturn D

Algorithm 4.2 Update(u) : Periodic computation of congestion at node u

for all y ∈ D where y ≡ {yID, yt, yrel} doif (t− yt) > tTH thenD ← D \ {y}

end ifend forcount← count+ 1HD[count]← {D}IC(u)← {D}IDSLC(u)← GetSLC(D, k)GCE(u)← GetGCE(u)return { IC(u) , SLC(u) , GC(u) }

Algorithm 4.3 GetSLC(D,k) : Computation of stabilized local congestion

CD ← φfor j = (count) to (count− k + 1) doCD ← CD ∩HD[j]

end forreturn {CD}ID

16


Algorithm 4.4 GetGCE(u) : Computation of global congestion estimate

GCu ← φfor all x ∈ SLC(v) doGCu ← GCu ∪ SLC(x)

end forreturn GCu

Thus, each node is able to populate its corpus D, which it uses for further

computation, along with modifying it suitably to contain only records of

beacons received over a window of tTH time and having a minimum of RelTH

relevance. The parameters tTH and RelTH , capture the ‘response-time’ and

‘congestion- sensitivity’ of the algorithm respectively. The algorithm can be

divided into a set of three functions as follows, each of which co-ordinates

with the others to obtain the different classes of congestion:

1. Receipt of beacon: Denoted as Rx BM( ),these are the set of actions

to be taken on the receipt of a beacon message BM(v) at node u.

It appends the new record for the beacon received suitably, as well

overwrites existing entries, if present, for the same sender node.

2. Periodic update: Denoted as Update(u), it performs periodic up-

dates the collection D and obtains the various metric(s) associated

with the node u. Since, this method is called periodically at each node,

it forms the basis of the distributed computation being carried out

autonomously at each node. The metric(s) thus obtained classify the

traffic congestion across node u.

3. Congestion Measures: Denoted as IC(u), SLC(u) and GC(u), per-

form the congestion computations for the different measures of conges-

tion.

• Instantaneous congestion estimate IC(u) is computed after every

periodic update carried out at each node. It comprises of those

nodes which have sent beacons bearing relevance at least RelTH ,

and have sent a beacon to u once in the last tTH time units at

most.

17


• Stabilized local congestion estimate SLC(u), comprises of nodes

which have been a part of IC(u) for the at least the last k, up-

date steps performed at node u , and measures the set of stable

neighborhood members of u.

• Global congestion estimate GC(u), captures an estimate of the

global traffic scenario across node u. It makes use of one-hop query

and answer messages across neighboring nodes present in SLC(u),

and obtains an aggregation of the stabilized local congestion of

each of the nodes in its present in its stabilized local congestion.

4.3 Discussion

In this section we present a discussion on the expected congestion estimates

as produced by the algorithm in light of the three-phase traffic theory[3]

described briefly in section 2.1. We analyze on a case by case basis the

expected performance on each of the possible traffic flow scenarios :

1. Flowing Jam (J) : In such a scenario, each node is present in highly

dense traffic and neighboring nodes have similar mobility leading to

a similarity between the instantaneous and stabilized local congestion

estimate. Also, the global congestion estimate is expected to produce

a substantially enhanced view of the local congestion as neighboring

nodes are expected to be in similar congestion conditions.

2. Synchronized Flow(S) : The stabilized local congestion estimate is ex-

pected to be lower than the instantaneous congestion, owing to presence

of passing traffic. Since the global congestion estimate would compute

a macroscopic view it is expected to produce a distribution of nodes

connected by local-stable congestion relationship.

3. Free Flow (F) : As the traffic is sparse , the reported local congestion

estimate is equivalent to the instantaneous congestion . Since negligi-

ble amounts of passing traffic exists in such a scenario, the connected

18


neighbor set is expected to be stable with time and space.

4. Transition between (S → J) : Although the instantaneous congestion

would be high as compared to the stabilized local congestion, a gradual

stabilization of nodes from instantaneous to stabilized local congestion

is expected, that is an increase in the stabilized local congestion. Such

a scenario is characterized by EEBL message disseminations[4], and

their receipt can be utilized to normalize the congestion computation,

in order to better cope with the changing scenario. Similarly for a

J → S transition, although the stabilized local congestion values are

expected to be higher , but an upfront S scenario would take some time

to provide the correct congestion estimate , until which the node would

assume itself to be in congested traffic but for a duration, depending

upon the parameter tTH .

5. Transition between (F → S) : In such a case, the instantaneous and

stabilized local congestion would be a smaller set of nodes that have

been in close proximity of the reference node, also causing the global

congestion estimate to be similar, and hence an accurate global conges-

tion estimate of the upfront traffic (S), does not seem to appear. Such

a scenario is characterized by an increase by both in instantaneous

and stabilized congestion with time and SVA / RHCN messages[4] are

expected to be disseminated by the upfront nodes, present in the rela-

tively denser traffic.

Note that, computation of the stabilized local congestion GetSLC(), is a

terminating computation over the set HD, which is defined as history of the

set of stabilized local congestion.The computation involves comparison of

the last k versions of stabilized local congestion obtained at previous update

events. Similarly, computations performed on receipt of every beacon mes-

sage Rx BM(), and periodic events to update congestion across the node u,

are also bounded computations. Similarly, getGCE() computes a bounded

set, which is an aggregation over the stable neighbor set and thus its com-

putation is bounded as well.

19

Chapter 5

Simulation Study

5.1 Simulation Setup

The network simulator employed in the experiments is NS[7]-version 2.28,

and VANET Mobisim[8]-version 1.1 is used as the traffic simulator to simu-

late the dynamic traffic scenario and mobility patterns of the vehicles. The

movement pattern of the vehicles, generated by the traffic simulator, in terms

of the current position, speed and direction, is fed into the network simulator,

used to simulate the associated dynamic network topology over the VANET

at real time. This process is automated by means of a data-pipe between

the two simulators and a server-client, two-way communication takes place

between the traffic and network simulators which synchronize the flow of

data amongst themselves and run in parallel at run time. The detailed spec-

ification and implementation of this integrated simulator can be found in

[19].

The network simulator effectively simulates the characteristic mobility

pattern of moving traffic by periodically seeking position and velocity data

of each node from the traffic simulator. The value of this periodic time step,

VMStime−interval , is kept appropriately “small” so as to effectively report

movement and changes in the movement pattern of a node to the network

simulator without substantial delay, and is commensurate to the granularity

20

Chapter 5. Simulation Study

Channel : WirelessPropagation : Two Ray GroundMac : Mac/802 11Queue : Drop Tail / Priority QueueAntenna : Omni Antenna (Ht. 1.5m)Transmission Power : 0.2818dBm (250m)Frequency : 914 Hz

Table 5.1: Network parameters

of the latter. All nodes in a scenario being studied, are associated with a sin-

gle “node group” and hence the update process of their position and velocity

for all nodes is done concurrently with little time-lag. Also, in accordance

with the beaconing model of communication in VANETs, each node sends

out periodic beacons, consisting of its most recent position and velocity, and

are notably used for mutual awareness. Since, the time granularity of the

simulation is indeed VMStime−interval , the beacon interval is always equal or

a multiple of it. The other network parameters are summarized in table-5.1.

5.2 Parameter Initialization

The time interval at which the updated position and velocities of the nodes

are read by the network simulator, VMStime−interval, is set to 1 second, and all

other algorithm parameters are varied, measured and studied in its respect.

Hence, the absolute values of the parameters bear little significant and all

analysis and performance evaluation of the algorithm is conducted by varying

parameters in multiples of VMStime−interval. This also contributes to the fact

that the results are independent of the absolute values of parameters, since

a change in the value of VMStime−interval, would just indicate a changed

granularity of the observed mobility pattern of the vehicles, as perceived by

the network simulator followed by an associated increase in the number of

beacons and other network-related events occurring in the VANET.

21


The parameters associated with the algorithm are as follows :

1. Beacon Period (Bp) : The time interval between successive beacon

events occurring at given node. To ensure homogeneity in the VANET,

without loss of generality, it is assumed to be the same for all nodes in

a given traffic scenario.

2. Update Period (Up) : The time interval between successive and periodic

execution of Update(u) at a node, which is assumed to be same for all

the nodes in a given traffic scenario. It can also be defined as the

time-frequency of “updation” of vehicular congestion estimates.

3. Threshold Time (tTH) : At any node u, the maximum amount of time

difference between the current time and the time of receipt of the last

beacon from any neighboring node v, which is reported in the instan-

taneous congestion (IC) at node u.

4. Window Size (K) : At any node u, the number of most recent successive

instances of the instantaneous congestion (IC), which need to contain a

node v which is reported as a member of the stabilized local congestion

(SLC) at node u.

5. Threshold Relevance (RelTH) : The minimum amount of relevance a

beacon needs to possess in order for the corresponding sender node to

be considered, as a member of the instantaneous congestion (IC) and,

hence a potential member of the stabilized local congestion(SLC).

6. Total number of nodes (n) : The total number of nodes in the given

scenario. All results are averaged over the entire set of nodes in the

scenario.

An empirical value ‘α’ of RelTH is calculated as follows :

At any node u, let BM(v) be a beacon message received from a sender node

v, hence Relevance of the beacon Rel(BM(v)), as defined in section 3.3 is,

Rel(BM(v)) =pv

(vrel + δ)× dv

22


VMStime−interval : 1 secBeacon period (Bp) : 5 secThreshold time (tTH) : 20 secRelTH : 0.0022544Update period (Up) : 15 secWindow Size (K) : 3

Table 5.2: Default values of parameters

Note that, we augment a constant factor δ here, so as to counter cases of

neighboring nodes moving with same or negligibly different speeds. In order

to obtain RelTH , we assign values to pv, vrel and dv :

pv = 0.2818dBm (3)

(Corresponding to a transmission range of 250m, and assumed equal for all

nodes).

vrel = 1m/sec (4)

(Corresponding to a distance of 5m, which a car would cover relative to

another, and is equal to the assumed length of the vehicle).

dv = 125m (5)

(Assuming any vehicle should be within at least half the transmission range

of another node in order to be a contributor for vehicular congestion at that

node).

Therefore, using (2),

α =0.2818

1× 125= 0.0022544 dBm/m2/s. (5)

The parameters ‘default’ values for these parameters chosen are shown in

table-5.2.

23


5.3 Performance Evaluation

For both the stabilized local congestion(SLC) and instantaneous congestion

(IC), two performance measures are studied, namely Precision and Recall of

the reported congestion, defined as follows :

1. Precision : The percentage of nodes in the reported congestion (IC

or SLC) of any node u, which would produce a beacon with relevance

greater than RelTH with respect to node u, if they were to send one

beacon message each to u at the instant at which the vehicular conges-

tion (being evaluated), was reported.

For any node u, let ε denote the set of all the nodes within its transmis-

sion range, which would produce a beacon with relevance greater than

RelTH with respect to any node u, if they were to send a beacon to u

at the instant at which the set is computed. The number of nodes in

ε is denoted by |ε|. Similarly, let ϕ be the set of nodes in the reported

congestion. Note that, ϕ will be equal to either SLC or IC. The number

of nodes in ϕ is denoted by |ϕ|. Therefore,

2. Recall : The fraction|ϕ||ε|

, expressed in percent.

Note that, these measures are studied for both the stabilized local and

instantaneous congestion, and their variation with other factors inherent in

the algorithm, as well as the total number of vehicles, in the traffic scenario

under observation.

In order to analyze the Global Congestion(GC) estimate, the distribution the

average cardinality of the reported Global Congestion(GC), with respect to

the fraction of the total nodes in the scenario is studied.

5.4 Experiments

Three traffic scenarios are considered for the simulation study of the algo-

rithm, each of which corresponds to Synchronized Flow(S), Wide Moving

24


Jam(J) and Free Flowing(F) traffic. However, due to the dynamics of the

traffic at run time, transitional traffic scenarios, namely, S→J and S→F, are

also analyzed. An average case analysis is done by averaging the observables

over all nodes present in the scenario. Also, each scenario is evaluated at two

values of the total number of nodes, in order to ensure consistency in the

average case analysis. Presented below are run-time snapshots of the traffic

scenarios considered for the performance evaluation followed by which the

study of the precision and recall for the scenarios as well as their sensitivity

analysis with respect to other variables.

5.4.1 Scenario 1 : Wide moving jam (J)

This scenario consists of intersecting roads, with two lanes per driving direc-

tion, and a uniform maximum speed on all road however speed of vehicles

varies, which causes heavy congestion and cluster formation near intersec-

tions. Note that, at other road stretches in the scenario, the observed the

traffic during run time is in transition from (S→J) and vice versa.

A run-time snapshot of the scenario is present in figure-5.1. The figure-

5.2, illustrates the precision and recall of the stabilized Local Congestion(SLC)

versus the update period(Up), which is varied in multiples of the beaconing

interval, other parameters are at default values, as given in table-5.2.

Figure 5.1: Run-time snapshot of scenario-1

25


50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11

Pre

cisi

on(in

per

cent

) of

SLC

Update Period(in multiples of Beacon period)

Precision(in percent) of SLC v/s Update period

n=100n=50

0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11

Rec

all(i

n pe

r ce

nt)

of S

LC


Recall(in per cent) of SLC v/c Update Period

n=100n=50

Figure 5.2: Precision and Recall of the Stabilized Local Congestion(SLC)versus the Update period for scenario-1

26


We see that the measured precision increases dominantly as the update period

is increased. This is attributed to the fact that, as more and more time is

allocated for the congestion to converge, its accuracy is bound to increase.

Similarly, the measured recall decreases with update period, owing to the

fact that with the increased time allocated for convergence of congestion,

newer nodes may enter the congestion and be un-reported due to the lesser

time spent in vicinity of the given node, with respect to which evaluation is

performed. However, in the given traffic scenario which is clustered in nature,

there exist certain vehicles which tend to be part of different clusters over

time, and thus if reported as a member of congestion, they are liable to be in a

different cluster at the time of performance evaluation, and hence result in the

corresponding decrease in measured precision. Likewise, a similar behavior

of occurrence of anomalies is also observed in the variation of recall.

Figure-5.3 illustrates the precision and recall of the stabilized local con-

gestion(SLC) versus the window size(K), which is varied in multiples of the

beaconing interval, other parameters are at default values, as given in table-

5.2. The increase in the precision observed as the window size(K) is increased,

owes to the fact that as more number of instances of instantaneous conges-

tion(IC) are considered for estimating congestion, only the “true” neighbor

nodes form a part of the reported congestion and hence, the accuracy in-

creases. However, in the given traffic scenario which is clustered in nature,

there exists an tentative amount of “time” for which any node stays in the

vicinity and hence forms a part of the congestion, and a window size(K)

corresponding to this optimal ‘time’ value yields the maximum measured

Precision. Thus, further increasing the window size(K) beyond the corre-

sponding optimal ‘time’ value leads to a decreased accuracy, as some of the

nodes reported to be a part of congestion are liable to be elsewhere in VANET

owing to the dynamism of the traffic scenario.

Figure-5.4, illustrates the precision and recall of the stabilized Local Conges-

tion(SLC) versus the threshold beacon relevance(RelTH), varied on a loga-

rithmic scale with respect to the value α (0.0022544), other parameters are

27


at default values, as given in table 5.2.

40

50

60

70

80

90

100

0 1 2 3 4 5 6

Pre

cisi

on(in

per

cent

) of

SLC

Window size(K)

Precision(in percent) of SLC v/s Window size(K)

n=100n=50

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6

Rec

all(i

n pe

r ce

nt)

of S

LC

Window size(K)

Recall(in per cent) of SLC v/c Window Size(K)

n=100n=50

Figure 5.3: Precision and Recall of the Stabilized Local Congestion(SLC)versus the Window Size(K) in scenario-1

28


0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Pre

cisi

on(in

per

cent

) of

SLC

Threshold Beacon Relevance(RelTH)

Precision(in percent) of SLC v/s Threshold Beacon Relecane(RelTH)

n=100n=50

0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Rec

all o

f SLC

(in

per

cent

)


Recall(in percent) of SLC v/s Threshold Beacon Relecane(RelTH)

n=100n=50

Figure 5.4: Precision and Recall of the Stabilized Local Congestion(SLC)versus the Threshold Beacon Relevance(RelTH) in scenario-1

29


Clearly, both the precision and recall are expected to increase as the thresh-

old relevance(RelTH) is increased, and the same is observed as well. How-

ever, as threshold relevance(RelTH) is decreased (on the logarithmic scale),

the measured precision and recall increases because a very low threshold

relevance(RelTH) causes all nodes in the vicinity to be reported in the con-

gestion, as any given beacon is bound to possess a relevance greater than such

a low value of threshold relevance(RelTH). And because the evaluation of

precision and recall considers only the nodes in the vicinity of the given node,

it leads to a pseudo-increase in the measure values. Note that, there exists

considerable difference in the recall values for the two sets of nodes, because

a increased number of nodes increases the vehicle-density in the scenario.

Figure-5.5 presents the distribution of the number of nodes reported in

the global congestion(GC) with respect to the fraction of nodes.

0

0.05

0.1

0.15

0.2

0.25

0.3

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Fra

ctio

n of

Nod

es

Number of nodes in GC

(Fraction of nodes) v/s (Number of Nodes in GC)

n=50n=100

Figure 5.5: Distribution of the number of nodes reported in the Global Con-gestion(GC) in scenario-1

30


It is evident that for the given two sets of nodes, for which the analysis is

conducted, a greater number of nodes(n=100), leads to a more sparse dis-

tribution, as compared to the case where number of nodes is lower(n=50),

which has a uniform but dense distribution. Also, the occurrence of peaks (in

fraction of nodes) at certain number of nodes in global congestion(GC), re-

lates to the ‘clustered’ nature of the traffic in the given scenario, where traffic

occurs in clusters juxtaposed by free-flowing or moderately dense traffic.

The variation of Precision of the instantaneous congestion(IC) with the

update period and threshold relevance(RelTH) respectively, is presented in

figures-5.6 and 5.7 respectively.

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11

Pre

cisi

on(in

per

cent

) of

IC


Precision(in percent) of IC v/s Update period

n=100n=50

Figure 5.6: Variation of Precision of the Instantaneous Congestion(IC) withthe Update period in scenario-1

31


0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Pre

cisi

on(in

per

cent

) of

IC


Precision(in percent) of IC v/s Threshold Beacon Relevance(RelTH)

n=100n=50

Figure 5.7: Variation of Precision of the Instantaneous Congestion(IC) withThreshold Relevance(RelTH) in scenario-1

5.4.2 Scenario 2 : Synchronized Traffic Flow

This scenario consists of intersections which are juxtaposed with connecting

roads with greatly varying maximum speed limits and two lanes per driving

direction. The roads with lower speed limits are shorter in length and cause

a bottleneck in the traffic merging from higher speed limit roads, leading to

the formation of a synchronized flow of traffic. Note that, the scenario also

encompasses a transition of traffic from (S→F) and vice versa, owing to vast

stretch of higher speed limit roads.

A run-time snapshot of the scenario is present in figure-5.8. The figures-

5.9 and 5.10, illustrate the precision and recall of the stabilized local con-

gestion(SLC) versus the update period, which is varied in multiples of the

beaconing interval, other parameters are at default values, as given in table

5.2.

32


Figure 5.8: Run-time snapshot of the scenario-2

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11

Pre

cisi

on(in

per

cent

) of

SLC



n=100n=50

Figure 5.9: Precision of the Stabilized Local Congestion(SLC) versus theUpdate period in scenario-2

33


0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11

Rec

all(i

n pe

r ce

nt)

of S

LC



n=100n=50

Figure 5.10: Recall of the Stabilized Local Congestion(SLC) versus the Up-date period in scenario-2

The explanation for this variation is similar to the corresponding variation in

scenario 1. However, it is observed that the deviation of anomalies from the

expected behavior is reduced as compared to scenario-1, which is attributed

to the reduced clustered topology of the traffic in the given scenario, which

is dominated by a synchronized flow of traffic.

In this type of scenario, owing to no distinct formation of active clusters

of traffic at run-time, there does not exist an tentative value of “time” a node

spends in the vicinity of another, due to which we observe that the precision

value increases with increased window size(K). Likewise, the recall value

decreases because a greater window size leads to the exclusion of “newer”

nodes from the reported congestion, thereby recall decreases with window

size(K). Figure-5.11 presents the associated variation.

34


40

50

60

70

80

90

100

0 1 2 3 4 5 6

Pre

cisi

on(in

per

cent

) of

SLC

Window size(K)


n=100n=50

0

20

40

60

80

100

0 1 2 3 4 5 6

Rec

all(i

n pe

r ce

nt)

of S

LC

Window size(K)


n=100n=50

Figure 5.11: Precision and Recall of the Stabilized Local Congestion(SLC)versus the Window Size(K) in scenario-2

35


The figures-5.11 and 5.12, illustrate the precision and recall of the stabi-

lized local congestion(SLC) versus the threshold beacon relevance(RelTH),

varied on a logarithmic scale with respect to the value α (0.0022544), other

parameters are at default values, as given in table-5.2.

0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Pre

cisi

on(in

per

cent

) of

SLC


Precision(in percent) of SLC v/s Threshold Beacon Relevance(RelTH)

n=100n=50

Figure 5.12: Precision of the Stabilized Local Congestion(SLC) versus theThreshold Beacon Relevance(RelTH) in scenario-2

Although the variation follows similar trend, as in case of the corresponding

variation in scenario-1, however, a greater difference is observed in the pre-

cision and recall values of the two node sets because a two-fold increase in

the number of nodes increases the vehicle-density in the synchronized flow

scenario and affects the traffic topology more adversely, leading to cluster-

formation in the scenario and consequent fall in the Precision and Recall

values.

36


0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Rec

all o

f SLC

(in

per

cent

)



n=100n=50

Figure 5.13: Recall of the Stabilized Local Congestion(SLC) versus theThreshold Beacon Relevance(RelTH) in scenario-2

Corresponding to figure-5.5, figure-5.14 presents the distribution of the num-

ber of nodes reported in the global congestion(GC) with respect to the frac-

tion of total nodes in the scenario. A more sparse distribution of the number

of nodes in the global congestion(GC), is evidently observed due to the traf-

fic topology in the scenario which is more uniformly distributed as compared

to clustered traffic and hence, equal size peaks are observed at different val-

ues, corresponding to the relative positions of nodes in a bottleneck-traffic

environment.

Finally, the variation of Precision of the instantaneous congestion(IC)

with the update period and threshold relevance(RelTH) respectively, is pre-

sented in figures-5.15 and 5.16. Similar to the variation observed in the stabi-

lized local congestion(SLC), the measure precision increases with the update

Period, and characteristic presence of anomalies is also observed. Note that,

the gradient of increase in lower in case of instantaneous congestion(IC).

37


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Fra

ctio

n of

Nod

es



n=50n=100

Figure 5.14: Distribution of the number of nodes reported in the GlobalCongestion(GC) in scenario-2

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11

Pre

cisi

on(in

per

cent

) of

IC



n=100n=50


38


0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Pre

cisi

on(in

per

cent

) of

IC



n=100n=50


5.4.3 Scenario 3 : Low Congestion

This scenario consists of straight roads with uniform maximum speed, 3-4

lanes per driving direction and hence lead to appearance of low congestion

in the traffic. However, the speed of individual vehicles varies, resulting to

a predominant free flow traffic supplemented with low cardinality clusters at

run time.

A run-time snapshot of the scenario is presented in figure-5.17. Figures-

5.18 and 5.19, illustrate the precision and recall of the stabilized local con-

gestion(SLC) versus the update period, varied in multiples of the beaconing

interval, other parameters are at default values, as given in table 5.2.

39


Figure 5.17: Run-time snapshot of scenario-3

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11

Pre

cisi

on(in

per

cent

) of

SLC



n=100n=50

Figure 5.18: Precision of the Stabilized Local Congestion(SLC) versus theUpdate period in scenario-3

40


0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11

Rec

all(i

n pe

r ce

nt)

of S

LC



n=100n=50

Figure 5.19: Recall of the Stabilized Local Congestion(SLC) versus the Up-date period in scenario-3

40

50

60

70

80

90

100

0 1 2 3 4 5 6

Pre

cisi

on(in

per

cent

) of

SLC

Window size(K)


n=100n=50

Figure 5.20: Precision of the Stabilized Local Congestion(SLC) versus theWindow Size(K) in scenario-3

41


20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6

Rec

all(i

n pe

r ce

nt)

of S

LC

Window size(K)


n=100n=50

Figure 5.21: Recall of the Stabilized Local Congestion(SLC) versus the Win-dow Size(K) in scenario-3

Likewise, figure-5.20 and 5.21 present precision and recall of the stabilized

local congestion(SLC) with the window size(K).

In all variations, we observe a reduced gradient of increase and decrease

in the precision and recall values, which can be attributed to the low vehicle

density prevailing in the scenario, which does not contribute considerably

to the congestion estimation, thereby the gradient of increase in precision

observed is low.

Also, in low congestion we observe that the precision and recall for both

instantaneous congestion(IC) and stable local congestion(SLC) nears to a

constant even with changing threshold relevance(RelTH) as in figure-5.22,

5.24 and 5.25. Similar is the case variation with changing window size(K).

42


0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Pre

cisi

on(in

per

cent

) of

SLC


Precision(in percent) of SLC v/s Threshold Beacon Relevance(RelTH)

n=100n=50

0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Rec

all o

f SLC

(in

per

cent

)



n=100n=50

Figure 5.22: Precision and Recall of the Stabilized Local Congestion(SLC)versus the Threshold Beacon Relevance(RelTH) in scenario-3

43


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

3 4 5 6 7 8 9 10 11 12 13 14 17 18 19 20 21

Fra

ctio

n of

Nod

es



n=50n=100

Figure 5.23: Distribution of the number of nodes reported in the GlobalCongestion(GC) in scenario-3

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11

Pre

cisi

on(in

per

cent

) of

IC



n=100n=50


44


0

20

40

60

80

100

2.2544e-05 0.00022544 0.0022544 0.022544 0.22544 2.2544 22.544 225.4

Pre

cisi

on(in

per

cent

) of

IC



n=100n=50


The variation of the distribution of the global congestion(GC) is observed to

be lesser than that in the case of scenario-1 and scenario-2.

45

Chapter 6

Applications

We believe that the stabilized local congestion estimate as computed by the

algorithm can be put to use in many applications, such as beacon frequency

control and transmission power control , as suggested by Torrent-Moreno in

[5], dynamic transmission power assignment to nodes can also be used to

control vehicular congestion.

Vehicular congestion can also be used in an application of trust building

notions among the nodes, so as to make the VANET system insusceptible

to effects of a single misbehaving attacker. The instantaneous congestion is

essential for information and emergency-message dissemination[4], so as to

relay the hazardous scenario information to a maximal number of nodes in

proximity. A global congestion estimate shall be appropriate for lower-layer

application in other convenience applications[4] such as providing alternate

route and providing credible foresighted view of the upfront traffic to the

node.

On the other hand, the congestion estimates can also be put to use for

verifying credibility of secondary information being broadcast by nodes in

proximity[20]. In the following section, we present an application of ve-

hicular congestion estimates in disseminating information over the VANET,

initialized from a single node.

46

Chapter 6. Applications

6.1 Application : Information Dissemination

Local danger warning (LDW) is one of the most promising active safety ap-

plications[21] in vehicular ad-hoc networks (VANETs). Vehicles exchange

information about the current road condition and dangerous situations and

are able to warn their drivers about upcoming dangers. Any vehicle, equipped

with sensors initiates dissemination of the relevant information to nodes and

the aim is to arrive at maximum node coverage over the VANET, with least

possible redundancy, and hence least packet loss. Information dissemination

in particular sets up many requirements that are not entirely fullled by ex-

isting concepts, as it requires co-ordinated action based upon a macroscopic

and microscopic view of the surrounding traffic at a node.

We describe two approaches for dissemination of information over a VANET,

initialized from a single node, based upon vehicular congestion estimation.

Also, we present a comparative analysis of our approach with the p-persistent

broadcast and the flooding-based approach for dissemination of information.

6.1.1 Probability based Approach

In this approach, a broadcast probability is calculated by the receiver to decide

whether it should rebroadcast or not.

Naive Approach

Each node, on receiving information broadcasts with a fixed probability p,

except the initiator which broadcasts irrespective of the value of p.

Using Congestion

Any traffic scenario, for example, a typical highway scenario can be consid-

ered as a superimposition of a high, low and synchronized flowing traffic,

and thus, a constant broadcast probability over all nodes causes either : i) a

47


great deal of redundant broadcast or ii) reduces the number of nodes the in-

formation reaches finally. The required probability of broadcast(forwarding)

by nodes for optimality, varies across the scenario, ranging from low in areas

of high congestion to high in low congestion. Given that each node has a well

defined set of stable neighbors SLC and instantaneous neighbors IC, associ-

ated with it, it is to be noted that, both, nodes present in clustered traffic

with high density(high congestion), and nodes present in very low conges-

tion, are most likely to have most of their instantaneous neighbors as stable

ones. Broadcast by all nodes in low congestion is essential for propagation of

information, however, all nodes in high congestion do not need to broadcast,

so as to avoid sending of redundant messages. Thus, we assert that a bal-

anced value of broadcast probability based on the number of instantaneous

neighbors which are stable should be used, as it is a specific characteristic

of the required broadcasting nodes. Based on this, we define the broadcast

probability as follows :

Broadcast Probability p′ = p+ (1− p)( |SLC| ∩ |IC||IC|

)

= p+ (1− p) |SLC||IC|

[Since, |SLC| ⊆ |IC|] (6)

6.1.2 Deterministic Approach Using Vehicular Con-

gestion

As noted in the preceding section, the number of instantaneous neighbors

which are stable, which is based on the local view of traffic at a node plays

a key role in determining the rebroadcast. In this approach we avoid prob-

abilistic broadcast of nodes by introducing for each node, a certain measure

(bcast), which should be greater than a given threshold(bcastTH) for the

node to propagate information received by broadcast. The bcast value of

the sender is propagated along with the information message, and can be

used to better estimate the extent to which the information has already been

propagated, and hence, redundant dissemination can be avoided.

48


If a node u receives an information message from sender node v,

bcastu = q|SLC||IC|

+ (1− q)bcastv, (7)

where q is a constant.

Node u, broadcasts, iff,

bcastu ≥ bcastTH

The initiator broadcasts the information message with an enclosed bcast value

of 1.0 in the information message. q provides for a weighted sum between

the local view of the node, as well as the global tendency to broadcast. Note

that, q with a value of 0 corresponds to the traditional flooding strategy,

whereas a value of 1, corresponds to controlled flooding using only the local

congestion information at each node.

6.1.3 Experiments

In this section we present simulation results for the information dissemination

approaches discussed in the preceding section. We use the same system model

and setup as described in chapter 5-section 5.1. We analyze a sample highway

scenario(4km stretch with 2 lanes per direction), and 200 nodes are shown

below. All results are averaged over a randomly selected set of 20 (n/10)

nodes which initialized the broadcast at independent instances. Figure-6.1

shows the observed variations in the number of nodes reached and number

of redundant messages with p, for the probability based approach. Shown

in red, green and blue are the results for flooding, p-persistent broadcast

and congestion governed probabilistic broadcast respectively. We see that

although our approach increases the number of redundant messages over the

simple p-persistent broadcast, however, a considerable increase in the number

of nodes reached is also observed.

49


0

10

20

30

40

50

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Num

ber

of n

odes

rea

ched

Broadcast probability(p)

Number of Nodes reached v/s Broadcast probability

Floodingpp’

0

100

200

300

400

500

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Num

ber

of r

edun

dant

mes

sage

s

Broadcast probability(p)

Number of redundant messages v/s Broadcast probability

Floodingpp’

Figure 6.1: Simulation results for information dissemination using the prob-ability based approach

50


0

10

20

30

40

50

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Num

ber

of n

odes

rea

ched

q

Number of nodes reached v/s q

FloodingDeterministic flooding with Broadcast threshold=0.5

250

300

350

400

450

500

550

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Num

ber

of r

edun

dant

mes

sage

s

q

Number of redundant messages v/s q

FloodingDeterministic flooding with Broadcast threshold=0.5

Figure 6.2: Simulation results for information dissemination using the deter-ministic approach : Variation with q ;Broadcast threshold (bcastTH) is set to0.5

51


Figure-6.2 and figure-6.3 show the observed variations in the number of nodes

reached and number of redundant messages with q and Broadcast threshold

(bcastTH) respectively, in case of the deterministic approach. Shown in red

and green are the results for flooding and congestion governed broadcast

respectively. We observe that the number of redundant messages decreases

as q is increased from 0 to 1. Note that, q with a value of 0 corresponds to the

traditional flooding strategy, whereas a value of 1, corresponds to controlled

flooding using only the local congestion information at each node.

52


0

10

20

30

40

50

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Num

ber

of n

odes

rea

ched

Broadcast threshold

Number of nodes reached v/s Broadcast threshold

FloodingControlled flooding with p=0.5

0

100

200

300

400

500

600

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Num

ber

of r

edun

dant

mes

sage

s

Broadcast Threshold

Number of redundant messages v/s Broadcast threshold

FloodingControlled Flooding with p=0.5

Figure 6.3: Simulation results for information dissemination using the deter-ministic approach : Variation with Broadcast threshold (bcastTH) ; Broad-cast probability (p) is set to 0.5

53

Chapter 7

Conclusion

This thesis demonstrated that how vehicular congestion estimation is aided

by means of VANETs and that it can be applied in many convenience and

safety applications targeted for VANETs. The preceding three chapters

have shown how the characteristic mobility pattern associated with nodes

in VANETs are a key factor in countering the extreme instability of links

between nodes in a VANET, as well as to establish the fact that congestion

estimation is an application which requires to differentiate between messages

of the same ‘type’ and hence differentiate their processing. The contributions

of this thesis have been threefold :

1. We assert that a need for characterizing messages according to the

‘importance’ or ‘relevance’ they bear for the application in question is

certain, as far as vehicular congestion estimation is concerned.

2. We have demonstrated how certain characteristics associated with spa-

tial mobility of a node coupled with the message transference over the

network may be used to measure quantitatively, vehicular congestion

surrounding a node.

3. We have shown how vehicular congestion surrounding a node can be

applied for dissemination of information, initialized from a single active

node, over the VANET.

54

Chapter 7. Conclusion

7.1 Future Work

Both convenience and safety applications over VANETs might benefit from

further research. Future work on vehicular congestion estimation might pro-

ceed along the following lines :

1. Although, an empirical calculation of the parameters involved is pre-

sented, the exact method of their determination in context of the

present surrounding traffic scenario of a node has been left un-specified.

Further work might investigate their correlation with other network and

mobility characteristics and a dynamic way to ‘learn’ them as the node

progresses through the traffic.

2. A more effective and efficient estimation technique could be developed

for estimation of global congestion of node. We suggest that exchange

of local congestion estimates to a reduced degree, by means of piggy-

backing may be employed to achieve the same.

3. The congestion estimation strategy can be treated as a lower layer, and

hence integrated with collaboration and misbehavior detection proto-

cols.

Further research into information dissemination over VANETs using vehicu-

lar congestion, might consider the following lines of inquiry :

1. While it has been established that a probability based approach using

vehicular congestion performs better in terms of the node coverage,

however, the redundancy increases as well. The exact nature of the

cause of this unseemingly feature needs to be investigated.

2. A deterministic approach using local information to infer the global in-

formation propagation pattern is shown potentially to be more effective

in propagation of information, however, on receipt of information at a

node, the local congestion can also be used in a ‘reverse-lookup’ way,

so as to determine the contribution that the sender would have had in

55

Chapter 7. Conclusion

propagating the information, and hence arrive at an adaptive decision

for broadcast accordingly.

56

Bibliography

[1] Haberman, R. 1977. Mathematical models: Mechanical vibra-

tions,population dynamics and traffic flow : An introduction to applied

mathematics .Engelwood Cliffs,N.J.,Prentice-Hall.

[2] M.Artimy, W.Robertson, and W.Philips, “Assignment of Dynamic

Transmission Range Based on Estimation of Vehicle Density”, in Pro-

ceedings of the 1st ACM International Workshop on Vehicular Ad Hoc

Networks[VANET], Philadelphia,Pennsylvania,Oct,2004.

[3] Kerner, Boris S. (December 2004). The Physics of Traffic: Empirical

Freeway Pattern Features, Engineering Applications, and Theory. Berlin:

Springer

[4] Fan Bai, Tamer ElBatt, Gavin Holland, Hariharan Krishnan, Varsha

Sadekar,“Towards Characterizing and classifying communication-based

Automotive Applications from a Wireless Networking Perspective”.

[5] M. Torrent-Moreno, P.Santi and H.Hartenstein, “Fair Sharing of Band-

width in VANET ”,in Proceedings of the 2nd ACM International Work-

shop on Vehicular Ad Hoc Networks (VANET), Cologne,Germany,

September 2005.

[6] “Car2Car Communication Consortium,” http://www.cat-to-car.org/.

[7] “Network Simulator ns-2”, http://www.isi.edu/nsnam/ns/.

[8] “VanetMobisim”, http://vanet.eurecom.fr/.

57

Bibliography

[9] Hall, F. L. “Traffic stream characteristics. In Traffic flow theory: A state

of the art report” - revised monograph on traffic flow theory, Oak Ridge,

Tennessee, Oak Ridge National Laboratory - 1997.

[10] D. Helbing, “Criticism of three-phase traffic theory”. Transportation

Research Part B: Methodische 43.

[11] Chen, Z. D., Kung, H. T. and Vlah, D. 2001. “Ad hoc relay wireless net-

works over moving vehicles on highways”, in Proceedings of the 2nd ACM

International Symposium on Mobile Ad Hoc Networking & Computing,

Long Beach, CA, USA.

[12] Kosch, T., Schwingenschlogl, C. and Li Ai. 2002. “Information dissem-

ination in multihop inter-vehicle networks”, in Proceedings of IEEE 5th

International Conference on Intelligent Transportation Systems.

[13] Rudack, M., Meincke, M., Jobmann, K. and Lott, M. 2003.“On traffic

dynamical aspects of inter vehicle communications (IVC)”, In Proceed-

ings of IEEE 58th Vehicular Technology Conference.

[14] Rudack, M., Meincke, M. and Lott, M. 2002.“On the dynamics of ad

hoc networks for inter vehicles communications (IVC)”. In Proceedings

of the 2002 International Conference on Wireless Networks. Las Vegas,

USA.

[15] Fubler, H., Mauve, M., Hartenstein, H., Vollmer, D. and Kasemann M.

“A comparison of routing strategies in vehicular ad-hoc networks” - 2002.

[16] Ghazy Ali and Ozkul Tarik, “Design and simulation of an artificially

intelligent VANET for solving traffic congestion”.

[17] Padron, Francisco M., “Traffic congestion detection using VANET ”.

[18] Wireless Access in Vehicular Environment (WAVE) in Standard 802.11

Information Technology Telecommunications and Information Exchange

Between Systems, Local and Metropolitan Area Networks, Specific Re-

quirements, Part 11: Wireless LAN Medium Access Control(MAC) and

Physical Layer(PHY) Specifications, IEEE 802.11p/D1.0, Feb. 2006.

58

Bibliography

[19] Vinu Rajshekhar, “An Integrated Traffic and Network Simulator for Re-

alistic VANET Simulation”, Bachelors thesis, IIT Kharagpur, May 2009.

[20] A. Vulimiri, A. Gupta, P. Roy, S. Muthaiah, and A. Kherani, “Applica-

tion of secondary information for misbehavior detection in VANET ”.

[21] T. Kosch, “Local Danger Warning Based on Vehicular Ad-hoc Networks:

Prototype and Simulation”.

59

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