2. D.istributed clustering algorithm-ppt - UOIT.CA

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25 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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A Distributed Clustering Algorithm for
Target Tracking in Vehicular Ad
-
Hoc
Networks


Dr.
Khalil

El
-
Khatib
, Dr. Richard
Pazzi
,
Sanaz

Khakpour


Table of Contents





-

Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion

Introduction



VANETs

are

network

of

autonomous

mobile

nodes

that

communicate

with

each

other

without

any

fixed

infrastructure
.




VANETs

are

large
-
scale

networks

and

dividing

the

network

into

smaller

clusters

in

such

dynamic

environment

is

an

advantageous

technic
.


Related Work



MANET and WSN clustering algorithms do not work
properly in VANET environment.




The most important challenge in clustering algorithms
that most of the protocols are trying to solve are:



Optimal cluster management in VANET’s highly
dynamic environment.


Increasing cluster stability (MDMAC, SBCA)


Prevention of frequent cluster changes


Increasing cluster head availability (SBCA)


Increasing cluster lifetime by using appropriate
mobility metrics (DCA, MDCAM, DMAC, SBCA,
MOBIC, …)


Special features of the proposed algorithm



A cluster
-
based target tracking algorithm


high
cluster head and cluster lifetime



robust and stable clusters



low
delay and overhead for electing new cluster
head in lost CH scenarios



distributed cluster head selection
mechanism


Table of Contents





-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion






Assumptions and Definitions


The

proposed

clustering

algorithm

was

designed

for

vehicle

tracking

in

VANETs
.




This

algorithm

assumes

that

vehicles

have

front

and

rear

cameras

and

can

detect

visual

features

of

a

target
.



A

central

entity

such

as

a

police

station

is

seeking

help

to

find

a

specific

target
.

This

entity

is

called

Control

Centre

(CC)

and

is

a

node

located

in

multi
-
hop

communication

distance

from

target
.







Tracking Failure Probability Metric (TFP)

Assumptions
:



All

vehicles

are

aware

of

their

location

and

velocity

by

using

a

GPS

device
.




The

location

of

a

target

is

unknown
;

But

can

be

calculated

by

visual

processing
.




To

calculate

TFP

between

a

vehicle

A

and

the

target

vehicle

T

at

time

t,

we

need

to

have

the

distance

between

node

A

and

T

and

their

speed

vector

at

that

time
.




We

define

a

value

called

Valid

Distance

Range

(VDR),

which

is

used

to

normalize

the

distance

between

any

node

and

target
.







Tracking Failure Probability Metric (TFP)


The

normalized

distance

is

calculated

as

follow
:


(
1
)














𝐷
𝐴
𝑁𝑡

=

𝐷
𝐴𝑇
𝑡
𝑉𝐷𝑅



By

using

velocity

vectors

of

vehicles

we

can

differentiate

between

nodes

moving

in

the

same

direction

and

nodes

moving

in

opposite

direction
.

𝑉

𝐴
𝑡

is

defined

as
:


(
2
)

𝑉

𝐴
𝑡

=

𝑉
𝐴
𝑡
𝑐𝑜
𝜃




We

use

a

value

called

Valid

Velocity

Range

(VVR)

in

order

to

normalize

the

value

of

velocity

vectors
.







Tracking Failure Probability Metric (TFP)


V

and

V

Are

normalized

velocity

vectors

of

vehicle

A

and

target

T

respectively
.


(
3
)

𝑉

𝐴
𝑁𝑡
=

𝑉

𝐴
𝑡
𝑉𝑉𝑅

(
4
)

𝑉

𝑇
𝑁𝑡
=

𝑉

𝑇
𝑡
𝑉𝑉𝑅



Two

values

α

and

β

are

defined

as

Distance

and

speed

Efficiency

Factors
.

These

values

are

coefficients

of

distance

and

velocity

to

control

efficiency

of

these

metrics

of

each

vehicle
.




The

TFP

of

node

A

at

time

t

is

calculated

as

in

the

following

formula
.

The

lower

TFP

indicates

higher

priority

to

become

cluster

head
.



(
5
)


𝑇𝐹𝑃

(
𝐴
)
𝑡
=

100

*

(
𝛼
𝐷
𝐴
𝑁𝑡

+

β

𝑉

𝐴
𝑁𝑡


𝑉

𝑇
𝑁𝑡
)


Table of Contents

-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion

Table of Contents





-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Control Centre Functions

-

Initialization Phase

-

Cluster Management Phase:

o

Cluster Head Functions

o

Cluster Members Functions

-

Tracking Phase


-

Conclusion

Control Centre (CC)


CC broadcasts a “Target Tracking Request Message”
(TTRM) to the entire network with target vehicle’s visual
information.



When CC receives “Response Message” from any
vehicle that has located the target, it will stop
broadcasting.



At any point later, if the CC stops receiving any
information from the cluster head regarding the
specified target (after a pre
-
defined time interval) it will
assume the cluster no longer exists and it will start
broadcasting target’s information again in the network.

Initialization Phase


Any

vehicle

that

receives

a

TTRM

message

from

the

Control

Center

(CC)

and

which

can

detect

the

target

responds

to

CC

and

starts

the

initialization

process
.



OBNs start broadcasting “Request Messages” to their
neighbors

and receive “Response Messages” from
them. OBNs also check the TDV field of the response
messages.



OBNs calculate their TFP. This value displays which
vehicle has closer movement pattern to target and is
more appropriate to be the cluster head.

Initialization Phase


Cluster

members

are

divided

into

2

groups
:

level
-
1

(OBNs)

and

Level
-
2

(NNs)
.




Member

nodes

are

connected

to

cluster

instead

of

cluster

head
.




Initialization

phase

might

be

repeated

only

if

there

is

not

any

cluster

members

available

and

the

cluster

is

destroyed
.




The

purpose

of

our

design

is

to

avoid

switching

to

Initialization

Phase

from

Cluster

Maintenance

phase
.



After

this

phase

the

initial

cluster

is

created

and

the

cluster

head

is

selected
.



Cluster Maintenance Phase

(Cluster Head Functions)


CH

is

responsible

of

managing

the

cluster

by

sending

request

messages

at

every

time

intervals

to

find

new

cluster

members
.



the

cluster

head

calculates

its

own

TFP

every,

and

compares

it

with

other

values

in

the

neighbour

list

to

check

if

it

is

still

a

valid

CH
.

If

not

it

will

send

a

“Resign

Message”
.



A

“Safe

Threshold”

is

defined

because

the

TFPs

are

changing

so

quickly

and

we

do

not

want

to

change

CH

so

frequently
.



vehicles

moving

in

opposite

direction

of

the

target

are

not

supposed

to

join

cluster

because

these

nodes

are

unstable

cluster

members

and

will

decrease

cluster’s

stability
.

Cluster Maintenance Phase

(Cluster Members Functions)


OBNs

calculate

their

TFP

every


𝑝

time

interval

and

send

it

in

RPM

to

their

neighbors
.

Also,

OBNs

store

the

TFP

of

other

nodes

in

their

neighbor

list
.



If

member

nodes

receive

a

RSM

they

are

responsible

to

find

a

node

with

lowest

TFP

value

in

their

neighbor

list

and

select

it

as

CH
.




Also,

if

a

member

node

does

not

receive

any

kind

of

message

after

a

defined

time

interval,

it

assumes

to

be

out

of

cluster

borders

and

will

try

to

send

its

information

directly

to

CC

(if

possible)
.



Tracking Phase


Tracking

includes

taking

continuous

video

of

target

and

sending

video

data

and

location

information

of

target

to

CC

during

specified

time

intervals
.



CMs

send

their

data

to

CH

and

CH

is

responsible

to

inform

CC

about

target’s

location
.



In

case

CM

goes

out

of

cluster

boundaries

(and

does

not

have

access

to

CH),

it

should

send

the

latest

information

to

CC
.


Table of Contents





-
Introduction

-

Related Work

-

Algorithm Features

-

Algorithm Overview

-

Algorithm Description

-

Conclusion

Conclusion

Introduced

algorithm

aims

to

improve

cluster

performance

by

making

stable

and

long

living

cluster
.



The

stability

of

this

algorithm

is

mostly

because

of

adding

candidate

cluster

members

which

are

highly

probable

of

detecting

target

in

near

future
.



The

TFP

value

is

used

as

an

evaluation

value

to

compare

movement

pattern

of

vehicles

with

target
.



The

idea

of

distributed

cluster

head

selection

is

introduced

by

use

of

TFP
.