thesis presentation chayanin thaina advisor : asst.prof. dr. kultida rojviboonchai

Thesis Presentation

Chayanin Thaina

Advisor : Asst.Prof. Dr. Kultida Rojviboonchai

Outline

• VANETs

• Beaconing in VANETs

• Related work

• Proposed adaptive beaconing scheme

• Performance and Evaluation

• Conclusion

2

Outline

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Vehicular Ad-Hoc Networks (VANETs)

• Intervehicle communication

• VANETs characteristics Nodes move with

high speed

Frequently change in network topology

High number of nodes

Vehicular Ad hoc Networks (VANETs)Avaliable from: http://www.car-to-car.org/

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Outline

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Beaconing in VANETs

• Vehicle Discover neighbors Exchange information

• Information may contain NodeID Position Direction Velocity Acknowledgement e.g.

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Beaconing in VANETs

“Most of protocols in VANET using constant beaconing rate”

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Examples of protocols (using constant beaconing rate)

• Routing protocol VADD Vehicle-assisted data delivery in vehicular Ad hoc networks (IEEE Trans. on vehicular tech., 2008)

• Broadcasting protocol AckPBSM Acknowledge Parameterless broadcast Protocol in static to highly mobile ad hoc networks (VTC, 2009) DV-Cast Distributed Vehicular Broadcast Protocol for Vehicular Ad-hoc Networks(IEEE Wireless communication, 2010)

Beacon interval

0.5 s

0.5 s

1 s

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Outline

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Related work

CAR : Connectivity-Aware Routing in Vehicular Ad Hoc Networks

(Valery Naumov and Thomas R. Gross, Infocom 2007)

Improving Neighbor Localization in Vehicular Ad Hoc Networks to Avoid Overhead from Periodic Messages

(Azzedine Boukerche, Cristiano Rezende and Richard W. Pazzi ,GLOBECOM 2009)

Efficient Beacon Solution for Wireless Ad-Hoc Networks (Nawut Na Nakorn and Kultida Rojviboonchai, JCSSE 2010)

Exploration of adaptive beaconing for efficient intervehicle safety communication (Robert K. Schmidt, Tim Leinmuller, Elmar Schoch, Frank Kargl and Gunter Schafer, IEEE Network, 2010)

Connectivity-Aware Routing in Vehicular Ad Hoc Networks (CAR)

• Methodology Beaconing interval is changed according to the

number of neighbors

Calculate beacon interval

0.5Beacon Interval weight

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weight : A weight proportional to the number of neighbors

Improving Neighbor Localization in Vehicular Ad Hoc Networks to Avoid Overhead from Periodic Messages

• Methodology Beacon rate adaptation based on differences in predicted

position

Use last beacon message to estimate position

Send next beacon- When the difference between the predicted and actual position is greater than threshold value

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Efficient Beacon Solution for Wireless Ad-Hoc Networks

• Methodology Adapt beacon based on number of neighbors and

number of buffered messages

1 2( ) ( )s w n w m

s : Dense value, n : Number of neighbors, m : Number of buffer messages

w1, w2 : Weight value of number of neighbors and number of buffer messages

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Efficient Beacon Solution for Wireless Ad-Hoc Networks

LIA : Linear Adaptive Algorithm

STA : Step Adaptive Algorithm

(3)

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Exploration of adaptive beaconing for efficient intervehicle safety communication

• Methodology Adjust the beacon frequency dynamically to the current

traffic situation

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The drawbacks of previous work

• Some works have to use so many tests to find the constant value for adjusting beacon interval.

• Some works, vehicles need GPS data for adjusting beacon interval.

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Conclusion of related workCAR Improving

Neighbor Localization in VANETs to Avoid Overhead from Periodic Messages

Efficient Beacon Solution for Wireless Ad-Hoc Networks

Exploration of adaptive beaconing for efficient intervehicle safety communica-tion

Proposed(Linear regression analysis)

Proposed(k-Nearest Neighbor)

Proposed(LIA+NCR)

Parameters used in calculation

- Number of neighbors

- Position- Speed- Direction

- Number of neighbors- Number of messages

- Velocity- Acceleration- Yaw rate- Emergency/ Regular vehicle- Vehicle density- Special situation

- Number of neighbors- Number of messages- Speed of Data dissemina- tion

- Number of neighbors- Number of messages- Speed of Data dissemina- tion

- Number of neighbors- Number of messages- Neighbor changing rate

Selection mechanisms

Linear function

Predicted position

- Linear Adaptive Algorithm (LIA)- Step Adaptive Algorithm (STA)

X

- Linear regression analysis

- Instance- Based Learning

Linear function

Conclusion of related workCAR Improving

Neighbor Localization in VANETs to Avoid Overhead from Periodic Messages

Efficient Beacon Solution for Wireless Ad-Hoc Networks

Exploration of adaptive beaconing for efficient intervehicle safety communica-tion

Proposed(Linear regression analysis)

Proposed(k-Nearest Neighbor)

Proposed(LIA+NCR)

GPS X X X X X

Beacon interval

>=0.5 X 1.5-7 X >=2.1509 1.5-9 >=1.5

Outline

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Goals of our adaptive beaconing schemes

• Reduce beacon overhead

• Maintain Reliability Retransmission overhead

• Provide the speed of data dissemination according to the requirement of each application

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Design of our adaptive beaconing schemes

• A study on adaptive beaconing is divided into 3 parts

1. Study on the parameters which affect adaptive beacon rate1. Study on the parameters which affect adaptive beacon rate

3. Study on the methods that can be applied on adaptive beacon rate 3. Study on the methods that can be applied on adaptive beacon rate

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2. Study on the system performance when using constant beacon rate and different parameters2. Study on the system performance when using constant beacon rate and different parameters

Node’s environment- Number of neighbors

- Number of buffered messages

Application requirement- Speed of data dissemination

Design of our adaptive beaconing schemes

1. Study on the parameters which affect adaptive beacon rate1. Study on the parameters which affect adaptive beacon rate

Number of neighbors +Number of messages

High

Beacon rate

Low

Number of neighbors +Number of messages

Low

Beacon rate

High

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• A study on adaptive beaconing is divided into 3 parts

Test sending beacon with different beacon intervals and different node’s environment.

Gather all the results and conclude the appropriate beacon intervals.

Design of our adaptive beaconing schemes

2. Study on the system performance when using constant beacon rate and different parameters

2. Study on the system performance when using constant beacon rate and different parameters

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• A study on adaptive beaconing is divided into 3 parts

Metrics

-Beacon overhead

-Reliability

-Retransmission overhead

-Speed of data dissemination

2. Study on the system performance when using constant beacon rate and different parameters

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Beacon overhead

Highway Scenarios Urban Scenarios

Beacon rate --> Beacon overhead

2. Study on the system performance when using constant beacon rate and different parameters

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Reliability

Highway Scenarios Urban Scenarios

Beacon rate in Dense area --> Reliability

Beacon rate in Sparse area --> Reliability

2. Study on the system performance when using constant beacon rate and different parameters

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Retransmission overhead

Highway Scenarios Urban Scenarios

Beacon rate --> Retransmission

2. Study on the system performance when using constant beacon rate and different parameters

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Speed of data dissemination (Low density 2 veh/km)

HighwayScenarios

UrbanScenarios

Sparse area --> Beacon rate

2. Study on the system performance when using constant beacon rate and different parameters

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Speed of data dissemination (Medium density 30 veh/km)

HighwayScenarios

UrbanScenarios

2. Study on the system performance when using constant beacon rate and different parameters

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Speed of data dissemination (High density 80 veh/km)

HighwayScenarios

UrbanScenarios

Dense area --> Beacon rate

2. Study on the system performance when using constant beacon rate and different parameters

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Gather all the results and conclude the appropriate beacon intervals

- Type of scenario that is suitable for choosing is the highway scenario

- In this study, considering the speed of data dissemination in highway to be within 10 s.

2. Study on the system performance when using constant beacon rate and different parameters

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Appropriate beacon intervals

Density (veh/km) Beacon interval (s.)

2 1.56 3

10 720 730 940 960 980 9

Method that determines a statistical model

Machine Learning technique

Improve the solution of Linear Adaptive Interval (LIA)

Design of our adaptive beaconing schemes

3. Study on the methods that can be applied on adaptive beacon rate 3. Study on the methods that can be applied on adaptive beacon rate

Linear regression analysis

k-Nearest Neighbor (k - NN)

k-Nearest Neighbor (k - NN)

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• A study on adaptive beaconing is divided into 3 parts

LIA with Neighbor Change Rate (LIA+NCR)

LIA with Neighbor Change Rate (LIA+NCR)

Linear regression analysis

• Finding relationship between independent variables and a dependent variable

Y a bX

: Dependent variable (Beacon Interval)

: Independent variable (Number of neighbors + number of messages)

: Regression coefficients

Y

X

,a b

1

2

1

( )( )

( )

n

i ii

n

ii

x x y yb

x x

,a y bx

: average of all recorded , : average of all recorded x x y y

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k-Nearest Neighbor

• Instance-based learning

• Training examples will be collected in the form of

• Assume all instances corresponding to points in the n-dimensional space

• Define k value which denotes the number of nearest neighbors

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))(,( ii xfx

k-Nearest Neighbor

• If has query instance - Nearest neighbors are defined by Euclidean distance

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qx

: distance between and

: the value of the th attribute of instance

qx

( )r ia x

Weigh each k-nearest neighbor according to their distance to the query point qx

: distance between and qx

: weight value of each k instance

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k-Nearest Neighbor

Output

: weight value of each k instance

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k-Nearest Neighbor

Improve the solution of Linear Adaptive Interval (LIA)

• Using a new parameter, “neighbor change rate” to improve the previous adaptive solution call “Linear Adaptive Algorithm” (LIA)

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Neighbor nodes --> Beacon rateNeighbor nodes --> Beacon rate

Improve the solution of Linear Adaptive Interval (LIA)

• Improve the solution of Linear Adaptive Algorithm (LIA) by using neighbor change rate (NCR) divided into 3 parts

Neighbor Change Rate (NCR) Using only the data of neighbor change rate to adapt beacon interval

Linear Adaptive Algorithm with Neighbor Change Rate (limited) (LIA+NCR(limited))Using the data of neighbor change rate and network density to adapt beacon interval (Limited the maximum beacon interval)

Linear Adaptive Algorithm with Neighbor Change Rate (unlimited) (LIA+NCR(unlimited))Using the data of neighbor change rate and network density to adapt beacon interval (unlimited the maximum beacon interval)

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Improve the solution of Linear Adaptive Interval (LIA)

Neighbor Change Rate (NCR)

(LIA+NCR (limited)) (LIA+NCR (unlimited))

Neighbor changing rate (NCR)

Neighbor changing rate (NCR)

Neighbor changing rate (NCR)

( ) min( ( ), )f s MinInv c NCR MaxInv ( ) min( ( ( )), )f s MinInv c s NCR MaxInv ( ) ( ( ))f s MinInv c s NCR

Neighbor changing rate (NCR)- When the number of neighbor nodes increase NCR + 1- When the number of neighbor nodes decrease NCR – 1

1 2( ) ( )s w n w m

n : Number of neighbors,

m : Number of buffer messages

w1, w2 : Weight value

n : Number of neighbors,

m : Number of buffer messages

w1, w2 : Weight value

1 2( ) ( )s w n w m

Network density Network density

Example

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Training data for adaptive algorithms

Network density Beacon interval (s.)

1 1.53 35 7

10 715 920 930 940 9

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Example (Linear regression analysis)

Example (Linear regression analysis)

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Ex. - If node has 3 neighbor nodes and 1 buffered messages

- Dense value = 3+1 = 4

- Next beacon interval

ˆ 2.1509 0.4957

ˆ 2.1509 (0.4957 4)

Y X

Y

= 4.1337

Each node will contain a table that collects the training examples ))(,( ii xfx

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Example (k-Nearest Neighbor)

Network density(xi)

Beacon intervalf(xi)

x1 1 1.5

x2 3 3

x3 5 7

x4 10 7

x5 15 9

x6 20 9

x7 30 9

x8 40 9

Define k value (denotes the number of the nearest neighbors)

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Example (k-Nearest Neighbor)

k = 2 Network density

(xi) Beacon interval

f(xi)

x1 1 1.5

x2 3 3

x3 5 7

x4 10 7

x5 15 9

x6 20 9

x7 30 9

x8 40 9

Network density(xi)

Beacon intervalf(xi)

x1 1 1.5

x2 3 3

x3 5 7

x4 10 7

x5 15 9

x6 20 9

x7 30 9

x8 40 9

Ex. - If node has 3 neighbor nodes and 1 buffered messages- Dense value (xq) = 3+1 = 4

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Example (k-Nearest Neighbor)

Calculate the distance between xq and xi

2

1( , ) (4 1) 3qd x x 2

2( , ) (4 3) 1qd x x 2

3( , ) (4 5) 1qd x x 2

4( , ) (4 10) 6qd x x

2

5( , ) (4 15) 11qd x x

2

6( , ) (4 20) 16qd x x

2

7( , ) (4 30) 26qd x x 2

8( , ) (4 40) 36qd x x

Network density(xi)

Beacon intervalf(xi)

x1 1 1.5

x2 3 3

x3 5 7

x4 10 7

x5 15 9

x6 20 9

x7 30 9

x8 40 9

Ex. - If node has 3 neighbor nodes and 1 buffered messages- Dense value (xq) = 3+1 = 4

47

Example (k-Nearest Neighbor)

Calculate the distance between xq and xi

2

1( , ) (4 1) 3qd x x 2

2( , ) (4 3) 1qd x x 2

3( , ) (4 5) 1qd x x 2

4( , ) (4 10) 6qd x x

2

5( , ) (4 15) 11qd x x

2

6( , ) (4 20) 16qd x x

2

7( , ) (4 30) 26qd x x 2

8( , ) (4 40) 36qd x x

Network density(xi)

Beacon intervalf(xi)

x1 1 1.5

x2 3 3

x3 5 7

x4 10 7

x5 15 9

x6 20 9

x7 30 9

x8 40 948

Example (k-Nearest Neighbor)

2

2

2 2 2

2

( , ) (4 3) 1

1 11

( , ) 1

q

q

d x x

wd x x

2

3

3 2 2

3

( , ) (4 5) 1

1 11

( , ) 1

q

q

d x x

wd x x

Calculate the weight value of each nearest neighbor

Network density(xi)

Beacon intervalf(xi)

x1 1 1.5

x2 3 3

x3 5 7

x4 10 7

x5 15 9

x6 20 9

x7 30 9

x8 40 949

Example (k-Nearest Neighbor)

2 2 2

2

1 11

( , ) 1q

wd x x

3 2 2

3

1 11

( , ) 1q

wd x x

Calculate the output

(1 3) (1 7)ˆ ( )1 1qf x

= 5

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Example (Neighbor change rate (NCR))

Ex. Previous- Node has 3 neighbor nodes and Neighbor Change Rate (NCR) is 2

Current- 2 Neighbor nodes adding- Node calculates the neighbor change rate (NCR) NCR+1 = 3

Calculate the next beacon interval

( ) min( ( ), )f s MinInv c NCR MaxInv MinInv = 1.5, MaxInv : 7,

c = 0.2

( ) min(1.5 (0.2 3),7)f s = 2.1

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Example (LIA+NCR (limited))

Ex. Previous- Node has 25 neighbor nodes, 5 buffered messages and Neighbor Change Rate (NCR) is 5

Current- 3 neighbor nodes leaving - Node calculates the neighbor change rate (NCR) NCR-1 = 4- Calculate the network density = 22+5 = 27

Calculate the next beacon interval

( ) min( ( ( ), ))f s MinInv c s NCR MaxInv MinInv = 1.5, MaxInv : 7,

c = 0.2

( ) min(1.5 0.2 (27 4),7)f s = 7

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Example (LIA+NCR (unlimited))

Ex. Previous- Node has 25 neighbor nodes, 5 buffered messages and Neighbor Change Rate (NCR) is 5

Current- 3 neighbor nodes leaving - Node calculates the neighbor change rate (NCR) NCR-1 = 4- Calculate the network density = 22+5 = 27

Calculate the next beacon interval

( ) ( ( ))f s MinInv c s NCR MinInv = 1.5, MaxInv : 7,

c = 0.2

( ) 1.5 0.2 (27 4),7)f s = 7.7

Outline

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Performance and Evaluation

• Case study

DECA : Density-Aware Reliable Broadcasting in

Vehicular Ad Hoc Networks

(ECTI-CON, 2010)

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DECA : Density-Aware Reliable Broadcasting in Vehicular Ad Hoc Networks

• Reliable broadcast protocol

• Store and forward solution

• Exchange beacon message Beacon information contains

Use Linear Adaptive Algorithm : LIA

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Node Identifier(4 bytes)

Number of neighbors(1 byte)

Message Ack#1 #2…

DECA : Density-Aware Reliable Broadcasting in Vehicular Ad Hoc Networks

• Broadcast message Sender select the forwarder from its neighbor list - Neighbor with the highest density will be selected

Selected node rebroadcast the message immediately

Other neighbors (which are not selected) - Store the message and set waiting timeout

In case the selected node doesn’t rebroadcast the message- Other neighbors will rebroadcast the message

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Simulation Setup

• Network Simulation : NS-2.34

• Traffic Simulation Trace generator : SUMO (Simulation of Urban

MObility)

XML convertor to NS2 trace : TraNS

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• Scenario 3x3 km. with 2 lanes

Urban Scenario

4 km. with 4 lanes

Highway Scenario

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Simulation Setup

Simulation Setup

Broadcasting message 1,5,10,15

Vehicle density Highway : 6,10,20,30,40,60,80 veh/kmUrban : 2,10,30,60,80 veh/km

Maximum speed Highway : 50,80 km/hUrban : 120 km/h

Packet life time Highway : 10 s.Urban : 50 s.

Linear Adaptive Algorithm (LIA)

Beacon interval : 1.5-7 ; (c = 0.2, MinInv = 1.5, MaxInv = 7)

Linear regression analysis Regression coefficients : a = 2.1509, b = 0.4957

k-Nearest Neighbor (k-NN) Number of nearest neighbor (k) = 2

LIA+NCR (limited) Beacon interval : 1.5-7; (c = 0.2, MinInv = 1.5, MaxInv = 7)

LIA+NCR (unlimited) Beacon interval : >=1.5; (c = 0.2, MinInv = 1.5)

Requirement of speed of data dissemination

Highway : 10 s.Urban : 15 s.

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• Use DECA to evaluate 6 beaconing schemes

LIA : Linear Adaptive Algorithm

Linear regression : Linear regression analysis

k-NN : k-Nearest Neighbor NCR : Neighbor Change Rate LIA+NCR (limited) : Linear Adaptive Algorithm with

Neighbor Change Rate (limited maximum beacon interval)

LIA+NCR (unlimited) : Linear Adaptive Algorithm with Neighbor Change Rate (unlimited maximum beacon interval)

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Simulation

Simulation

• Metrics Beacon overhead

- bandwidth that has been used for every beacon (bytes/node/message)

Reliability- percentage number of received node to number of total node

Retransmission overhead- bandwidth that has been used for data transmission (bytes/node/message)

Speed of data dissemination - percentage of number of node that received message at time (t)

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Simulation

• Metrics Number of beacon

- The number of beacon that has been sent in scenario Number of retransmission

- The number of data transmission that has been broadcast in scenario

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• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (Beacon Overhead)

Highway Scenarios Urban Scenarios

k-Nearest Neighbor can reduce beacon overhead up to 54% in highway and 41% in urban scenarioLinear regression can reduce beacon overhead up to 78% in highway and 70% in urban scenario

• LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

Simulation result (Beacon Overhead)

Highway Scenarios Urban Scenarios

LIA+NCR (limited) can reduce beacon overhead up to 18% in highway and 11% in urban scenarioLIA+NCR (unlimited) can reduce beacon overhead up to 50% in highway and 51% in urban scenario

• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (Reliability)

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Highway Scenarios Urban Scenarios

• LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

Simulation result (Reliability)

68

Highway Scenarios Urban Scenarios

• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (Retransmission Overhead)

69

Highway Scenarios Urban Scenarios

• LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

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Highway Scenarios Urban Scenarios

Simulation result (Retransmission Overhead)

• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (Speed of data dissemination)

Highway Scenarios

Urban Scenarios

Low density : 10 veh/km

• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (Speed of data dissemination)

Highway Scenarios

Urban Scenarios

Medium density : 30 veh/km

• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (Speed of data dissemination)

Highway Scenarios

Urban Scenarios

High density : 80 veh/km

• LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

Simulation result (Speed of data dissemination)

Highway Scenarios

Urban Scenarios

Low density : 10 veh/km

• LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

Simulation result (Speed of data dissemination)

Highway Scenarios

Urban Scenarios

Medium density : 30 veh/km

• LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

Simulation result (Speed of data dissemination)

Highway Scenarios

Urban Scenarios

High density : 80 veh/km

• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (No.Beacon&No.Retransmission)

77Highway Scenarios

• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages

Simulation result (No.Beacon&No.Retransmission)

78Urban Scenarios

• LIA, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

Simulation result (No.Beacon&No.Retransmission)

79Highway Scenarios

• LIA, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages

Simulation result (No.Beacon&No.Retransmission)

80Urban Scenarios

Outline

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Conclusion

• Propose 3 adaptive beaconing methods Linear regression analysis k-Nearest Neighbor Improve the solution of Linear Adaptive Algorithm (LIA)

by using neighbor change rate (NCR)

• 2 methods can be applied to adjust beacon interval according to Node’s environment Application requirement

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Lowest beacon overhead

Conclusion

• Our proposed methods can save bandwidth Highway : 78 % Urban : 70%

• Our proposed methods can maintain Reliability Speed of data dissemination

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Question &

Answer

84

THANK YOU.

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