thesis presentation chayanin thaina advisor : asst.prof. dr. kultida rojviboonchai
TRANSCRIPT
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
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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
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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 40 948
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)
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Highway Scenarios Urban Scenarios
• LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages
Simulation result (Retransmission Overhead)
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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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