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

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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: / 4

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Beaconing in VANETs Vehicle  Discover neighbors  Exchange information Information may contain  NodeID  Position  Direction  Velocity  Acknowledgement e.g. 6

Beaconing in VANETs “Most of protocols in VANET using constant beaconing rate” 7

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 1 s 8

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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 11 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 12

Efficient Beacon Solution for Wireless Ad-Hoc Networks Methodology  Adapt beacon based on number of neighbors and number of buffered messages s : Dense value, n : Number of neighbors, m : Number of buffer messages w 1, w 2 : Weight value of number of neighbors and number of buffer messages 13

Efficient Beacon Solution for Wireless Ad-Hoc Networks  LIA : Linear Adaptive Algorithm  STA : Step Adaptive Algorithm (3) 14

Exploration of adaptive beaconing for efficient intervehicle safety communication Methodology  Adjust the beacon frequency dynamically to the current traffic situation 15

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. 16

Conclusion of related work CARImproving 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 work CARImproving 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) GPSX  X  XXX Beacon interval >=0.5X1.5-7X>= >=1.5

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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 20

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 rate 3. Study on the methods that can be applied on adaptive beacon rate 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 rate Number of neighbors + Number of messages High Beacon rate Low Number of neighbors + Number of messages Low Beacon rate High 22 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 23 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 24  Beacon overhead Highway ScenariosUrban Scenarios Beacon rate --> Beacon overhead

2. Study on the system performance when using constant beacon rate and different parameters 25  Reliability Highway ScenariosUrban 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 26  Retransmission overhead Highway ScenariosUrban Scenarios Beacon rate --> Retransmission

2. Study on the system performance when using constant beacon rate and different parameters 27  Speed of data dissemination (Low density 2 veh/km) Highway Scenarios Urban Scenarios Sparse area --> Beacon rate

2. Study on the system performance when using constant beacon rate and different parameters 28  Speed of data dissemination (Medium density 30 veh/km) Highway Scenarios Urban Scenarios

2. Study on the system performance when using constant beacon rate and different parameters 29  Speed of data dissemination (High density 80 veh/km) Highway Scenarios Urban Scenarios Dense area --> Beacon rate

2. Study on the system performance when using constant beacon rate and different parameters 30  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 31  Appropriate beacon intervals Density (veh/km)Beacon interval (s.)

 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 Linear regression analysis k-Nearest Neighbor (k - NN) k-Nearest Neighbor (k - NN) 32 A study on adaptive beaconing is divided into 3 parts LIA with Neighbor Change Rate (LIA+NCR)

Linear regression analysis Finding relationship between independent variables and a dependent variable : Dependent variable (Beacon Interval) : Independent variable (Number of neighbors + number of messages) : Regression coefficients : average of all recorded, : average of all recorded 33

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 34

k-Nearest Neighbor If has query instance - Nearest neighbors are defined by Euclidean distance 35 : distance between and : the value of the th attribute of instance

 Weigh each k-nearest neighbor according to their distance to the query point : distance between and : weight value of each k instance 36 k-Nearest Neighbor

 Output : weight value of each k instance 37 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) 38 Neighbor 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) 39

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) - When the number of neighbor nodes increase  NCR When the number of neighbor nodes decrease  NCR – 1 n : Number of neighbors, m : Number of buffer messages w 1, w 2 : Weight value n : Number of neighbors, m : Number of buffer messages w 1, w 2 : Weight value  Network density

Example 41  Training data for adaptive algorithms Network densityBeacon interval (s.)

42 Example (Linear regression analysis)

43 Ex.- If node has 3 neighbor nodes and 1 buffered messages - Dense value = 3+1 = 4 - Next beacon interval =

 Each node will contain a table that collects the training examples 44 Example (k-Nearest Neighbor) Network density (x i ) Beacon interval f(x i ) x1x x2x2 33 x3x3 57 x4x4 107 x5x5 159 x6x6 209 x7x7 309 x8x8 409

 Define k value (denotes the number of the nearest neighbors) 45 Example (k-Nearest Neighbor) k = 2 Network density (x i ) Beacon interval f(x i ) x1x x2x2 33 x3x3 57 x4x4 107 x5x5 159 x6x6 209 x7x7 309 x8x8 409

Network density (x i ) Beacon interval f(x i ) x1x x2x2 33 x3x3 57 x4x4 107 x5x5 159 x6x6 209 x7x7 309 x8x8 409 Ex.- If node has 3 neighbor nodes and 1 buffered messages - Dense value (x q ) = 3+1 = 4 46 Example (k-Nearest Neighbor) Calculate the distance between x q and x i

Network density (x i ) Beacon interval f(x i ) x1x x2x2 33 x3x3 57 x4x4 107 x5x5 159 x6x6 209 x7x7 309 x8x8 409 Ex.- If node has 3 neighbor nodes and 1 buffered messages - Dense value (x q ) = 3+1 = 4 47 Example (k-Nearest Neighbor) Calculate the distance between x q and x i

Network density (x i ) Beacon interval f(x i ) x1x x2x2 33 x3x3 57 x4x4 107 x5x5 159 x6x6 209 x7x7 309 x8x Example (k-Nearest Neighbor)  Calculate the weight value of each nearest neighbor

Network density (x i ) Beacon interval f(x i ) x1x x2x2 33 x3x3 57 x4x4 107 x5x5 159 x6x6 209 x7x7 309 x8x Example (k-Nearest Neighbor)  Calculate the output = 5

50 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 MinInv = 1.5, MaxInv : 7, c = 0.2 = 2.1

51 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 MinInv = 1.5, MaxInv : 7, c = 0.2 = 7

52 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 MinInv = 1.5, MaxInv : 7, c = 0.2 = 7.7

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Performance and Evaluation Case study  DECA : Density-Aware Reliable Broadcasting in Vehicular Ad Hoc Networks (ECTI-CON, 2010) 54

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 55 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 56

Simulation Setup Network Simulation : NS-2.34 Traffic Simulation  Trace generator : SUMO (Simulation of Urban MObility)  XML convertor to NS2 trace : TraNS 57

Scenario 3x3 km. with 2 lanes Urban Scenario 4 km. with 4 lanes Highway Scenario 58 Simulation Setup

Broadcasting message1,5,10,15 Vehicle densityHighway : 6,10,20,30,40,60,80 veh/km Urban : 2,10,30,60,80 veh/km Maximum speedHighway : 50,80 km/h Urban : 120 km/h Packet life timeHighway : 10 s. Urban : 50 s. Linear Adaptive Algorithm (LIA)Beacon interval : ; (c = 0.2, MinInv = 1.5, MaxInv = 7) Linear regression analysisRegression coefficients : a = , b = 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. 59

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) 60 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) 61

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 62

LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages Simulation result (Beacon Overhead) Highway ScenariosUrban Scenarios k-Nearest Neighbor can reduce beacon overhead up to 54% in highway and 41% in urban scenario Linear 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 ScenariosUrban Scenarios LIA+NCR (limited) can reduce beacon overhead up to 18% in highway and 11% in urban scenario LIA+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 (Beacon Overhead) 65 Highway ScenariosUrban Scenarios

LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages Simulation result (Beacon Overhead) 66 Highway ScenariosUrban Scenarios

LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages Simulation result (Reliability) 67 Highway ScenariosUrban Scenarios

LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages Simulation result (Reliability) 68 Highway ScenariosUrban Scenarios

LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages Simulation result (Retransmission Overhead) 69 Highway ScenariosUrban Scenarios

LIA, NCR, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages 70 Highway ScenariosUrban 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) 77 Highway Scenarios

LIA, Linear Regression, k-Nearest Neighbor - Broadcasting message : 10 messages Simulation result (No.Beacon&No.Retransmission) 78 Urban Scenarios

LIA, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages Simulation result (No.Beacon&No.Retransmission) 79 Highway Scenarios

LIA, LIA+NCR (limited), LIA+NCR (unlimited) - Broadcasting message : 10 messages Simulation result (No.Beacon&No.Retransmission) 80 Urban Scenarios

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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 82 Lowest beacon overhead

Conclusion Our proposed methods can save bandwidth  Highway : 78 %  Urban : 70% Our proposed methods can maintain  Reliability  Speed of data dissemination 83

Question & Answer 84

THANK YOU. 85