A study of Intelligent Adaptive beaconing approaches on VANET Proposal Presentation Chayanin Thaina...

A study of Intelligent Adaptive beaconing approaches on VANET

Proposal Presentation

Chayanin ThainaAdvisor : Dr.Kultida Rojviboonchai

MANET & VANET

Beaconing in VANET

Problems of using constant beaconing rate in VANET

Research on Adaptive beaconing rate in VANET

My Proposal

Future work

Group of mobile nodes No infrastructure Establish connectivity via multi-hop wireless

communication

Mobile Ad hoc Network (MANET)

Mobile Ad hoc network (MANET)

Vehicular Ad hoc Network (VANET)

Intervehicle communication VANET characteristics• Nodes move with high speed• High number of nodes• Frequently change in network topology



Avaliable from: http://www.car-to-car.org/

Comparison of MANET and VANET

Parameters MANET VANET

Mobility Low High

Change in network topology Slow Frequently and fast

Partitioned network Low Frequently

Moving pattern of nodes Random Constrained by road

Beaconing in VANET



My Proposal

Future work

MANET & VANET

Beaconing in VANET

Vehicle • Discover neighbors• Exchange information

Information may contain • nodeID• vehicle velocity• Position• acknowledgement e.g.

Most of protocols in VANET using constant beaconing rate

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)

- Beacon interval : 0.5s Broadcasting protocol

• AckPBSM : Acknowledge Parameterless broadcast protocol in static to highly mobile ad hoc networks

- Reliable and efficient broadcasting vehicular ad hoc networks (VTC, 2009) - Beacon interval : 0.5s• DV-Cast : Distributed Vehicular Broadcast Protocol for

Vehicular Ad-hoc Networks (IEEE Wireless communication, 2010)

- Beacon interval : 1s


Research on Adaptive Beaconing in VANET

My Proposal

Future work

MANET & VANET

Beaconing in VANET


Sparse area Dense areaHigh beaconing rate

- Faster neighbor discovery

- Collision problem- Decrease reliability- High beacon overhead

Low beaconing rate

- Decrease reliability - Increase reliability- Lower beacon overhead


Reliability Beacon interval (s)


Beacon overheadBeacon interval (s)


My Proposal

Future work

MANET & VANET

Beaconing in VANET



CAR: Connectivity-Aware Routing in Vehicular Ad Hoc Networks (Infocom, 2007) Improving Neighbor Localization in Vehicular Ad Hoc Networks to Avoid Overhead from Periodic Messages (GLOBECOM, 2009) Efficient Beacon Solution for Wireless Ad-Hoc

Networks (JCSSE, 2010) Exploration of adaptive beaconing for efficient

intervehicle safety communication (IEEE Network, 2010)

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

Proposed• Find connected paths between source and

destination pairs• “Guards” help to track the current position

Methodology• Adaptive beaconing rate

- Beaconing interval is changed according to the number of neighbors- Calculate beacon interval

0.5s*w*number of neighbors

Advantage• Simple solution

Disadvantage• Considered only the number of neighbors

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

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

Proposed• Improve accuracy of the localization of

neighboring vehicles• Decrease number of beacons

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

Advantage• Position predicted increase the accuracy of

the localization of neighbors

Disadvantage• Use GPS information• Did not consider node’s environment


Efficient Beacon Solution for Wireless Ad-Hoc Networks

Proposed• Adaptive beaconing schemes

- Save beacon overhead - Maintain accurate local information

Methodology• Adapt beacon based on number of neighbors and

number of buffered packets

s = (w1 x n)+(w2 x m)

s : Dense value, n : Number of neighbors, m : Number of buffer packetsw1, w2 : Weight value of number of neighbors and number of buffer packets


LIA : Linear Adaptive Algorithm STA : Step Adaptive Algorithm

𝐋𝐈𝐀 : 𝑓 (𝑠 )=min(𝑀𝑖𝑛𝐼𝑛𝑣+ (𝑐 𝑥 𝑠) ,𝑀𝑎𝑥𝐼𝑛𝑣)STA :

Advantage• Did not use GPS information

Disadvantage• Not flexible

- Considered only in case of the fastest speed of data dissemination


Exploration of adaptive beaconing for efficient intervehicle safety communication

Proposed• Control the offered load • Adjust the beacon frequency dynamically to the current traffic situation• Maintain appropriate accuracy

Methodology• 2 categories of schemes

1. Depending on the vehicle’s own movement

2. Depending on the surrounding vehicles’ movement

• Combination of Schemes


Situation-adaptive beaconing

Depending on own movement Depending on surrounding vehicles’ movement

Velocity

Movement change

Specialvehicle

Acceleration Yaw rate

Macroscopic scope

Microscopic scope

VehicleDensity

Close-byvehicles

Crossingvehicles

…


Advantage• Considered several schemes for adaptation of

beacon rate

Disadvantage• Only a theoretical analysis• Did not show how to implement • Did not show the result

Paper conclusion

CAR




Parameters used in calculation

- Number of neighbors

- Vehicle position- Speed- Direction

- Number of neighbors- Number of packets

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

Selection mechanisms Linear

function Predicted position- Linear Adaptive Algorithm (LIA)- Step Adaptive Algorithm (STA)

X

GPS X X

My Proposal

Future work

MANET & VANET

Beaconing in VANET



Idea of my beacon approaches Adaptation beacon rate by considering node’s

environment e.g.• Number of neighbors• Number of messages

Number of neighbors +Number of messages

High

Beacon rate

Low

Low

Beacon rate

High

Consider the requirement of speed of data dissemination in each application

Number of neighbors +Number of messages

Goals of my beacon approaches Reduce beacon overhead Maintain • Reliability• Retransmission

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

application

Idea of my beacon approaches

Determination of a math model by using• Linear regression analysis

Linear regression

http://upload.wikimedia.org/wikipedia/commons/3/3a/Linear_regression.svg

Idea of my beacon approaches

Use the technique of Artificial Intelligence (AI) to adapt beacon rate• Machine Learning (a branch of AI)

e.g. K-nearest neighbor

Linear regression analysis

Finding relationship between independent variables and a dependent variable

bxay ˆ

y: Dependent variable x: Independent variableba, : Regression coefficients

Linear regression analysis

Adaptive beaconing rate• Dependent variable

- Beacon interval • Independent variable

- Number of neighbors + number of messages

Instance-based learning• Approximate real-valued or discrete-valued

target function• Store and all training examples

K-nearest neighbor• Assume all instances corresponding to points in

the n-dimensional space

K-nearest neighbor

))(,( ii xfx

• If has query instance

- Nearest neighbors are defined by Euclidean distance

• Distance-weighted k-nearest neighbor- Weigh each k-nearest neighbor according to their distance to the query point

K-nearest neighbor

qx

n

rjrqrjq xaxaxxd

1

2))()((),(

qx

2),(

1

jqi xxdw

K-nearest neighbor• Output

�� (𝑥 𝑞)=∑𝑖=1

𝑘

𝑤𝑖 𝑓 (𝑥𝑖)

∑𝑖=1

𝑘

𝑤𝑖

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

Reliable broadcast protocol Store and forward solution Exchange beacon message (Use Linear Adaptive Algorithm :LIA)• Beacon information contains

- Local density- Identifier of received message

DECA

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

DECA

Simulation Network Simulation : NS-2.34 Traffic Simulation• Trace generator : SUMO (Simulation of Urban

MObility)• XML convertor to NS2 trace : TraNS

Vehicles• Maximum speed : 120 km/h

Message : 1 message

Simulation Scenario 3x3 km. with 2 lanes

Urban Scenario

4x4 km. with 4 lanes

Highway Scenario

Simulation

Highway Urban

Density (veh/km) 2,6,10,20,30,40,60,80 2,10,30,60,80

Speed of data dissemination (s) 10 15

Simulation Metrics• Reliability

- percentage number of received node to number of total node

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

• Retransmission- 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)

Simulation result (Highway) 1 message (Reliability)

Simulation result (Highway) 1 message (Beacon overhead)

Simulation result (Highway) 1 message (Retransmission)

Simulation result (Highway) 1 message 2,6,10,20 veh/km (Speed of data dissemination)

Simulation result (Highway) 1 message 30,40,60,80 veh/km (Speed of data dissemination)

Simulation result (Urban) 1 message (Reliability)

Simulation result (Urban) 1 message (Beacon overhead)

Simulation result (Urban) 1 message (Retransmission)

Simulation result (Urban) 1 message 2,10,30 veh/km (Speed of data dissemination)

Simulation result (Urban) 1 message 60,80 veh/km (Speed of data dissemination)

Future work

MANET & VANET

Beaconing in VANET



My Proposal

Future work More than one concurrent messages in

simulation Simulate in real scenario map Analyze advantage and disadvantage of each

methodology Determine the method that can adjust

beaconing rate more efficiently

A study of Intelligent Adaptive beaconing approaches on VANET Proposal Presentation Chayanin Thaina...

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