TSF: Trajectory-based Statistical Forwarding for Infrastructure-to-Vehicle Data Delivery

in Vehicular Networks

Jaehoon Jeong, Shuo Guo, Yu Gu, Tian He, and David DuDepartment of Computer Science and Engineering

June 23, 2010

ICDCS 2010

2

Intelligent Transportation Systems (ITS)

ITS provides the transport safety and efficiency through the computing and communications among transport systems.

Vehicle Trajectory

Vehicle follows the route provided by GPS-based navigation systems for efficient driving.

GPS-based Navigation

Vehicle Trajectory

Vehicle moves along its trajectory with bounded speed.

Road Network Layout

Road Map Road Network Graph

Road network layout can be represented as road map.

This road map can be reduced to the road network graph.

Vehicular Traffic StatisticsRoad Map

Road Segment

Road Segment

Vehicle Density

Vehicular traffic statistics can be measured per road segment.

Vehicle density can be measured by vehicle inter-arrival time.

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Motivation We design Data Forwarding for Vehicular

Networks based on these four characteristics of road networks: Vehicle Trajectory Road Network Layout Vehicular Traffic Statistics

Data Forwarding for Vehicular Networks In this paper, we investigate the Data Forwarding for

Infrastructure-to-Vehicle Data Delivery.

Problem Definition

7

Good RendezvousPoint

!

Challenge in Reverse Data Forwarding

8

Target Missing!

Inaccurate Delay

Estimation

The destination vehicle moves along its trajectory.

9

Data Delivery by VADD from AP to Target Point

Expected Delay Actual Delay Error

489 sec 413 sec 16%

Expected STD Actual STD Error

10 sec 139 sec 1277%

Difficult to deliver packets with these errors!

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Packet Forwarding based onStationary Nodes

Assume each intersection has a stationary node for packet buffering.

1. Source Routing to Target Stationary Node

2. Source Routing to Destination Vehicle

Target Point Selection

Miss! Miss! Miss!

Hit!

Hit!

Hit!

Target point with a minimum delay and a high delivery probability.

Minimum Delay

Target Point

Design Challenges

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How to model Packet Delay and Vehicle Delay?Modeling of Packet Delay Distribution and

Vehicle Delay Distribution as Gamma Distributions

How to select an Optimal Target Point?Optimal Target Point Selection Algorithm using

the Distributions of Packet and Vehicle Delays

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Link Delay ModelCase 1:

Immediate Forward

Case 2:Wait andForward

Link Delay ModelCase 1: Immediate ForwardCase 2: Wait and Forward

Let d be the link delay for a road segment.

1. Expectation of link delay

2. Variance of link delay

].wait[] wait|delay link [

]forward[]forward |delay link [][

PE

PEdE

.][][][ 22 dEdEdVar

Case 1

Case 2

Link Delay Distribution Link Delay is modeled as Gamma Distribution:

),(~ iiid Where

i

i

i

ii dE

dVar

2

][

][

2

2][

i

i

i

i

i

ii

dE

End-to-End Packet Delay Model

N

ii

N

iidEPE

11

][][

N

ii

N

iidVarPVar

1

2

1

][][

.segment roadover delay link be Let ii ld

Vehicle Delay Model

N

ii

N

iitEVE

11

][][

N

ii

N

iitVarVVar

1

2

1

][][

.segment roadover time travelbe Let ii lt

50 100 150 200 250 300 350 4000

0.002

0.004

0.006

0.008

0.01

Delay [sec]

PD

FPacket Delay (P)Vehicle Delay (V)

Delay Distributions for intersection i

Optimization

Optimal Target Point Selection

18

TTL v

ii

iiiTi

dpdvvgpfVPP

VPPVEi

0 0.)()(][ re whe

][ subject to ][minarg*

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Performance Evaluation Simulation Setting

Road Network: 5.1miles x 5.6 miles (49 intersections) Communication Range: 200 meters (656 feet)

Performance Metrics Average delivery delay Packet Delivery ratio

Baselines compared with TSF Random Trajectory Point (RTP) Last Trajectory Point (LTP)

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CDF Comparison for Delivery Delay

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Impact of Vehicle Density

For TSF, as the more vehicles exist, 1. The shorter delivery delay is obtained and.2. The higher delivery ratio is obtained.

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Impact of Delivery Probability Threshold

For TSF, as the threshold α increases, 1. The delivery delay increases and.2. The delivery ratio increases.

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Conclusion This paper designs a trajectory-based statistical

data forwarding tailored for vehicular networks, Considering road network characteristics:

• Vehicle Trajectory• Road Network Layout• Vehicular Traffic Statistics

As future work, we will continue to investigate vehicle trajectory for vehicular networking: Data Forwarding, Data Dissemination, and Vehicle

Detouring Protocol.