Download ICDCS-2010-TSF-Presentation

ICDCS 2010
TSF: Trajectory-based Statistical Forwarding
for Infrastructure-to-Vehicle Data Delivery
in Vehicular Networks
Jaehoon Jeong, Shuo Guo, Yu Gu, Tian He, and David Du
Department of Computer Science and Engineering
June 23, 2010
Intelligent Transportation Systems (ITS)
 ITS provides the transport safety and efficiency through
the computing and communications among transport systems.
2
Vehicle Trajectory
GPS-based
Navigation
Vehicle Trajectory

3

Vehicle follows the route provided by GPS-based navigation systems
for efficient driving.
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.
4
Vehicular Traffic Statistics
Road Map
Road Segment
Vehicle Density
Road Segment

Vehicular traffic statistics can be measured per road segment.

Vehicle density can be measured by vehicle inter-arrival time.
5
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.
6
Problem Definition
Good
Rendezvous
Point !
7
Challenge in Reverse Data Forwarding
The destination vehicle moves along its trajectory.

Target Missing!
Inaccurate
Delay
Estimation
8
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!
9
Packet Forwarding based on
Stationary Nodes
 Assume each intersection has a stationary node for
packet buffering.
1. Source Routing to
Target Stationary Node
2. Source Routing to
Destination Vehicle
10
Target Point Selection

Target point with a minimum delay and a high delivery probability.
Hit!
Hit!
Target Point
Hit!
Minimum
Delay
11
Miss!
Miss!
Miss!
Design Challenges

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
12
Link Delay Model
Case 1:
Immediate
Forward
Case 2:
Wait and
Forward
13
Link Delay Model
Case 1: Immediate Forward
Case 2: Wait and Forward
Let d be the link delay for a road segment.
1. Expectation of link delay Case 1
E[d ]  E[link delay | forward ]  P[forward ]
 E[link delay | wait ]  P[ wait ].
2. Variance of link delay
Case 2
Var[d ]  E[d ]  E[d ] .
2
14
2
Link Delay Distribution

Link Delay is modeled as Gamma Distribution:
di ~ ( i ,i )
Where
2
i
Var[d i ]
i 

E[d i ]
i
i 
15
E[di ]
i
i


i
2
i
2
i
End-to-End Packet Delay Model
Let di be link delay over road segment li .
N
N
i 1
N
i 1
N
E[ P]   E[d i ]   i
Var[ P]  Var[di ]  
i 1
16
i 1
2
i
Vehicle Delay Model
Let ti be travel time over road segment li .
N
N
i 1
N
i 1
N
E[V ]   E[ti ]   i
Var[V ]  Var[ti ]  
i 1
17
i 1
2
i
Optimal Target Point Selection

Delay Distributions for intersection i
0.01
Packet Delay (P)
Vehicle Delay (V)
0.008
PDF
0.006
0.004
0.002
0

50
Optimization
100
150 200 250
Delay [sec]
300
350
400
i*  arg min E[Vi ] subject to P[ Pi  Vi ]  
iT
TTL v
18
where P[ Pi  Vi ]  0
0 f ( p) g (v)dpdv.
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)
19
CDF Comparison for Delivery Delay
20
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.
21
Impact of Delivery Probability Threshold
 For TSF, as the threshold α increases,
1. The delivery delay increases and.
2. The delivery ratio increases.
22
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.
23