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Utilizing Shared Vehicle Trajectories for Data Forwarding in Vehicular Networks

IEEE INFOCOM MINI-CONFERENCE

Fulong Xu, Shuo Gu, Jaehoon Jeong, Yu Gu, Qing Cao, Ming Liu and Tian He

Computer Science and EngineeringUniversity of Minnesota

April 11rd, 2011

Motivation2

The vehicular networking is getting a hot research topic. Internet Access, Driving Safety, Data Dissemination,

etc. The existing data forwarding protocols in VANETs

Many ones only take advantage of the road network layout and traffic statistics.

A few adopt available vehicle trajectories along with road traffic. (use the trajectory in a privacy-preserving way)

The objective in this paper Utilizing shared trajectories to provide effective

vehicle-to-vehicle (V2V) communications over multihops in VANETs.

Problem Formulation

The assumptions for the vehicular networks

Every vehicle has a GPS-based navigation system. Traffic statistics are available via commercial navigation services.

The V2V communication operates in a participatory manner. To obtain the communication service, a vehicle should shares its trajectory with other participated ones.

The Access Points (APs) are sparsely deployed in road networks. They are interconnected and disseminate vehicles’ real time trajectory information.

1 2

53 4

Basic Idea4

If the vehicle Va want to send data to the Vc

STDFS is based on vehicular encounter prediction

a

b

c

Packets can be forwarded through the “encountered vehicles path”: Va → Vb → Vc.

Contribution and Challenges

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Contribution Data forwarding based on Shared Vehicle

Trajectory With shared vehicle trajectory, STDFS

outperforms the existing scheme (VADD and TBD).

Challenges Pair-wise encounter prediction and the

construction of a vehicle encounter graph Mathematical model for the travel time

Optimization of the encounter graph to achieve a low delivery delay under the required delivery ratio threshold

Travel Time Prediction6

Basic Theory The travel time of one vehicle over a fixed distance follows the Gamma distribution.

Therefore, the travel time through a travel path in the road network is modeled as:

and can be calculated using the traffic statistics.

Pair-Wise Enconter Prediction

The probability of Va encounter Vb at road section L12 is:

The “ 12” means “encountering at road section L12”.

Its transformation is:

Ta1 and Tb1 are independent stochastic variables following gamma distribution.

Conditional Encounter Probability Calculation in Multi-hop Encounter Prediction

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b

c

a Why use the conditional

encounter probability?

It is used for multi-hop encounter prediction.

If Va want to send packets to Vc,

The success probability is:

An approximate method is used to calculate this conditional probability.

Conditional Encounter Probability

Why to calculate the expectation of two vehicle’s encounter time? It is used in the process of constructing the

encounter graph.

Let the encounter time is T, the expectation of the encounter time is . T is a function of Ta1 and Tb1..

The expectation of two vehicles’ encounter time

encounter position

Constructing a Predicted Encounter Graph

The predicted encounter graph is a directed graph. originates from the source vehicle that intends

to forward packets ends at the forwarding destination

For a node e in the Graph, Its child nodes are the vehicles it might

encounter later after its parent; Its child nodes are sorted in the sequence of

their expected encounter time with node e.

Constructing a Predicted Encounter Graph The construction is a process of expanding the

graph by adding new nodes one by one, according to the sequence of the expected encounter time.

aQueue:

Graph:

b dc s1s2

s

Three Definitions

1. Forwarding Sequence

includes n vehicles (children) that can forward packets from vehicle e to the destination.

This sequence is sorted by the expected encounter time with its parent (vehicle e).

Three Definitions

2. Expected Delivery Ratio (EDR):

The expected delivery ratio of a given vehicle e, denoted by EDRe, is the expected packet delivery ratio from vehicle e to its destination.

If vehicle e’s ith forwarder’s EDR value is EDRi,

1 2 3

Forwarding Sequence

EDR1= 80% EDR2= 60% EDR3= 70%

0.40.9

0.2 EDRe = 0.4*0.8

+ (1-0.4)*0.9*0.6

+ (1-0.4)*(1-0.9)*0.2*0.7

e

Three Definitions

3. Expected Delivery Delay (EDD): The expected delivery delay of a given

vehicle e, denoted by EDDe, is the expected data delivery delay for the packets sent by vehicle e and received by the destination.

If vehicle e’s ith forwarder’s EDD value is EDDi,

Optimizing Expected Delivery Ratio (EDR)

For vehicle e’s full forwarding sequence

So we should only choose a subset of the forwarders and get the optimal forwarding sequence!

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e1.0 1.0

EDR1=0.6 EDR2=0.9

Should all the forwarders in the sequence be selected to forward packets?

If both node 1 and node 2 are selected as forwarding nodes:

EDRe = 1*0.6 =0.6

If only node 2 are selected as forwarding nodes:

EDRe = 1*0.9 =0.9

Optimizing Expected Delivery Ratio (EDR)

Select only a subset of the forwarders

node 3 has to be selected for data forwarding.

Then try to add more nodes into the optimal forwarding sequence backwardly.

1 2 3

e

EDR1= 80% EDR2= 60% EDR3= 70%

0.40.9

0.2

Optimizing Expected Delivery Delay (EDD)

It is meaningless if only optimize EDD and do not care about EDR.

Our goal is to optimize the EDD metric for the root node under the constraint that the EDR metric is no less than a certain threshold R.

Since the predicted encounter graph is expanded in the order of expected encounter time, it is helpful to optimize EDD.

Optimizing Expected Delivery Delay (EDD)

The method to optimize EDD In the process of constructing the graph,

when a new path to the target node is found, use the approach of optimizing EDR to calculate the EDR of the root node:

If the the EDR value is greater than the required bound R, the graph construction stops and the optimal forwarding sequence is acquired.

Otherwise the expanding continues.

is the best forwarder

Forwarding Protocol19

STDFS Forwarding Rule Within a connected component, packets are

forwarded to the best forwarder.

How to select the best forwarder? Within the connected component, each

vehicle calculates its own EDR and EDD:

If the EDRs of all the connected vehicles can not meet the requested bound R, the vehicle having the highest EDR is the best forwarder;

If there exists the vehicles whose EDRs are greater than the bound R, within these vehicles the one having the minimal EDD value is the best forwarder.

Performance Evaluation21

Evaluation Setting Performance Metric: (i) Data Delivery Ratio, (ii)

Average Delivery Delay Parameters: (i) Vehicle speed deviation, (ii)

Vehicular traffic density.*We focus on data forwarding from vehicles to fixed points.

Simulation Environments 36-intersection road network (4.2 miles X 3.7 miles) Vehicle mobility model: Manhattan Mobility model Vehicle speed distribution: N(40,7) MPH Communication range: 200 meters Time-To-Live (TTL): 1000 seconds Requested EDR bound R: 0.9

Impact of Vehicle Speed Deviation

STDFS outperforms VADD and TBD under different vehicle speed deviations.

As the vehicle speed deviation increases, STDFS has a higher delivery delay.

Impact of Vehicular Density

STDFS is more suitable for data forwarding when vehicular networks become sparse.

Conclusion24

In this talk, the data forwarding scheme called STDFS is introduced based on the vehicle trajectory: Data Forwarding from Vehicle to Vehicle.

Also, the predicted encounter graph is introduced for STDFS data forwarding scheme: This predicted encounter graph can be used for

other VANET routing or forwarding schemes.

As future work, the privacy issue caused by sharing trajectories with public will be studied.