+ Network Research Lab, University of CaliforniaLos Angeles, California, USA

* University of Karlsruhe in the Karlsruhe Institute of TechnologyKarlsruhe, Germany

GeoDTN+Nav: A Hybrid Geographic and DTN Routing with Navigation Assistance in Urban Vehicular Networks

Pei-Chun Cheng+, Jui-Ting Weng+, Lung-Chih Tung+, Kevin C. Lee+, Mario Gerla+, Jérôme Härri*

1st Annual Symposium on Vehicular Computing Systems (ISVCS)

Dublin, IrelandJuly 23rd , 2008

2

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Outline

� Background and Motivation

� Navigation-Assist Model – VNI

� GeoDTN+Nav

� Evaluation

� Conclusion and Future Work

3

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Motivation

� Geographic Routing:

� routes packets based on geographic information of the source, relays and

destination

� Based on a greedy forwarding approach

� Efficient in this situation:

� and in this one ??

s d

d

s ?

4

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Motivation

� A second phase called “recovery” exists..

�And in this one ??

�Need to use nodes mobility to move between partitions

� Low density

� Spatial and temporal mobility patterns

� Obstacles

d

s

d

s

d

s

5

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Goal of GeoDTN+Nav

� Aim to improve packet delivery in a disconnected VANET

� Targeted for DTN

� If delay is not crucial

� Inspired from

� LER (Last Encounter Routing),

� ZebraNet,

� Data mules…

� Basic idea

� Exploit vehicles’ mobility

� Use store-carry-forward instead of store-forward

� Challenge:

� Finding the correct “Mule”

6

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Outline

� Background and Motivation

� Navigation-Assist Model – VNI

� GeoDTN+Nav

� Evaluation

� Conclusion and Future Work

7

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Navigation-assisted Model - VNI

� Basic idea is trivial

� Exploit mobility to help deliver packets across disconnected networks

� The problem now is which neighbor to choose?

� Blind random choice

� Might not help

� Neighbors may move even farther away from the destination

� “Informed” choice

� Better decision

� If we know neighbors’ destination or path information

� How to know such Information ?

8

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Navigation Systems

� General Idea� Exploit navigation systems

� Harvest neighbors’ destination/path information

� Assumption� Every vehicle has a navigation system

� Navigation system becomes common accessory� Provide real-time traffic information

� Not always true !

� Relaxed Assumption� “Pseudo/Virtual” navigation system

9

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Categorizing Vehicular Mobility Patterns

Categories Example

Deterministic(Fixed) RouteBig Blue Bus, UCLA Shuttle,

Metro Train

Deterministic(Fixed) Destination Taxi, UCLA Van pool

Probabilistic(Expected) Route /

DestinationNavigation system guided vehicles

Unknown Random movement

� We propose a “Virtual Navigation Interface” to generalize these

vehicle categories

10

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Virtual Navigation Interface - VNI

� Assume VNI is installed on every vehicle

� A lightweight wrapper interfaces: interact with data sources

� Provide two unified information:

� Route info

� Destination

� Path

� Direction

� Confidence

� 0% (Random) ~ 100%(Deterministic)

11

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Virtual Navigation Interface - VNI

w/ Navigation

VNI: (path, 25%)

w/o Navigation

VNI: (?, 0%)

Bus

VNI: (trip, 100%)

Taxi

VNI: (dest, 100%)

12

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Outline

� Background and Motivation

� Navigation-Assist Model – VNI

� GeoDTN+Nav

� Evaluation

� Conclusion and Future Work

13

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

� Introduce a third forwarding mode in georouting

� DTN recovery mode

� Complement conventional two-mode geo-routing

� Three routing modes

� Greedy

� Perimeter

� DTN

14

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Algorithm

� Every node adds the VNI tuple to the periodic beacons

� “Route_info, Confidence”

� Follow conventional Greedy mode as long as possible

� In Perimeter forwarding

� Detect possible partitions

� Check neighbors’ navigation information

� Switch to DTN mode if necessary

� Network partitioned

� “mule” may bring the packet out of the local maximum

15

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

When to Switch to DTN mode ?

� Computes a switch score in perimeter mode:

� S(i)= αP(h) • βQ(i) • γDir(i)

� Where:

� P(h): partition probability

� Q(i): Quality of the “mule”

� Dir(i): Direction of the “mule”

� If S(i) > Sthres => Switch to DTN mode

16

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

� Let

� α = β = γ = 1

� Sthres = 0.25

Example

Q(N1) = 0.1

S(N1) = P(8) * Q(N1)

= 0.04

P(9) = 0.5

Q(N2) = 0

S(N2) = 0

P(8) = 0.4

Q(N3) = 0.6

S(N3) = 0.24

Q(N1) = 0.2

S(N1) = 0.01

Q(N2) = 0.7

S(N2) = 0.35 Q(N3) = 0.6

S(N3) = 0.30

17

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Outline

� Background and Motivation

� Navigation-Assist Model – VNI

� GeoDTN+Nav

� Evaluation

� Conclusion and Future Work

18

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Sythetic Scenario

� Qualnet 3.95, 802.11b, transmission rate 2Mbps, radio range 350m

� α = β = γ = 1, Sthres = 0.25

� Static nodes randomly placed

� Mules moving at 50km/hr, varies from 5 to 40 mules

� Inter-departure time at A and B: � Uniform

� Random

� Fixed source and destination

� 20 runs

� Compare GPCR and GeoDTN+Nav for� PDR

� latency

19

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Realistic Scenario

� Oakland, 1500m by 4000m

� Mobility generated by VanetMobisim

� Additional mules generated (5 to 40)

� Random source and fixed destination

� 20 runs each

� Compare GPSR, GPCR, and GeoDTN+Nav

� PDR

� latency

20

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Synthetic Scenario (PDR)

� GeoDTN+Nav has a PDR

70% PDR above GPCR in

both inter-departure

� Almost Full connectivity

explains the sharp increase in

PDR for GPCR/UNIM

21

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Synthetic Scenario (Latency)

� GeoDTN+Nav’s latency drops

as more mobile nodes

increase connectivity

22

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Realistic Scenario (PDR)

� GeoDTN+Nav has higher PDR

than GPSR and GPCR in both

random and uniform inter-

departure time

23

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Realistic Scenario (Latency)

� GPCR’s latency remains small

as only 1-hop connectivity

24

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Outline

� Background and Motivation

� Navigation-Assist Model – VNI

� GeoDTN+Nav

� Evaluation

� Conclusion and Future Work

25

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Conclusion

� We proposed a generalized framework for navigation information � Different level of information for privacy: Path, Destination, Direction

� Consider uncertainty: Confidence

� We presented a hybrid georouting protocol with DTN mode� Use DTN to when network is partitioned and georouting fails

� For DT applications

� Use VNI to select the appropriate “mule” for DTN mode

� Showed it was able to deliver packets in partitioned and sparse networks

� Future work:� Increase the mules heterogeneity

� Parameter optimization: setting switch score threshold adaptively based on topology connectivity

� Cache policy: How much to cache? What is the right buffer capacity?

26

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Synthetic Scenario (Hop Count)

� GeoDTN+Nav has higher hop

count due to higher PDR

� Closeness in PDR explains

the hop count convergence of

GPCR/UNIM and

GeoDTN+Nav/UNIM from 25

onward

27

ISVCS 2008

Jérôme Härri

Traffic Telematics –

DSN Research Group – Institute of Telematics

Evaluation – Realistic Scenario (Hop Count)

� GeoDTN+Nav has higher hop

count due to higher PDR

� 1 hop for GPSR and GPCR

shows that there is NO

connectivity except src/dest

pair that is one hop away!!!

� GeoDTN+Nav operates under

intermittent connectivity!