Transportation-aware Routing in Delay Tolerant Networks (DTNs)

Asia Future Internet 2008

Taekyoung Kwon

Seoul National University

MMLAB

2

IntroductionIntroduction11

outline

Scenario ModelScenario Model22

Our ApproachesOur Approaches33

SummarySummary44

MMLABIntroduction

DTN Delay (or Disruption) Tolerant Networks

Delay? Disruption? Interplanetary networks Sensor networks

Nodes sleep to save power Vehicular networks

Mobile devices get out of other devices’ radio ranges Opportunistic networks

a sender and a receiver make contact at an unscheduled time Underwater networks

MMLABIntroduction

Motivation DTNs may have to be accommodated in future networks

Intermittent connectivity Long or variable delay Asymmetric data rates Heterogeneous links High packet error rates Limited node uptime

MMLABResearch Issues in DTNs

Delay Tolerant Network Architecture Overall redesign

E.g. Bundle Protocol

Routing Protocols Delivery ratio Reducing delay

Congestion control Distributed Caching Multicast/Anycast

MMLAB IP routing may not work

E2e connectivity may not exist at the same time Routing (e.g. MANET) performs poorly in DTN

environments Some assumptions for routing will not work

E.g. BGP leverages TCP

6

Source: Kevin Fall, IRTF DTN RG

MMLABRelated Work (mobility)

Mobility model

DTN

No Mobility Mobility

Routine Random

Predictable Tendency-based

MMLABRelated work (routing)

Some Routing Strategies Epidemic routing

Flooding Spray and wait (S&W)

Limited number of copies of a message

Important Metrics delivery probability delivery latency overhead ratio

MMLABMotivation

Existing routing protocols use only past information like contact history, etc.

DTN Routing can leverage additional information in the future

speed, direction, destination of mobile node, etc.

We want to propose routing protocol using these additional information

MMLABScenario Model

When to use DTN? DTNs can be used for delay tolerant applications

environmental monitoring, some publish/subscribe applications

We assume that each node has location information E.g. GPS, Navigation, localization techniques

MMLABPotential Approaches

Leveraging mobility information Direction of mobile host Speed of mobile host Location of mobile host’s destination Location of message’s destination

Message’s destination can be fixed or mobile

Our approaches Direction-based Destination-based Transportation info-based

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Our Approach 1

Direction-Based routing protocol Spray & Wait based Number of tokens: n Number of split tokens depends on direction difference

sender’s direction

hand over n/2 tokens

0 °

90 °

-90°

hand over

n*angle/180° tokens

receiver’s direction

hand over

-n*angle/180° tokens

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Our Approach 2

Destination-Based routing protocol Spray and wait based Number of tokens for handover

n/2*( distance / max diameter )

Maximum diameter

MAP

Sender’s destination

Receiver’s destination

dist

ance

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Hybrid of approaches 1 and 2

Direction-Distance-Hybrid (DDH)

n/2*Direction(d1)*Distance(d2)*Speed(s) Direction(): function ranged [0,1] Distance(): function ranged [0,1] Speed(): function ranged [0,1] d1: direction difference of two nodes d2: distance difference of two nodes’ destinations s: difference of nodes’ speeds

Direction Destination Handed over tokens

similar close few

similar far medium

different close medium

different far n/2

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Simulation results (1/2)

Simulator The Opportunistic Network Environment (ONE) simulator http://www.netlab.tkk.fi/~jo/dtn/

Parameter settings

Parameters Value

Area size (m*m) 4500 X 3400

Number of nodes 100 (mobile), 10 (static)

Transmission range (m) 100

Speed (m/s) 0~18

Buffer size (GB) 1 (mobile), 200 (static)

Message size (MB) 0.01 ~ 3

Transmission rate (KB) 250

Movement model Random waypoint

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Simulation results (2/2) Comparison btw. S&W and DDH

DDH can deliver 18% more packets than S&W When destination is fixed

* : # of delivered packets per 1000 relayed packets

MMLABProblem of Previous Approaches

Randomization effect problem It is caused by local view of tendency As number of contacts is increased, direction or distance is

randomized Effect of our proposal gets reduced

Angle = 90°

∴ handover n/2 tokens Angle = 90°

∴ handover n/4 tokens

An illustration Some tokens can be carried in the

same direction movement information that decides

the number of copies relayed becomes meaningless

2nd contact

1st contact

MMLABScenario Model

A DTN area consists of a certain number of subareas or regions

There is a need of DTN between regions due to poor infrastructure or delay tolerant application

How to dissemination messages between regions efficiently

Region 1Region 1 Region 2Region 2

MMLAB Our Approach 3

Prevention of randomization problem using history Area is divided into several sub areas with non uniform distribution Token handover policy

When a source creates the message, it reserves a fixed number of tokens for each sub-area

If the source meets a mobile host toward other regions, it sends the message to the host with pre-reserved tokens

Tokens can be distributed more evenly across the area

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MMLAB Simulation Settings

Simulator: Opportunistic Network Environment (ONE)

Area size: 45 X 34km2

4 sub-areas (20x15km2 each) # of nodes: 500

Intra-area node & Inter-area node

Tx range: 100m Speed: 100km/h, 4~60km/h S&W copies: 32 Packet

# of packets: 1000 (2 packets per each node) Packet size: ~ 30KB

Buffer size big enough

MMLAB Simulation Results Destination is mobile Delivery ratio

= # of delivered packets / # of originated packets

Delivery Probability (20% Inter-area Mobile Nodes)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.5 1 1.5 2

DaysDeliver

y Pr

obab

ility

epi_20snw_20our_20

MMLAB Simulation Results

Overhead ratio= (# of relayed - # of delivered) /

# of delivered

Average number of relay nodes

0

50

100

150

200

250

300

350

400

Epidemic SprayAndWait Region- based

Ove

rhea

d R

atio

10% 20%

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Epidemic SprayAndWait Region- based

# o

f re

laye

d no

des

10% 20%

MMLAB Simulation Results

Avg. latency Med. latency

95000

100000

105000

110000

115000

120000

Epidemic SprayAndWait Region- based

Late

ncy

Avg

.

10% 20%

85000

90000

95000

100000

105000

110000

115000

Epidemic SprayAndWait Region- based

Late

ncy

Med

.

10% 20%

MMLABConclusions

DTNs may play a vital role in future Routing is a key player in DTNs We proposed

Direction-based Distance-based Transportation info-based

Destination’s mobility affects the routing performance The more information, the better routing