Enhancing DTN capacity Enhancing DTN capacity with Throwboxeswith Throwboxes

(work-in-progress)(work-in-progress)

Wenrui Zhao, Yang Chen, Mostafa Ammar, Mark Corner,

Brian Levine, Ellen Zegura

Georgia Institute of TechnologyUniversity of Massachusetts Amherst

Delay Tolerant Networks (DTN) DTNs: non-Internet-like networks

Intermittent connectivity Large delays High loss rates

Examples of DTNs Tactical networks, disaster relief,

peacekeeping Interplanetary networks, rural village

networks Underwater acoustic networks

DTN features Store-Carry-and-forward Message switching

Capacity Limitation in DTNs

DTNs are intermittently connected Potentially low throughput, large delay

Question: enough capacity for applications? What if not?

Use radios with longer range Deploy a mesh network as infrastructure Message ferrying

This presentation: Throwboxes

Enhancing DTN Capacity

MF

SM

D

Our Work on MF/DTN

Ferry Route Design Problem [FTDCS 03] MF with Mobile Nodes [MobiHoc 04] Efficient use of Multiple Ferries [INFOCOM 05] The V3 Architecture: V2V Video Streaming [PerCom 05] Ferry Election/Replacement [WCNC 05] MF as a power-savings device [PerCom 05] Multipoint Communication in DTNs/MF [WDTN 05, WCNC 06] Power Management Schemes in DTNs/MF [SECON 05,

PerCom 05] Road-side to Road-side relaying using moving vehicles

[WCNC 06]

Throwboxes

Basic idea: add new devices to enhance data transfer capacity between nodes

Deploy throwboxes to relay data between mobile nodes

Throwboxes are: small, inexpensive, possibly dispensable, battery-

powered wireless devices Some processing and storage capability Easy to deploy and replenish

Throwboxes

Processor Intel PXA255 400MHz

Memory 64MB SDRAM

32MB Flash

Power consumption

< 500mA

Size 3.5’’ x 2.5’’

Weight 47g

Example: DTN w/out Throwboxes

Example: DTN w/ Throwboxes

UMassDiesel DTN Example

w/out TB

w/ TB

Total contact duration (sec)

631 11927

Effective capacity (Kbps)

3.5 66.3

Delay (sec) 63012 3120

Data transmission between bus 38 and bus 45 A single throwbox achieves an improvement factor of 19 for

both capacity and delay

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throw-boxBus 30Bus 31Bus 34Bus 35Bus 36Bus 38Bus 39

Bus 39eBus 45

Main Question

How to best deploy ‘s Where? How to route through them? When? -- Later work

Throwbox Deployment & Routing Framework

Objective: throughput enhancement Important to deliver data May improve delay too

Deployment issue Where to place throw-boxes?

Routing issue How data are forwarded?

Contact-oblivious Contact-based Traffic and Contact

based Single path routing Multi-path routing Epidemic routing

Network Model

DTN consists of mobile nodes Relative traffic demand between nodes bij

Total throughput λ Given inherent capacity (w/out TBs) as a

function of: Contacts – dictated by mobility patterns Data rate

1,

ji

ijb

Throwbox Assumptions

Sufficient energy supplies No interaction between throwboxes Deployed to a given set of potential locations

Center of Grid Cells Deployment Vector (0/1 vector)

Throwbox Deployment & Routing Framework

Contactoblivious

Contactbased

Traffic & Contact based

Multi-pathrouting

Single pathrouting

Epidemic routing

Deployment approach

Routing approach

Random or Regular Deployment

Throwbox Deployment & Routing Framework

Contactoblivious

Contactbased

Traffic & Contact based

Multi-pathrouting

Single pathrouting

Epidemic routing

Deployment approach

Routing approach

Random or Regular Deployment

Multi-Path Routing – Multi-Path Routing – Traffic and Contact-Aware DeploymentTraffic and Contact-Aware Deployment

Need to determine Deployment locations of throwboxes Routing paths and traffic load on each path

Performance objective Given m throwboxes, maximize total throughput λ

such that traffic load λbij is supported from node i to j

Multi-Path Routing – Multi-Path Routing – Traffic and Contact-Aware Deployment Traffic and Contact-Aware Deployment

Formulated as an 0/1 linear programming problem Throwbox deployed at location 1 Solution also gives optimal flow vector describing

use of multiple paths

NP-hard to solve optimally

Greedy Heuristic

Deploy throwboxes one by one

Given throwbox locations, (2) is a concurrent flow problem Solved by network flow techniques or linear programming

tools

(1) for i=1 to m do(2) find location L that maximizes λ (3) deploy a throwbox at location L(4) end(5) compute routing

Throwbox Deployment & Routing Framework

Contactoblivious

Contactbased

Traffic & Contact based

Multi-pathrouting

Single pathrouting

Epidemic routing

Deployment approach

Routing approach

Random or Regular Deployment

Multi-Path Routing – Multi-Path Routing – Contact-Based DeploymentContact-Based Deployment

Throwbox deployment is based on contact information, but not traffic information Benefits varying traffic patterns May not be optimal for specific traffic

Maximize Absolute contact enhancement

Maximize absolute enhancement of contact between nodes Relative contact enhancement

Maximize relative enhancement of contact between nodes

Throwbox Deployment & Routing Framework

Contactoblivious

Contactbased

Traffic & Contact based

Multi-pathrouting

Single pathrouting

Epidemic routing

Deployment approach

Routing approach

Random or Regular Deployment

Single Path RoutingSingle Path Routing

Single path routing Data for a S-D pair follow a single path Adapt greedy algorithm for multi-path routing by

enforcing the “single path” requirement

Throwbox Deployment & Routing Framework

Contactoblivious

Contactbased

Traffic & Contact based

Multi-pathrouting

Single pathrouting

Epidemic routing

Deployment approach

Routing approach

Random or Regular Deployment

Epidemic RoutingEpidemic Routing

Epidemic routing (ER) Difficult to characterize traffic load among nodes

because of flooding ER exploits all paths to propagate data

Multi-path heuristic Proportional allocation heuristic

Performance Evaluation

Objectives Utility of throwboxes in performance enhancement Performance impact of various routing and deployment approaches

ns simulationdeployment/routing

computation

traffic demand

node mobility

throwbox locations

routing path/load

Simulation Settings Node mobility models

Predictable/constrained: UMass model based on measured bus trace

Random/unconstrained: Random waypoint model Random/constrained: Manhattan model

Simulation Parameters 9 nodes in a 25Km x 25 Km area 802.11 MAC, radio range: 250m, bandwidth: 1Mbps 20 source-destination pairs, message size is 1500 bytes,

Poisson message arrival with same data rate FIFO buffer, buffer size is 50000 messages

Delivery Ratio vs. Number of Throwboxes

Multi-path routing

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T &C AwareAbsoluteContactRelativeContact

RandomGrid

Delivery Ratio vs. Number of Throwboxes

Single path routing

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RandomGrid

Delivery Ratio vs. Number of Throwboxes

Epidemic routing

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Number of throw-boxes

MultiPathProportional

AbsoluteContactRelativeContact

RandomGrid

Delay vs. Number of Throwboxes(High Traffic Load)

Multi-path routing

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Number of throw-boxes

T & CAbsoluteContactRelativeContact

RandomGrid

Delay vs. Number of Throwboxes(Low Traffic Load)

Multi-path routing

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RandomGrid

Summary of Simulation Results

RWPmobility

Manhattanmobility

UMassmobility

Multi-pathrouting

Single pathrouting

Epidemic routing

Delay improvement(high traffic load)

Throughput improvement

Delay improvement(low traffic load)

Contact based

T & C

Multi-pathrouting

Single pathrouting

Epidemic routing

Contactoblivious

T & C/Contact based

T & C /Contact based

Contactoblivious

Contactoblivious

High

Low

Throughputimprovement

Routingapproach

Summary of Simulation Results (2)

Summary Study the use of throwboxes for capacity enhancement in mobile

DTNs

Develop algorithms for throwbox deployment and routing Routing: multi-path, single path, epidemic Deployment: traffic and contact, contact-based, contact-oblivious

Evaluate the utility of throwboxes and various routing/deployment approaches Throwboxes are effective in improving throughput and delay,

especially for multi-path routing and predictable node mobility

Questions?

Message Ferrying

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