An Energy-Efficient Architecture for DTN

Throwboxes

Author: Nilanjan Banerjee, Mark Corner, Brian N. Levine

Presenter: Zhe Chen

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What are Disruption Tolerant Networks ?

DTNs are sparse networks with low node density

Nodes are largely disconnected

Transfer data through intermittent contacts

Come naturally from the applications they support

Wildlife tracking

Underwater exploration and monitoring

Or from fragility and failures in the network itself

Major natural disasters

Jamming and Noise

Power Failure

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Examples of DTN

UMass DieselNet [Burgess et al. Infocom

06]

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Limitations of Mobile DTNs

Do you have enough capacity in your DTN?

Most influential factor in DTN performance?

the frequency and number of contact opportunities

How can we increase contacts?

more mobile nodes=$$$$

or change the mobility pattern of nodes

(mobility patterns inherent to a particular network)

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Observation

Place a relay and create a

virtual contact

Route B

Route A

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Solution : Throwboxes

Throwboxes: stationary battery powered relays

has radios and storage

cheap, small, easy to deploy

solar power=perpetual operation

Challenges

where do we place these boxes ? [Wenrui et al. : Mass 06]

make them ultra low power for perpetual operation

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Solution : Throwboxes

Throwboxes: stationary battery powered relays

has radios and storage

cheap, small, easy to deploy

Challenges

Previous paper: where to place these boxes thus maximize network performance: ? [Wenrui et al. : Mass 06]

Power management: trade of between nodes’ lifetime and connection opportunities

Aim: maximize performance and simultaneously meet individual energy constraints

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Outline

Design Goals

Throwbox Architecture

Mobility Prediction Engine

Lifetime Scheduler

Throwbox Prototype and Deployment

Experimental Results

Power Savings

Routing Performance

Conclusions

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Throwbox Design Goals

Small form factor, portable and cheap

Can be placed practically anywhere in the network

Design should be general

Applicable to wide variety of DTNs

Should not use prior information about mobility patterns

Run perpetually on solar panels of the size of the box

Translates to a small average power constraint

Optimization goal: maximize the number of packets forwarded

Primary source of overhead

Energy cost of neighbor discovery

Idle, on and off, searching contacts

DTN networks

Sparse, is it worth the cost of waking the node

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Overview

Mobile Node

802.11 Range

XTend Radio Range

Tier-0Mote

Xtend Radio

Mobility Prediction

Lifetime Scheduler

Tier-1 Stargate

802.11 Radio

Speed and direction beacon

Contact

RoutingEngine

Packet Storage

Throwbox

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Buses transmit: pos, dir, and speed.

Throwbox predicts:

if bus will reach data-range before tier-1 can be woken?

length of time in range(is it worth?)

Mobility Measurement and Prediction

• Track the probability the node enters data-range given series of cells it must traverse

• Statistics kept on each cell• Markovian assumption allows simple calculation

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SchedulingEach contact incurs fixed cost to wake tier-1 platform.

Most efficient strategy: wake for largest contacts

saves energy, but mostly designed to limit power

0-1 Knapsack problem reduces to this scheduling problem

choose items to carry s.t. (∑weight ≤ capacity) and maximizes ∑value.

C1 ... Cn events, each has

total energy cost ei (weight), bytes transferred di (value)

Energy constraint P ∙t (capacity)

Solution is subset of events s.t. (∑ei ≤ P∙t) and maximizes ∑di

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Token Bucket ApproachTake this event, next event, or both?

Token rate = average power constraint

Estimate the size & energy cost

ignore if insufficient tokens

Compute tokens generated till next event

based on tracking inter-arrival times

If sufficient tokens for both events

take current event

If current event larger than next connection take it

otherwise wait for next one

new new tokenstokens

Battery capacity

?

Events

Takenevents

Ignored or skipped events

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Experimental SetupHow effective is our energy management design?

compare with single platform periodic wake up (PSM*)

Two-platform with mobility prediction (WoW*)

Can we really run it on solar-power?

At reduced consumption does it still help?

use the successful delivery metric

Use trace-based simulation and deployment

equipped 40 busses with XTend radios

placed three Throwboxes for several weeks

record contact opportunities with buses (both radios)

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Throwbox Placement

Throwbox deployed on bikes in UMassDieselNet

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Power Savings (equivalent transfers)

20x less power than periodic wakeup

5x less power than just mobility prediction

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Throwbox WoW* PSM*

Ave

rag

e P

ow

er (

mW

)

TelosB Disovery CostIdle CostTokens LeftTransition CostData Transfer

80 mW

410 mW

1710 mW

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Routing performance

Throwbox at 80mW equivalent to best case.

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3 8 13 18 23 28

Number of Packets per hour (per node)

Pa

ck

ets

De

liv

ere

d (

%)

No Throwbox

Throwbox (80mW)

Always-on Throwbox

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Conclusions Placing relays in DTNs can produce huge performance boost

Motivates studies on adding Meshes or Infostations to DTN

Tiered Architecture can produce substantial energy savings

Can lead to 31 times less energy consumption

Need for systems to adapt to variable solar power

Multi-radio systems are energy efficient in sparse networks

Need for more efficient use of the XTend channel

Low bitrate radio can be used to gather packet info

Need to integrate power management into routing

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Energy performance

Need larger cell, but perpetual operation possible

Unanswered questions about solar variation

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2:00 AM 8:40 AM 3:20 PM 10:00 PM

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