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An Energy-Efficient Architecture for DTN
Throwboxes
Author: Nilanjan Banerjee, Mark Corner, Brian N. Levine
Presenter: Zhe Chen
2
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
3
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
10
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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
0
200
400
600
800
1000
1200
1400
1600
1800
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.
0
10
20
30
40
50
60
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
0
50
100
150
200
250
300
2:00 AM 8:40 AM 3:20 PM 10:00 PM
Time
Ba
tte
ry C
ap
ac
ity
(m
Ah
)