procrastination might lead to a longer and more useful life
DESCRIPTION
Procrastination Might Lead to a Longer and More Useful Life. Prabal Dutta, David Culler, and Scott Shenker University of California, Berkeley. In sensor networks, energy is the defining constraint. Battery-operated2 “AA” batteries 10 mA active current 10 uA sleep current 1% duty cycle - PowerPoint PPT PresentationTRANSCRIPT
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Procrastination Might Lead toa Longer and More Useful Life
Prabal Dutta, David Culler, and Scott ShenkerUniversity of California, Berkeley
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In sensor networks, energy is the defining constraint
• Battery-operated 2 “AA” batteries– 10 mA active current– 10 uA sleep current– 1% duty cycle
• CPU 10 MIPS• RAM 4 KB to 10 KB• ROM 32 KB to 128 KB• Flash 512 KB to 1 MB• Radio 40 kbps to 250 kbps
2000 mA-Hr
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Many scientific data collection applicationsstream sensor data from sensor nodes to sinks
SELECT *FROM sensorsSAMPLE PERIOD 5 min
SELECT time, epoch, id,parent, voltage, depth, hum,
temp, toplight, botlight
FROM sensorsSAMPLE PERIOD 5 min
“Redwoods” [Tolle05]
“Great Duck Island” [Szewczyk04]
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Necessity as the mother of invention:Sensornets run at a few percent duty cycle
Year Deployment MAC DCPeriod (s)2003 GDI Polled 2.2% 0.54-1.0852004 Redwoods Sched 1.3% 3002005 FireWxNet Sched 6.7% 9002006 WiSe Sched 1.6% 60
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Scheduled Communications
G D
Tpkt
tpkt
tguard
G D
G D G D
G D G DRX
TX
DC = tguard/Tpkt + tpkt/Tpkt
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Polled Communications
P P
Tpoll
tpoll
P
D
P D
D
P D
Tpkt
tpkt
Tpoll + tpoll
DDD D DD D
RX
TX
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Even at 1-2% duty cycles, idle listening dominates power budget*
Low-Power Listening(idle listening)
R. Szewczyk, A. Mainwaring, J. Polastre, D. Culler, “An Analysis of a Large Scale Habitat Monitoring Application”,ACM SenSys’ 04, November, 2004, Baltimore, MD
* See paper for detailed derivations
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Procrastinate by decouplingsensing and sending periods
SELECT nodeid,timestamp,
temperature,humidity,pressure,totalrad,photorad
FROM sensorsSAMPLE PERIOD 5
minREPORT PERIOD 1
day
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Procrastination:Opportunities and challenges across the network
stack
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Network
Transport
Application
Link
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Network layer protocols are chatty
• Frequent routing beacons ensure availability
• But topology maintenance is expensive
• Why maintain the topology if it goes unused?
• Delay topology (tree) formation until needed
• However, interactive use could justify more frequent communications
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Network layer challenges: Establishing the topology
• How would you quickly establish a gradient?
• Would you pick aggressive (long) links?
• Would you cache old state (perhaps a day old)?
• Would you try to use old state first?
• Would you prefer to build a new topology?
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Quickly flooding the routing beacon with Ripple
• Start with a basic flooding protocol
• Modify the retransmission timer– Delay proportional to RSSI, which
“schedules” network approximately
• Related work– Trickle [Levis04]– SRM [Floyd97]
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Network
Transport
Application
Link
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Transport layer opportunities
• Improve reliability byusing “good” links quickly
• Send data hop-by-hop and may achievehigher throughput since RTT is smaller
• Avoid intra-path interferencedue to multi-hop wireless flows
• Avoid expensive end-to-end retransmissions
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But how to buffer route-throughbundles with a limited storage?
• Add cheap NAND flash to nodes– 1GB spot price ~ $7– > 40% annual price decline– 68% decline in last year
• Energy-efficient– 100x less costly to write a byte
than send a byte over radio– Can write entire contents with
just a few percent of energy in AA batteries Source: DRAM
Exchange
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Procrastination: opportunities and challenges across the network stack
Network
Transport
Application
Link
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At the application layer, compresssensor readings before transmission
• Temperature
• Light Sou
rce:
[Tolle05]
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Lots of CPU cycles for compressing prior to storage or communications
• Prior to transmission– O(100K) instructions for O(1 byte) transmitted– Massive asymmetry in computation and communication– Unlikely to change: Moore’s Law vs Maxwell’s Law
• Prior to storage– O(10K to 1M) instruction for O(1 byte) written– Massive asymmetry in computation and storage– Unlikely to change: [Super-]Moore’s Law vs Moore’s Law
• But raises some new questions– What compression algorithms should be used?– What logical and physical data structures should be
used?
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Network
Transport
Application
Link
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Picking good links quicklymight not be too difficult
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Caveat: Channel access costs stilldominate infrequent communications
Global “Real” Time
Node L
oca
l Tim
e
t
αt
βt
α > 1 > β
α 1 β
*CSMA-LPL costs don’t scale with time, but have high fixed costs for transmission
t’ RXTX ONON
Min(RXon) = 2 x MaxDrift + Startup + Jitter
Research on:(1) Minimizing clock skew(2) Speeding up radio startup(3) Providing bus arbitration(4) Lowering RSSI detection cost
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Conclusion
• Delay offers many optimization opportunities– Link– Network– Transport– Application
• But standby power dominates budget– 90% of node power– 25% of laptop power– 8% of the UK electricity in 2004
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Discussion
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The total load on a 1-hop nodein an n-hop network is: 2(n2-1)+1
1-hop
2-hop
3-hop
n-hop
Area n2-1
Area 1
1-hop area must: (a) route-through (RX & TX) 2-hop to n-hop traffic: 2(n2-1) (b) originate (TX) 1-hop traffic: 1
N 2(n2-1)+11 12 73 174 315 496 71
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Rapid time synchronization
• [Kusy06, Sallai06]• Achieved
– 2.7 us average error– 26 us max error
• Over– 11 hop network– 45 nodes
• In 4 seconds• With two-phase flood• Using fixed neighbors
• But how to flood without fixed neighbors?
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What could storage enable?Delay expensive operations to reduce overhead
• Many operations have high startup costs– Sending packets– Writing to flash or disk– Acquiring sensor data
• Often better to postpone expensive operations
• But what to do with all the flowing data? Store it
• Related work– Nagle’s algorithm– Cache coherence– Disk buffering– The FedEx Truck
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What else could storage enable?Break synchrony between subsystems
• Many subsystems are synchronous– Sensor and signal conditioning– Signal conditioning and A/D conversion– Packet arrival and processing
• Asynchrony allows each element to operate optimally. Storage is the glue between elements
• Related work– Elastic pipelines [Sutherland89]– Bulk synchronous-parallel [Valiant89]– Queue element in Click [Morris99]– Fjords in streaming [Madden02]– Asynchronous computer architectures
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Digging deeper into overhead
Typical• Data generation rate (raw): 20 bytes / 5 min• Data generation rate (raw): 0.53 bits / sec• Packet overhead (headers): 50% (17B hdr/36B data)• Data generation rate (w/ overhead): 0.80 bits / sec• Radio data rate (raw, CC1000): 40 kbps• Radio data rate (duty cycled at 2.2%) 880 bits / sec• Over-provisioning factor (1-hop) 1,100 times• Over-provisioning factor (2-hop) 157 times• Over-provisioning factor (3-hop) 64 times• Over-provisioning factor (4-hop) 35 times• Over-provisioning factor (5-hop) 22 times• …• Optimally provisioned for 23-hop, uniformly distributed network
Radio Capacity
Node Data Rate
880 bits /sec
0.8 bits / sec= 1,100=
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Bottom line:Streaming is not efficient
Radio On-Time
Radio Active Time
19 minutes
40kbps/10KB= 1,100=
Streaming delivers a trickle through a fire hose
A typical 1% duty cycle translates to over 14 minutes of daily on time.
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Streaming every sensor sample is inefficient
• TCP doesn’t send single-byte packets (exception: TCP_NODELAY); bytes are coalesced into larger packets
• Operating systems don’t write to disk; they cache multiple writes and flush
• College students don’t do their laundry daily; they wait until their underwear runs out
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Related work: Delay-Tolerant Networking
• Others [Fall03] have suggested DTN used by necessity when:– No contemporaneous path from source to sink is available– End-to-end round-trip times exceed a few seconds– Nodes exhibit mobility over short time scales– Links exhibit high loss rates over short periods of time
• Others [Mathur06] have suggested NAND flash be used because of energy-efficiency. This proposal explores how
• This proposal suggests DTN be used by choice when:– Delay is tolerable– Energy-efficiency is important– The network is otherwise over-provisioned
• No characterization of the energy-efficiency of DTN in the literature over streaming data transfer