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Power Issues in Wireless Sensor Nets

David CullerCS252 Spring 2005

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Outline• Basic model of operation• Node Design a for low power consumption• Operating System Issues• Design of the power-supply subsystem• MAC-level network design for power• Higher-level network design for power• Application level• Important areas of development• Discussion

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Model of operation

• Sleep – Active [Wakeup / Work]• Peak Power

– Essentially sum of subsystem components– MW in supercomputer, kW in server, Watts in PDA– milliwatts in “mote” class device

• Sleep power– Minimal running components + leakage– Microwatts in mote-class

• Average power– Pave = = (1-factive)*Psleep + factive*Pactive

– Pave = fsleep*Psleep + fwakeup*Pwakeup+ fwork*Pwork

• Lifetime– EnergyStore / (Pave - Pgen )

Sleep WakeUP Work Sleep WakeUP Work

Active Active

Duty Cycle

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Passive Vigilance• Sense only when there is something useful to

detect• Listen only when there is something useful to

hear• How do you know?

– By arrangement– By cascade of lower power triggers

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Mote Power Parameters• 1s Microwatts sleep• 10s of milliwatts active (wakeup or work)• Wakeup substantial

– Milliseconds (1000s of instructions)

• 1% Duty Cycle is common– Wakeup matters

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Batteries• Still the best energy store• Issues

– Voltage– Source current– Leakage– Voltage profile– Recharge

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Design of a Low Power Node

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Key Design Elements

• Efficient wireless protocol primitives• Flexible sensor interface• Ultra-low power standby• Very Fast wakeup• Watchdog and Monitoring• Data SRAM is critical limiting resource

proc

DataSRAM pgm

EPROM

timersSensor Interface digital sensors

analog sensorsADC

Wireless NetInterface

Wired NetInterface

RFtransceiver

antenna

serial linkUSB,EN,…

Low-powerStandby & Wakeup

Flash Storage

pgm images

data logs

WD

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Mote Platform Evolution

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802.15.4 Platforms

• Focused on low power • Sleep - Majority of the time

– Telos: 2.4µA– MicaZ: 30µA

• Wakeup– As quickly as possible to

process and return to sleep– Telos: 290ns typical, 6µs max– MicaZ: 60µs max internal

oscillator, 4ms external• Process

– Get your work done and get back to sleep

– Telos: 4MHz 16-bit– MicaZ: 8MHz 8-bit

• TI MSP430– Ultra low power

» 1.6µA sleep» 460µA active» 1.8V operation

• Standards Based– IEEE 802.15.4, USB

• IEEE 802.15.4– CC2420 radio– 250kbps– 2.4GHz ISM band

• TinyOS support– New suite of radio stacks– Pushing hardware abstraction– Must conform to std link

• Ease of development and Test– Program over USB– Std connector header

• Interoperability– Telos / MicaZ / ChipCon dev

UCB Telos

Xbow MicaZ

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TinyOS-driven architecture

• 3K RAM = 1.5 mm2

• CPU Core = 1mm2

– multithreaded• RF COMM stack = .5mm2

– HW assists for SW stack• Page mapping • SmartDust RADIO = .25 mm2

• SmartDust ADC 1/64 mm2

• I/O PADS

• Expected sleep: 1 uW– 400+ years on AA

• 150 uW per MHz• Radio:

– .5mm2, -90dBm receive sensitivity– 1 mW power at 100Kbps

• ADC: – 20 pJ/sample – 10 Ksamps/second = .2 uW. jhill mar 6, 2003

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Microcontrollers• Memory starved

– Far from Amdahl-Case 3M rule

• Fairly uniform active inst per nJ– Faster MCUs generally a bit better– Improving with feature size

• Min operating voltage– 1.8 volts => most of battery energy– 2.7 volts => lose half of battery energy

• Standby power– Recently a substantial improvement– Probably due to design focus– Fundamentally SRAM leakage– Wake-up time is key

• Trade sleep power for wake-up time

– Memory restore

DMA Support

• permits ADC sampling while processor is sleeping

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Radio

• Trade-offs: – wakeup vs resilience / performance– Wakeup vs interface level– Ability to optimize vs dedicated support

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Flash Technology

Data storageData storageCode storageIntended Use

10^5 – 10^710^410^4 – 10^5Erase Cycles

Fast + I/O BusSlow + I/O BusFastRead

High (requires ECC)LowLowBit-Errors

100’s bytes256 bytes1 bitWrite Unit

Fast (MB/s)Slow (60 kB/s)Slow (100 kB/s)Writes

Medium (8K-32K)Small (256)Large (64K-128K)Erase Unit

Fast (ms)Fast (ms)Slow (seconds)Erase

NANDAT45DBNOR

• One write per bit per erase cycle• Flash characteristics:

Not used in current motes

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Power States at Node Level

Sleep WakeUP Work Sleep WakeUP Work

Active Active

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Tiny OS Concepts

• Scheduler + Graph of Components– constrained two-level scheduling model:

threads + events• Component:

– Commands, – Event Handlers– Frame (storage)– Tasks (concurrency)

• Constrained Storage Model– frame per component, shared stack, no heap

• Very lean multithreading• Efficient Layering

structured event-driven executionNever wait or spin

Messaging Component

init

Pow

er(m

ode)

TX_p

acke

t(buf

)

TX_p

ack

et_d

one

(suc

cess

)RX_

pack

et_d

one

(buf

fer)

Internal State

init

pow

er(m

ode)

send

_msg

(add

r, ty

pe, d

ata)

msg

_rec

(type

, dat

a)m

sg_s

end_

done

)

internal thread

Commands Events

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Cooperative Interfaces

• Power management extends std control

– 1000-fold range of power draw– Components informed of intention to

go to sleep– Take internal actions– Propagate control– Scoreboard determined permissible

depth of sleep state– Scheduler drops to sleep on idle– Key interrupts drive wake-up

• Rich communication interfaces– Signal strength– Post-MAC time-stamping– Sub-carrier signaling

Application: query processing

detection, reporting

Radio

Comm

Stack

Clock

Timer

Sensor 3Sensor 2

Sensor

Drivers &

Filters

Sensor1

Power Management

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Power-supply Subsystem• Energy Store• Power Source• Consumer• Management & Control

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Importance or primary buffer• Node is able to operate from capacitors

– Moderate period of time (~week)

• Source active load (mAs !)• Absorb energy input• Perform frequent charge cycles (daily)

– shallow

• Source high voltage recharge of secondary• Power MCU during secondary recharge

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It’s all about leakage• Bigger isn’t better• More doesn’t help• We use two 22 F in series

– operate in the flat

under load (1%, 10 mA)

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Recharging

• High density & Low leakage• Software on MCU manages recharge sequence

– Include temperature compensation– Pulsing charge current (~1x battery capacity) to 80%

• High level charge management and load control

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High Level Management

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Outline• Basic model of operation• Node Design a for low-power consumption• Operating System Issues• Design of the power-supply subsystem• Communication Basics• MAC-level network design for power• Higher-level network design for power• Important areas of development• Discussion

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Basics• Power required to transmit a given distance

grows like the r3 in free space with omnidirectional antenna

– Can be as bad as r7 close to the ground– Slower growth rate with directional, but …

• Power required to route data hop-by-hop a given distance grows only linearly

• Connectivity determined by a host of factors– SNR– Transmission power, receiver sensitivity, distance– Interference– obstructions

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The Basic Primitive• Transmit a packet• Received by a set of nodes

– Dynamically determined– Depends on physical environment at the time– What other communication is on-going

• Each selects whether to retransmit– Potentially after modification

• And if so, when

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Routing Mechanism

• Upon each transmission, one of the recipients retransmit

– determined by source, by receiver, by …– on the ‘edge of the cell’

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off

Communication and Power

• Costs power whenever radio is on– Transmitting, receiving, or just listening

• Transmit is easy, Rcv is what’s tricky– Want to turn it on just when there is something to hear

• Two approaches– Schedule transmission intervals

» Statically, dynamically, globally, locally– Make listening cheap

listenoffRX

TX TX

RX

TX

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S-MACYe, Heidemann, and Estrin, INFOCOM 2002

• Traditional monolithic protocol design

• Synchronized protocol with periodic listen periods

• “Black Box” design– Designed for a general set of

workloads– User sets radio duty cycle– SMAC takes care of the rest

so you don’t have to– Integrates higher layer

functionality into link protocol– Hard to maintain set of

schedules• T-MAC

[van Dam and Langendoen, Sensys 2003]

– Reduces power consumption by returning to sleep if no traffic is detected at the beginning of a listen period

Schedule 2Schedule 1

Wei Ye, USC/ISI

Node 1

Node 2

sleeplisten listen sleep

sleeplisten listen sleep

sync

sync

sync

sync

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TDMA variants• Time Division Media Access

– Each node has a schedule of awake times– Typically used in star around coordinator

» Bluetooth, ZIGBEE» Coordinator hands out slots

– Far more difficult with multihop (mesh) networks– Further complicated by network dynamics

» Noise, overhearing, interference

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Complexity of Connectivity

• Direct Reception

• Non-isotropic• Large variation in affinity

– Asymmetric links– Long, stable high quality links– Short bad ones

• Varies with traffic load– Collisions– Distant nodes raise noise floor– Reduce SNR for nearer ones

• Many poor “neighbors”• Good ones mostly near, some far

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Low Power Listening (LPL)• Energy Cost = RX + TX + Listen• Scheduling tries to reduce listening• Alnternatively, reduce listen cost• Example of a typical low level

protocol mechanism• Periodically

– wake up, sample channel, sleep• Properties

– Wakeup time fixed– “Check Time” between wakeups variable– Preamble length matches wakeup interval

• Robust to variation• Complementary to scheduling• Overhear all data packets in cell

– Duty cycle depends on number of neighbors and cell traffic

RX

wak

eup

wak

eup

wak

eup

wak

eup

wak

eup

wak

eup

wak

eup

wak

eup

wak

eup

TX

sleep sleep sleep

sleepsleepsleep

Node 2

Node 1time

time

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The Common Case: Data Gathering• Collection of nodes take periodic samples• Stream data towards a root node

• Root announces interest– depth = 0

• Nodes listen• When hear ‘neighbor’ with smaller depth

– start transmitting data to “best” lower neighbor– set own depth to one greater (and include with data)

• Data transmission continuously reinforces & adjusts routes• Aggregation within nodes or within the tree

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Radio Cells

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Continuous Network Discovery

0

112

2

2

22

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Local Operations => Global Behavior

• Nodes continually ‘sense’ network environment– uncertain, partial information

• Packets directed to a “parent” neighbor– all other neighbors “hear” too– carry additional organizational information

• Each nodes builds estimate of neighborhood– adjusted with every packet and with time

• Interactively selects parent– # trans := 1/ParentRate + #trans(Parent->root)

• Routes traffic upward⇒Collectively they build and maintain a stable spanning tree⇒ takes energy to maintain structure goodnes

s% link

parent?

child?depth

node #

...

.675yes36

.790yes117

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Power-aware Routing• Cost-based Routing

– Minimize number of hops– Minimize loss rate along the path– Perform local retransmissions, minimize number along path

• Energy balance– Utilize nodes with larger energy resources

• Utilize redundancy– Nodes near the sink route more traffic, hence use more energy– Give them bigger batteries or provide more of them and spread

the load– Randomize routes

• Utilize heterogeneity– Route through nodes with abundant power sources

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Communication Scheduling• TDMA-like scheduling of listening slots• Node allocates

– listen slots for each child – Transmission slots to parent– Hailing slot to hear joins

• To join listen for full cycle– Pick parent and announce self– Get transmission slot

• CSMA to manage media– Allows slot sharing– Little contention

• Reduces loss & overhearing• Connectivity changes cause mgmt traffic

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In-network Processing• Best way to reduce communication cost is to not

communicate• Compute at the sensor

– Only communicate important events

• Compute over localized regions of the network– Distributed detection– Validation

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Exceptional Event Detection• Challenge: Detecting Exceptional Events

– Rare most time spent monitoring noise– Random nearly continuous sampling– Ephemeral low bound of duty-cycling off time

• Approach: Passive Vigilance– Multi-modal, low-power sensors – Duty-cycled, where possible and arranged in– Energy-Quality hierarchy with low (E, Q) sensors– Triggering higher (E, Q) sensors, and so on…

• How to Estimate Energy Consumption?– Power = idle power + energy/event x events/time– Estimate event rate probabilistically: p(tx) =

from ROC curve and decision threshold for H0 & H1• How to Optimize Energy-Quality?

– Let x* = (x1*, x2*,..., xn*) be the n decision boundaries between H0 & H1. for n processes. Then, given a set of ROC curves, optimizing for energy-quality is a matter of minimizing the function f(x*) = E[power(x*)] subject to the power, probability of detection, and probability of false alarm constraints of the system.

FalseAlarmRate

EnergyUsage

HighLow

LowHigh• Trigger network includes hardware wakeup,

passive infrared, microphone, magnetic, fusion, and radio, arranged hierarchically

• Nodes: sensing, computing, and commprocesses

• Edges: <↓Energy, ↑PFA> <↑Energy, ↓PFA>

Energy-Quality Hierarchy

sub. SPOTS 05

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Example: Detection & Tracking

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Key Areas to Improve• Low leakage SRAM

– 1T sram

• Low leakage supercaps• Communication accelerators• Radio wake-up cascade

– Very low power detection of signal triggers receiver

• Robust communication protocols• Sensor detection cascades

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Discussion

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Where to read for more

• Telos: Enabling Ultra-Low Power Wireless Research, Joseph Polastre, Robert Szewczyk, David CullerTo appear in The Fourth International Conference on Information Processing in Sensor Networks: Special track on Platform Tools and Design Methods for Network Embedded Sensors (IPSN/SPOTS), April 25-27, 2005

• Perpetual Environmentally Powered Sensor Networks, XiaofanJiang, Joseph Polastre, David CullerTo appear in The Fourth International Conference on Information Processing in Sensor Networks: Special track on Platform Tools and Design Methods for Network Embedded Sensors (IPSN/SPOTS), April 25-27, 2005

• Versatile Low Power Media Access for Wireless Sensor Networks, Joe Polastre and David Culler, The Second ACM Conference on Embedded Networked Sensor Systems, Nov. 2004

• "Design of a Wireless Sensor Network Platform for Detecting Rare, Random, and Ephemeral Events",Prabal Dutta, Mike Grimmer, Anish Arora, Steve Bibyk, and David Culler, In The Fourth International Conference on Information Processing in Sensor Networks (IPSN'05), 2005.