Power Aware Wireless Microsensor Networks (The MIT µAMPS Project)

http://www-mtl.mit.edu/research/icsystems/uamps

Anantha Chandrakasan

Massachusetts Institute of Technology

The Team: Manish Bhardwaj, SeongHwan Cho, Travis Furrer, Tim Garnett

Wendi Heinzelman, Nathan Ickes, Rex Min, Piyada Phanaphat, Eugene Shih, Amit Sinha, Paul-Peter Sotiriadis, Alice Wang

The µAMPS System

n A universal substrate for power aware data gathering from a massively distributed wireless network

Sensor& A/D StrongARM RF

Tx/Rx

Battery/DC-DC Conversion

µ-OS (Power Aware Control)

Remote Basestation

Point Solutions

Low Power Techniques:• Low voltage logic and memory• Conditional clocks• Application specific techniques

Chip Details [ISSCC ’00]:190K Transistors, 0.6µm CMOSVDD = 1V

Energy/Sample:StrongARM-1100: 11µJCustom DSP: 26pJ

Hardwired solutions enable No Power Signal Processing

Power Aware System Design

n Focus on operational scenarios vs. point solutionsoQuality on Demand: Provide hardware and software hooks to trade-off

quality and energy oData Centric: Exploit signal statistics

n Exploit attributes of distributed sensor networksoData Correlation: Perform signal processing in the networko Event Driven Operation: Architect network protocols and OS to minimize

active time

n Translate operation policy to optimal system configurationoAPI: Expose power awareness to the application-leveloCompilers : Generate and evaluate network configurations automatically

Focus on scenarios and not point solutions

Signal Processing in the Network

M-sensor cluster

0

1

2

3

4

5

0 10 20 30 40 50

Distance to Basestation

n Collaborative signal processing trading-off computation and communication

M=2

M=3

M=4

M=5

Ed

irec

t/EA

ggre

gatio

nBasestation

Information Architectures

n LEACH: Low Energy Adaptive Clustering HierarchyoAdaptive, self-configuring cluster formationo Localized control for data transfersoApplication specific data aggregation

AB C

Multi-hop Routing Clustering

Cluster Based Protocols

n Cluster formation: distributed or centralized

n TDMA in steady state to eliminate collisions and active time

n Cluster head rotation to evenly distribute energy load

Set-up Steady-state

Clusters Formation

Time

Slot for node i

Slot for node i •••

Frame

Cluster-heads = •

Preliminary Protocol Evaluation

n Preliminary implementation of LEACH in ns complete

n Data aggregation results in significant lifetime extension

n Future work: event driven model and detailed node model

Nu

mb

er o

f n

od

es a

live

Amount of data received at BS

LEACH-CENTRAL

LEACH

Multi-Hop

OS Directed Power Management

offonsleepsleeps3

offonsleepsleeps4

rxonsleepsleeps2

rxonsleepidles1

tx, rxonactiveactives0

RadioSensorMemoryARM

Battery and DC/DC converter

Sen

sor

A/D

Rad

io

Sensor Node

Memory

StrongARM

µ-OS

• OS must decide suitable transitionpolicy based on observed history

-200

0

200

400

600

800

1000

1200

-10 0 10 20 30 40 50 60

s0

s1

s2s3

s4

Po

wer

(m

W)

Transition Latency (ms)

Deeper sleepLower powerMore overhead

OPTIMUM (VDD, f )

MEASUREDNOT VALID(EXTRAPOLATED)

Just In Time Computing

n µAMPS nodes in one of multiple modes: sensor, relay, cluster heado Workload is highly variable

n Adapt power supply to deliver “just enough performance”

ACTIVE IDLE

EFIXED = ½ C VDD2

Fixed Power Supply

ACTIVE

EVARIABLE = ½ C (VDD/2)2 = EFIXED / 4

Variable Power Supply

Data from SA-1100

Power Aware Transforms - IDCT

X2

X6

X1

X5

X3

X7

X4

X0

x2

x3

x4

x5

x6

x7

x1

x0

1-D IDCT on

Columns

Tra

nsp

ose8 x 8

DCT8 x 8 Image

[ ] [ ] [ ] [ ] ( ) ( )

+

+

= ∑ ∑= = 16

12cos

1612

cos,41

,7

0

7

0

ππ vjuivuXvcucjix

u v

Non Power Aware Power Aware

1-D IDCT on Rows

++

+

=

7,763

7,72

7,71

7,70

7,7

1,063

1,02

1,01

1,00

1,0

0,063

0,02

0,01

0,00

0,0

7,7

2,0

1,0

0,0

:...

:::

c

ccc

X

c

ccc

X

c

ccc

X

x

xxx

Power Aware Transforms

Non Power Aware

Power Aware

Power Aware Architectures

n Adjust precision based on incoming data

n Power aware architectures trade energy and quality

Pro

bab

ility

Bit Precision

Speech Data

h[3]

h[2]

h[3] +h[0]

h[3] +h[1]

h[2] +h[0]

h[2] +h[1]

h[2] +h[1]+h[0]

h[3] +h[1]+h[0]

h[3] +h[2]

h[3] +h[2]+h[0]

h[3] +h[2]+h[1]

h[3] +h[2]+h[1]+h[0]

h[1]+h[0]

h[1]h[0]

0

y[n]

x2 Add/Sub

x[n-3]

x[n-2]

x[n-1]

x[n]

LSB MSB

Distributed Arithmetic

Power Awareness Metric

n Diversity in operating scenarios: number and type of events, signal statistics, desired quality, latency, etc.

n Cannot achieve Esystem = Eperfect at all pointsoOptimize at important scenarios (Esystemi di is high)

1−

=

∑∑

Scenariosiperfect

Scenariosisystem

PA dE

dE

i

i

η

Scenario

Eperfect

En

erg

y Esystemdi

Technology Transfer Plans

n Power aware algorithms developed using the ARL ACIDS data set (ARL Federated Lab Program)

n Field experiments using the ARL test bed (Nino Srour)

n April ’00: LEACH framework (ns) provided to Deborah Estrin (DARPA SenseIT Program)

n May ’00: Released the StrongARM OS modifications to eCOS(RedHat)

M1 TankT72 Tank

4 Acoustic Sensor LocationLine of bearing from sensor

4

1

SensorArray

SensorArray

ArraySensorArray

Sensor

3

2

Sensor& A/D

StrongARM RFTx/Rx

Battery/DC-DC Conversion

µ-OS (Power Aware Control)

Remote Basestation

Power Aware Wireless Microsensor Networks (The MIT µAMPS Project)

New Ideas• Power Aware Information Architectures : Signal processing in the network dynamically trading-off computation and communication costs• Scenario-Agile Systems : Hardware and software knobs to adapt power dissipation to desired quality of results, latency and input signal statistics• Just-in-time Processing : Hardware and operating system support for dynamic voltage scheduling, energy-agile algorithms, and precision-on-demand architectures• Power Aware Generators : Translate operational policy to optimal power aware configuration • Metrics and Benchmarks : Figures of merit and benchmarks that capture variability in operating conditions

Impact•Power aware software and hardware fabrics using COTStechnology will enable more than two orders of magnitudevariation in system lifetime (and quality)• New breed of protocols, algorithms and architectures that formalize the notion of energy agility•Generators will enable application developers to create optimal power aware networks without detailed knowledge of power management technologies

Schedule

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5/00 9/00 12/00 3/01 6/01 9/01 12/01 3/02 5/02

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Prel

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µAM

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Sens

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Massachusetts Institute of Technology: Anantha Chandrakasan (PI)

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