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An Energy Efficiency Evaluation for Sensor Nodes with Multiple Processors, Radios and Sensors

Deokwoo Jungdeokwoo.jung@yale.eduEmbedded Networks and Applications Lab (ENALAB)Yale University

*Research sponsored by NSF

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Challenges for designing energy efficient wireless sensor network

Large set of Application domain From a simple data logging to a complex signal processing

Long sleep period For surveillance application, typically more than 90 % of lifetime is in

a sleep state Dynamic roles

A sensor node can perform various functionalities from a cluster head to a simple end node

A large dynamic range of trade-off between power and performance

The fundamental limit of electronics in single type of hardware

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CPU Trend Computation cost of 32bit-FFT and energy efficiency

comparison in CPUs

1

10

100

1000

10000

ComputationEfficiency (uJ/bit)

Sleep power(uW) Wakeup overhead(uJ)

32-bit 416Mhz PXA271 CPU

16-bit 8Mhz MSP430 CPU

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Radio Trend Data transfer cost of and energy efficiency comparison in

Radios

1

10

100

1000

10000

Data TransferEfficiency (nJ/bit)

Listening power (mW) Wakeup overhead(uJ)

802.11b-SMC2532802.15.4-CC2420

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Energy Trade-off in different roles

2000 4000 6000 8000 100000

1

2

3

4

5

6

7

8

Data size (byte)Ave

rag

e p

ow

er

co

nsu

mp

tio

n (

mW

)

Data Collecting CeterProcessing Ceter

=1/1min

high-end CPU+ low-end radios

low-end CPU+ high-end radios

Large dynamic range of operation Collect and Forward Processing and Report

Computation Load

Communication Load

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Related work for energy optimization Energy-Efficient Platform

Telos [Berkley], ZN1[HITACHI], Stargate[Crossbow], mPlatform [MS], LEAP[UCLA], ASPIRE [Yale-UCLA-UMASS]

Energy-Aware Wireless Communication LEACH: Energy-efficient communication protocol for wireless sensor networks

[Heinzelman00] S-MAC: An Energy-Efficient MAC Protocol for Wireless Sensor Networks[Wei02] A MAC protocol to reduce sensor network energy consumption using a wakeup

radio.[Matthew05] Network-Wide Energy Optimization

SPAN: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks [chan01]

GAF: Geography-informed energy conservation for ad hoc routing, [Chu01] STEM: Topology management for energy efficient sensor networks[Curt02]

Cross-Layer Design and Optimization Cross-layer design for lifetime maximization in interference-limited wireless

sensor networks [Madan05] Physical layer driven protocol and algorithm design for energy-efficient wireless

sensor networks [Eugene01]

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Our contribution

Reconfigurable platforms - high & low-end components in one platform

A large dynamic range of energy and performance trade-off Using the most efficient component subset for each task

Its energy efficiency modeling has not been studied well Energy efficiency gain given a hardware set? Parameters of affecting energy efficiency? Optimal operation points given workload?

Inter – Component Communication Link

CameraMotion Sensor

PXA271 TI MSP802.15.4CC2420

802.115006XS

Sensor CPU Radio

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Analytical Model of Evaluating Reconfigurable platform

Main design consideration factors Component Interconnect => Combination of each

component The choice of hardware => Lifetime bound

Predicting energy behavior is a key step toward optimum reconfigurable platform design

Quantifying energy efficiency Estimating energy efficiency gain Identifying key parameters for energy efficiency

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Reconfigurable sensor platforms Dual-Platform with Serial Interface Straight-forward SERIAL design between high-end and low-end platform Limited binding among system components Lowest interconnect protocol overhead -> Lowest latency Limited Bandwidth (< 3.4 Mbps at I2C in High Speed mode)

Low-End Radio(802.15.4)

High-End Radio(802.11)

Low-End CPU(TI MSP)

Flash

SDRAM

Control

High-End CPU(PXA271)

Flash

SDRAM

Control

CIF CIFRIFRIF RIF RIF

MIF MIF

Low-End Sensor(Motion Sensor)

High-End Sensor(Camera)

IO IO

MUX

Real Time Clock

Voltage Regulator

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Reconfigurable sensor platforms Reconfigurable Platform with reconfigurable interconnect

Maximum Reconfigurability, Complexity, and Power Smallest latency and highest throughput. Maximum range of power mode -> Fine-grained power control

Low-End Sensor(Motion Sensor)

High-End Sensor(Camera)

Real Time Clock

Voltage Regulator

Low-End Radio(802.15.4)

High-End Radio(802.11)

Low-End CPU(TI MSP)

FlashSDRAM

High-End CPU(PXA271)

MIF MIF

IO IO

FlashSDRAM

Reconfigurable InterconnectInter Component

Router Shared RAM and ArbiterRIF-1

RIF-1

RIF-2

RIF-2

MIF MIFIO IO

IO IO Component Power control

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Architecture abstraction It’s all about path combination…

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Model Sensor Operation as a Semi-Markov Decision Chain

Trigger-Driven Energy Management Model Power mode, time variable, and

transition cost of current state are determined previous decision action A, e.g {l,h}={Low-end CPU, High-end radio }

Embedded chain in processing stage and communication stage characterizes workload profile in each stage.

Pre-processing

StageS0

ProcessingStage S1

Comm.Stage

S2

olAP ,,1 11

ooAP ,,2.0 22

hlAP ,,8.0 33 ooAP ,,1 44

L H O

Using Low-End Using High-End Off

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An Energy Efficiency Evaluation for Sensor Nodes with Multiple Processors, Radios and Sensors Closed form Energy Efficiency Formula

Solving Bellman equation derived from semi Markov decision process

Decision vectors of CPU and radios

(Arrival rate, Proc.time in low-end CPU, Proc.time in high end CPU,

Comm.time in low-end Radios, Comm.time in high end Radios,

Function of Uk

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An Energy Efficiency Evaluation for Sensor Nodes with Multiple Processors, Radios and Sensors Graphical Analysis Example of Energy Efficiency evaluation

simple low/high-end node and architecture with dynamic interconnect

δ

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An Energy Efficiency Model VerificationUsing LEAP node Architecture*

(*) The low power energy aware processing (LEAP) embedded networked sensor system. In IPSN ’06

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500 1000 1500 2000 2500 3000 3500 40000

1

2

3

4

Optim

al A

vera

ge

Pow

er

Consu

mptio

n

g* (m

W)

500 1000 1500 2000 2500 3000 3500 4000

5

10

rl

500 1000 1500 2000 2500 3000 3500 4000

5

10

Energ

y E

ffic

iency

Ratio

s sl

500 1000 1500 2000 2500 3000 3500 4000

2

4

Iteration

rs

Low-End Node Reconf. Interconnect Static Interconnect

An Energy Efficiency Evaluation for Sensor Nodes with Multiple Processors, Radios and Sensors Simulation result – Optimal Average Power Consumption

ends-Lowwer with Opt.Avg.Po

dynamicwer with Opt.Avg.Po

ends-Lowwer with Opt.Avg.Po

Staticwer with Opt.Avg.Po

Staticwer with Opt.Avg.Po

dynamicwer with Opt.Avg.Po

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An Energy Efficiency Evaluation for Sensor Nodes with Multiple Processors, Radios and Sensors Simulation result – Upper Bound of Energy Efficiency Gain

0 200 400 600 800 1000 1200 14000

50

100

Ave

rag

e O

ptim

al

Po

we

r C

on

su

mp

tio

n

g* l (m

W)

0 200 400 600 800 1000 1200 14000

20

40

60

E

ne

rgy E

ffic

ien

cy G

ain

s

0 200 400 600 800 1000 1200 14000

20

40

60

Iteration

r

Upper Bound Simulation

Upper Bound Simulation

Low-End Node

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How much can we afford to spend on the interconnect ? Numerical result – Interconnect Chip Power Budget

0 50 100 150 200 250 300 350 400 450 5000

1

2

3

4

5

6

Interconnect Chip Power, PInC

(mW)

Ene

rgy

Eff

icie

ncy

Gai

n,

r

= 1/5min, = 5.06 = 1/5min, = 6.06 = 1/1hr, = 6.27

nodes end-lowwer with Opt.Avg.Po

ct interconne dynamicwer with Opt.Avg.Po

Dynamic Range of Power Mode in Platform

δ=6.06

δ=5.06>2x

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Conclusion and Future work Follow the guideline before you build!

Energy efficiency evaluation model of Reconfigurable platform Framework to pursue a design flow for sensor platform with

multiple sensors, CPUs, and radios. Opportunity in designing an interconnect chip – Might improve its

energy efficiency by 8 X Design target for Interconnect chip: Power consumption bound,

event arrival rate, dynamic range of power

Reconfiguration algorithms and Simulation Online estimation of system parameter Optimal online reconfiguration algorithm Simulation for proposed reconfiguration algorithms

* For More information pleas, visit “http://www.eng.yale.edu/enalab/aspire.htm”