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Model-Based Design Exploration of Wireless Sensor Node Lifetimes Deokwoo Jung [email protected] Embedded Networks and Applications Lab (ENALAB) Yale University http://www.eng.yale.edu/enalab Deokwoo Jung, Thiago Teixeira , Andrew-Barton Sweeney and Andreas Savvides

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Model based design exploration of wireless sensor node lifetimes l.jpg

Model-Based Design Exploration of WirelessSensor Node Lifetimes

Deokwoo Jung

[email protected]

Embedded Networks and Applications Lab (ENALAB)

Yale University

http://www.eng.yale.edu/enalab

Deokwoo Jung, Thiago Teixeira , Andrew-Barton Sweeney and Andreas Savvides


How to explore the design space of wireless sensor platform l.jpg
How to explore the design space of wireless sensor platform?

Energy Constraint Sensor Platform Design

  • Battery operated wireless sensor node

  • Energy-opt. design vs Performance-opt. design

  • Choice of hardware components

    Model-Based Design Exploration

  • Two Major Design Paradigm

    • Trigger-Driven / Schedule-Driven

  • Reveal Trade-off between two Design Choice

    • Detection probability / Node lifetime

  • Matlab Pre-sensor platform design toolbox

    • Parallel lifetime comparison of new platform

    • Power budget/ breakdown analysis


Motivation related work l.jpg
Motivation & Related Work

  • Motivation

    • Mica2(2002) , XYZ(2003), Telos(2004), iMote2(2005), Hitachi(2006), ??… (20xx)

    • Standard way of analyzing node level lifetime over the platforms?

    • Talking about how a chosen combination of hardware components and operation patterns can influence node lifetime

  • Node level lifetime analysis

    • Nath et al : Markov chain-based simulation to analyze energy dissipation behavior per node.

    • Snyder et al : Power consumption simulation tool, PowerTOSSIM

    • Coleri et al: Hybrid automata models for analyzing power consumption of a node at the operating system level (TinyOS)

  • Our Work

    • Focus on the Node level & Hardware Impact in Lifetime

    • Consider many realistic factors in model such as ‘transition energy cost’

    • Derived models that show the effect of each parameter on the lifetime


Trigger vs schedule driven operation l.jpg

Trigger-Driven Strategy

Schedule-Driven Strategy

Simple Sensor

Intelligent Sensor

Processor

Processor

Polling

Interrupt

Event Monitoring

Duty Cycle

Trigger vs Schedule Driven Operation


Mathematical model l.jpg
Mathematical Model

  • In order to capture energy consumption pattern, must consider “Time” and “Power” simultaneously

    • Need “Continuous Markov Chain”

    • {Z(t):t ≥ 0} describing the state the chain is in at time t: Generalized Markov chain, or Semi-Markov process

    • It does not have the Markov property: future depends on

      • The present state, and

      • The length of time the process has spent in this state

  • Not too much complex and also practical

    • Modeling most typical pattern of node behavior

    • Reduce the number of states (Consider only practically meaningful power level)

    • Only consider 1’st order statistics of RV, mean value.


Power state description l.jpg
Power State Description

  • Preprocessor (On), Sensor (Off / On), CPU (Off /On /Idle), Radios (Off / Tx / Rx)

  • CPU On=Busy or Normal / Radio Rx= Listen or Idle

  • Off = The lowest power mode at each component

  • CP=CPU Wakeup Energy Cost, CR=Radio Wakeup Energy Cost


Power state cost for different nodes imote2 xyz mica2 telos l.jpg
Power State Cost for Different Nodes (iMote2, XYZ, Mica2, Telos)

  • Trigger Driven Platform Schedule Driven Platform


Trigger driven model l.jpg

Inter-arrival Time,X

Interrupt to CPU

Proc. Time,Y

Process Complete

Tx Time,Z

Tx Radio On

Radio Off

Trigger Driven Model

Power Profile/ Markov Chain Model (Simplified)


Trigger driven model9 l.jpg

Job completed

Job enters

Tx completed

Channel Idle

Trigger Driven Model

Power Profile Model (Detailed)

Processing

Stage

Communication

Stage


Trigger driven model10 l.jpg

Preprocessing Stage

Processing Stage

Triggered by Event

Job completed

Monitoring Event

Job enters

Tx completed

Channel Idle

Communication Stage

Trigger Driven Model

Markov Chain Model (Detailed)


Trigger driven model formula l.jpg

Minimize if Minimize if

Trigger-Driven Model Formula

Event Arrival Rate

Preprocessing Power

  • Simplified

  • Detailed

Average time spent in non-preprocessing stages per event

Average energy spent in non-preprocessing stages per event


Schedule driven model l.jpg

Communication Stage

Processing Stage

Missed Event

Detected Event

Schedule Driven Model

Power Profile Model


Schedule driven model13 l.jpg

Idle Stage

Processing Stage

Event Detected

Job completed

Monitoring Event

Job enters

Tx completed

Channel Idle

Schedule Driven Model

Markov Chain Model (Awake Period)

Communication Stage


Schedule driven model formula l.jpg

Average Power at Awake Period

Power at Asleep Period

Detection Probability = duty cycle

  • If and , Minimize Idle Power

  • If , Minimize Sleep Power

CPU Wake-up Energy Cost

Duty Period=Asleep + Awake Period

Idle State Power

Average power spent in non-preprocessing stages per event

Average energy spent in non-preprocessing stages per event

Schedule-Driven Model Formula

  • Average Power at Awake Period


Numerical validation of the models l.jpg
Numerical Validation of the Models

  • Comparison between prediction and simulation results using discrete event simulator


Trigger driven and schedule driven comparison l.jpg
Trigger-Driven and Schedule-Driven Comparison

Average Steady State Power Consumption Comparison

  • Trigger-Driven Model

  • Schedule-Driven Model


Trade off diagram l.jpg

Average Power Consumption

Trigger driven node

Schedule driven node

Average Detection

Probability

Trade-off Diagram



Does it make sense to develop a trigger driven camera node19 l.jpg

Preprocessor

Event

Sensing Motion

PIR Motion

Detector

Motion

Data

PIC

microcontroller

To

BaseStation

Wake-Up Signal

Sensing Image

Processing and Communication Unit

Imote2

Image

Data

OV7649

Camera

Centroid

Data

CC2420

Radios

PXA 27x

DMA

Turn On

Does it make sense to develop a trigger driven camera node ?


Does it make sense to develop a trigger driven camera node20 l.jpg
Does it make sense to develop a trigger driven camera node ?

  • Single target localization application



What is the expected lifetime for the existing platform versus detection probability duty cycle l.jpg
What is the expected lifetime for the existing platform versus detection probability (duty cycle)?


The best lifetime of the proposed camera imote2 given a certain arrival rate l.jpg
The best lifetime of the proposed Camera iMote2 given a certain arrival rate?

Even with the lowest power level less then 25 days of Lifetime

Less then 10 days of Lifetime with default configuration


What is the bottleneck for extending lifetime of trigger driven camera imote2 l.jpg
What is the bottleneck for extending lifetime of trigger-driven camera iMote2 ?

  • Power breakdown of Markov Chain State

Exponential decrease of arrival rate brings only linear decrease of Preprocessing Power

Due to Leakage Power of Camera Board During Preprocessing Stage


Using the models to characterize and make decisions about a camera sensor node l.jpg

iMote2 Deep Sleep trigger-driven camera iMote2 ?

94.15

2

10

Camera board Off

PXA Standby Mode

Camera board Off

PXA Standby Mode

Camera Standby

12.33

Lifetime (days)

1

10

8.45

0

10

-3

-2

-1

0

1

2

10

10

10

10

10

10

Event Inter-Arrival Rate (1/min)

Using the models to characterize andmake decisions about a camera sensor node

  • Preprocessor (PIR+PIC) is always ON &

  • Non -Preprocessing Unit (Camear+iMote2) are selectively ON

10X Improvement


Maximum power budget of preprocessor given lifetime requirement and event arrival rate l.jpg

25 trigger-driven camera iMote2 ?

l

=0

iMote2 in Deep-Sleep Mode

20

l

=0

PXA in Standby Mode

15

l

=1/10min

Lifetime (days)

PXA in Standby Mode

10

l

¥

=

PXA in Standby Mode

5

0

0

10

20

30

40

50

60

70

80

90

100

Preprocessor (mW)

Maximum Power Budget of Preprocessor given lifetime requirement and event arrival rate


Lifetime trend versus detection probability duty cycle for the sentry node described in vigilnet l.jpg
Lifetime trend versus detection probability (duty cycle) for the sentry node described in VigilNet

Reported Lifetime: 90 days

Our Prediction: 92.5 days

He, T et al. ‘Achieving long-termsurveillance in vigilnet’ ,Infocom 2006.


What if the vigilnet sentry node was trigger driven l.jpg

94.15 the sentry node described in VigilNet

What if the Vigilnet sentry node was trigger-driven?


Conclusion l.jpg
Conclusion the sentry node described in VigilNet

  • Parametric lifetime model for trigger-driven node and schedule-driven node

    • Key parameter for trigger-driven and schedule driven

    • Trade-Off Diagram between Trigger and Schedule

    • Numerical Correctness validated though simulation

  • The application of the models in making decisions about a iMote2+camera node platform

    • No significant lifetime improvement without reducing leakage power in trigger-driven architecture

  • Quick & Standard Way of Comparing Platform Lifetime

    • MATLAB toolbox for Pre-design and Platform comparison tool

  • Future work

    • Extending our models to cover more complex cases involving multiple processors and radios.


For more details visit http www eng yale edu enalab aspire htm thanks l.jpg
For more details visit the sentry node described in VigilNethttp:// www.eng.yale.edu/enalab/aspire.htm Thanks


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