Aruna Ravinagarajan
System Energy Efficiency Lab
Aruna Ravinagarajan
Advisor : Prof. Tajana Simunic Rosing
CSE Dept., University of California, San Diego
Structural Health Monitoring (SHM)
� SHM: Process of monitoring a structure over time and identifying damage
System Energy Efficiency Lab
damage
� A wireless sensor network (WSN)…
� Monitors a physical space or object� Environment
� Humans and animals
� Structures
� Remote Location:
Needs long lasting energy source
SHM – How is it done?SHM – How is it done?
System Energy Efficiency Lab
SHM – How is it done?SHM – How is it done?
System Energy Efficiency Lab
Stuart G Taylor, Kevin M Farinholt, Eric B Flynn, Eloi Figueiredo, David L
Mascarenas, Erik A Moro, Gyuhae Park, Michael D Todd and Charles R
Farrar, 2009. A mobile-agent-based wireless sensing network for
structural monitoring applications. Meas. Sci. Technol. 20 045201 (14pp)
PZT’s
Shimmer
SHiMmer for Structural Health Monitoring
System Energy Efficiency Lab
� Localized embedded node for detection of damage in structures
� Local analysis on SHiMmer is mandatory due to data size, 4 MB per test
� High DSP computational workload
� Max power 370mW at 400 MIPS
� Peak power consumption 1.1W when sensing/actuating
� Powered only with energy harvesting
SHiMmer for Structural Health Monitoring
System Energy Efficiency Lab
Powered only with energy harvesting
Energy Harvesting
Challenges:
• Solar energy is not uniformly distributed over time
• Daily weather and seasons change the total input energy
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The task scheduler needs to The task scheduler needs to manage energy consumption manage energy consumption and accuracy of computationand accuracy of computation
Motivation
� Problem Definition:
� WSNs powered only via energy harvesting
� Operating with severe energy constraints
� Too much data to continually transmit
� Localized processing a must
System Energy Efficiency Lab
� Key challenge:
� Minimize energy costs while maximizing accuracy of computation
Related work
� Related work:
� Energy Harvesting in WSNs
� Guarantee energy neutrality and adapt duty cycle [Kansal et al., DAC ’06]
� Task Scheduling in WSNs
� Power Management with discrete service levels [Moser et al., ISLPED ’09]
� Adapting Task Utility in externally triggered WSN [J.Steck et al., INSS ’09]
System Energy Efficiency Lab
� Adapting Task Utility in externally triggered WSN [J.Steck et al., INSS ’09]
Steady State Mode
•Periodic Lifetime Monitoring
•Identifies damage that accumulates over long period of time
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• Effective bridge maintenance costs millions of $$$ every year
• By performing steady state monitoring, reduces burden on bridge’s annual maintenance
External Request Mode
2007, Minneapolis Bridge
1967, Silver Bridge
Event
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2007, Minneapolis Bridge
http://www.engineeringcivil.com/civil-engineering-disasters-collapse-of-bridges.html
External Request Mode
Event
Extra measurements are required Extra measurements are required
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Execution Time Constraint
� Given a time limit, what is the highest level of data accuracy?
Data Accuracy Constraint
� Given a minimum data accuracy, how long will it take to execute tasks?
Extra measurements are required Extra measurements are required to verify the structure’s integrityto verify the structure’s integrity
Challenge and Contribution
To run an intensive workload, Task Scheduler needs to manage:
•Energy Consumption
•Accuracy of computation
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Contribution:
• Regression based algorithm to optimize resource utilization
• Applying DVFS techniques scaled by available energy
Active Ultrasonic SHM
• 16 PZT sensors provide 120 different sensing paths
• 120 waveform data give about 4 Mbyte
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• Filtering signal using FFTs
• Convolving filtered set of data with baseline signals
• Damage detection consists of combining data using a correlation function
• A higher number of measurements increases detection and localization accuracy
Setting up Regression Model
BlackFin integrated DVFS defines three different DSP working modes:
• Active High: Vcore =1.20V, fDSP =300 MHz, PMax =370mW
• Active Low: Vcore =0.85V, fDSP =150 MHz, PMax =220mW
• Idle: Vcore =0.85V, fDSP =75 MHz, PMax =35mW
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Setting up Regression Model
The energy and time consumption, accuracy for every task in the task graph
can be determined based on the number of paths, frequency and power
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Setting up Regression Model
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Steady State Algorithm
• Steady State activity performed every 15 min (900 sec) : Tslot
• Eavailable represents the available energy in buffer
• Based on regression model, DVFS mode is automatically selected by the scheduler in order to maximize number of path
System Energy Efficiency Lab 18
selected by the scheduler in order to maximize number of path measurements with respect to energy availability
Steady State Algorithm
Available EnergyThe available energy is calculated as:
where Eth_min is the minimum amount of
tconsumedtharvestedtbuffertbuffer
tht
buffertavailable
EEEE
EEE
−+=
−=
−1
min_
System Energy Efficiency Lab 19
where Eth_min is the minimum amount of
energy to execute an external request
Steady State Algorithm
Available Energy
Estimate Execution TimeExecution time is estimated using
relation obtained from regression :
tavailableEtT ⋅=
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where tDSP and eDSP are model
coefficients that depend on the
DVFS mode (high/low frequency)
{ }lowHighDSPDSP
DSP
tavailable
DSPtexecution
ffte
etT
,, ∈
⋅=
Steady State Algorithm
Available Energy
Estimate Execution Time
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Select DVFS modeThe DVFS mode is selected to max
Npaths according to estimated limits:
{ }lowHighDSP
availableiexecution
iexecution
DSP
texecution
DSP
ffe
EfE
fEt
Te
,
)(
)(
∈
<
=⋅
Steady State Algorithm
Available Energy
Estimate Execution Time
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Select DVFS mode
Execute tasks according to
task graph for Npaths
Transmit Result
External Request Algorithm
• External Requests (ER) set two constraints:
• Maximum Execution Time
• Minimum SHM Accuracy
Available Energy
Max Time OR Min Accuracy
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• Minimum SHM Accuracy
• DVFS mode selection performed in manner similar to steady state
Select DVFS mode
Execute tasks according to
task graph for Nrequired paths
Transmit Result
The DVFS mode is selected
according to estimated limits:
requiredthavailable
data
DSPrequired
DSP
constrain
DSPrequired
EEE
a
AccuracyeE
t
TeE
>>
⋅=
⋅=
min_
min
min
max
min
OR
Experimental set-up
Inputs to the system:
• Variable solar energy conditions to the energy harvester
• Task sequence as shown in Task Graph
• DVFS active frequency and power modes
• Periodic activity time slots: T = 900s
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• Periodic activity time slots: Tslot = 900s
• Constant number of External Requests per day: 25
• Results compared to an iterative search algorithm for task scheduling in SHM2
J. B. Steck et al. "Adapting Performance in Energy Harvesting Wireless Sensor Network for Structural Health Monitoring Applications," 6th International Conference on Networked Sensing Systems, 2009
2
Number of measurements increase
• 50% measurement increase with regression algorithm
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algorithm
• 15-20% further increase with DVFS
• 16% accuracy increase with regression algorithm
Measurement accuracy improvement
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• 27% accuracy increase with DVFS
• 95% of external request served with regression
Increase in number of served ER
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with regression algorithm
• 20% of energy saving with DVFS
Conclusions
With our task scheduler, SHiMmer for SHM analysis achieved:
� Up to 85% increase in the number of daily measurements
� Up to 27% increase in result accuracy
� Up to 95% of external request service requests processed
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The algorithm improves performance through an efficient combination of:
� The adoption of the regression algorithm, optimizing the usage of available resources
� An efficient usage of DVFS