3.1 – power constrained sensor sample selection for improved form factor and lifetime in localized...
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Tuesday, October 23, 2012 Technical Session #3: System Optimization for Wireless Health Vishwa Goudar (University of California, Los Angeles, US), Miodrag Potkonjak (University of California at Los Angeles, US)TRANSCRIPT
Power Constrained Sensor Sample Selection for Improved Form Factor and Lifetime in Localized
BANs
Vishwa Goudar and Miodrag PotkonjakComputer Science Department
University of California, Los Angeles
Outline
Body Area Networks and Applications
Challenges from Power Consumption Problem Formulation
Algorithmic Motivations
Algorithm Outline
Experimental Verification and Results
Outstanding Challenges
Q&A
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Body Area Networks and Applications
Body Area Networks (BANs) are a new subclass of Wireless
Sensor Networks (WSNs) Multi-Sensor Systems measure distinct behavioral aspects
Significant spatio-temporal resolution in measured human activity
Wireless systems support mobility
Applications in Medicine, Sports, Military and Security
BANs and hybrid systems have been applied to a host of
medical applications Gait Analysis, Geriatric Assistance, Stress Inference, Emotional
Health Monitoring, etc.3
Body Area Networks and Applications A system measuring foot plantar pressure can
Monitor risk of recurrent falls in elderly adults (GARS-M)
Manage/prevent development of plantar ulcers in diabetic patients
Forewarn of repetitive stress injuries in runners
HERMES is a smart shoe that extends instability analysis outside the lab Measures foot plantar pressure via array of 99 passive resistive sensors
Sensors are placed according to pedar® mapping of foot sole
Each sensor is sampled at 60Hz
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HERMES Shoepedar® mapping with
maximum amplitude metric
Outline
Body Area Networks and Applications
Challenges from Power Consumption Problem Formulation
Algorithmic Motivations
Algorithm Outline
Experimental Verification and Results
Outstanding Challenges
Q&A
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Challenges from Power Consumption
BANs admit a tradeoff between reliability and usability As a sensor system, the metrics they consolidate over space and
time must be accurate• Must collect as much data as possible
As a wearable system, they must be lightweight, non-intrusive and able to go for extended periods between battery recharges • Must be as power efficient as possible
Competing requirements for power usage
Problem formulation: Maximize sensing accuracy under
power usage constraints Limit number of samples taken at a time to k
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Challenges from Power Consumption
Energy optimization solutions exist with respect to
communication, sensing coverage, energy harvesting, etc.
Traditionally reliability-usability tradeoff tackled as a
sensor coverage problem We pose it as a sample coverage problem
Recently diagnostic-based coverage was proposed System needs to collect enough data to reconstruct relevant
aggregate metrics only
Rather than collect or reconstruct the detailed spatio-temporal structure of the sensed domain
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Outline
Body Area Networks and Applications
Challenges from Power Consumption Problem Formulation
Algorithmic Motivations
Algorithm Outline
Experimental Verification and Results
Outstanding Challenges
Q&A
8
Algorithmic Motivation To maintain high accuracy with less power, only sample for medical
metrics that are contextually relevant We will focus on maximum amplitude, guardedness and lateral difference
Construct repeatable sampling scheme with fixed number of epochs Human strides are periodic at 1-2Hz while walking
Super-sampled at 60Hz at each sensor; Sampling time bins are called epochs
Take advantage of spatio-temporal redundancy in localized multi-
sensor array Correlation between sensors vary in space, but also with time
Simple linear regression models to reconstruct multiple samples from a single sample when mutual info. is high
Improve coverage by taking time under consideration9
Algorithmic Motivation Dir. 1: For contextually relevant
information of an aggregate
metric: Sample sensors that exhibit most
entropy for the metric
Sample at epochs when a sensor is most likely to observe metric
Dir. 2: Infer samples via spatio-
temporal model Infer sample between 2 distinct
sensors at the same epoch if their mutual info is high
Time Shifting: Infer sample between 2 sensors at different epochs if their mutual info is high
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Algorithmic Motivation
Dir. 3: Cover a sensor
over majority of epochs
when it is likely to
observe the metric
Dir. 4: Forfeit concurrent
coverage of sensors
whose metrics are
strongly correlated
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Outline
Body Area Networks and Applications
Challenges from Power Consumption Problem Formulation
Algorithmic Motivations
Algorithm Outline
Experimental Verification and Results
Outstanding Challenges
Q&A
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Sample Selection Algorithm Offline learning algorithm
Requires data to train• From all sensors, sampling at all epochs, over a few strides
Generates sampling schedule for a given maximum allowable samples per epoch, k (Power usage constraint)
Pre-process data to generate Sensor entropies, entropy
Spatio-temporal model, senPred
Sensor metric distributions, distr
Algorithm composed of 2 routines PCSS greedily selects most valuable samples into schedule
CIIR algorithm prunes schedule and blacklists sensors to improve coverage and reduce redundancy
Iterative improvement algorithm with CIIR calling PCSS 13
Sample Selection Algorithm (cont.)POWER CONSTRAINED SAMPLE SELECTION (PCSS)
1: Input: senPred; distr; entropy; k2: Output: Sampling and Prediction Schedule3: Do until no more coverage is possible4: Compute heuristic for sampling each sensor at each free epoch upto k, based on non-covered samples5: Schedule sensor sample with best heuristic value and track the readings it will cover (and infer)6. End do7. Return sampling and inference schedules
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Heuristic ascertains value of a sample based on Aggregate merit of samples that will be inferred from it (Dir. 2)
Merit of inferred sample based on• Likelihood that it will observe metric (Dir. 1)• Entropy of corresponding sensor (Dir. 1)• Coverage received thus far by corresponding sensor (Dir. 3)
Multi-metric/modal sampling achieved by weighted round robin sample
selection
Sample Selection Algorithm (cont.)Coverage Improving Iterative Refinement (CIIR)
1: Input: senPred; distr; entropy; k2: Output: Sampling and Prediction Schedule3: for k’ in 1:k do4: senPred’=senPred5: while cov spans the range [mincov, maxcov] in small increments do6: Run PCSS with input senPred’, distr, entropy and k’ and store the returned schedules temporarily7: Remove samples from schedules corresponding to predicted sensors whose coverage is less than cov8: Restrict senPred’ to only cover remaining sensors9: end while10: Copy temporary schedules to permanent ones11: Restrict senPred to only cover sensors that are not covered or equivalent to those that are covered12: end for13: Return sampling and inference schedules
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To reduce error, CIIR ensures selected sensors are well covered (Dir. 3) Iteratively eliminates sensors that have failed to meet coverage threshold
Iteratively forfeits sensors from coverage that correlate well with already
covered sensors (Dir. 4)
Outline
Body Area Networks and Applications
Challenges from Power Consumption Problem Formulation
Algorithmic Motivations
Algorithm Outline
Experimental Verification and Results
Outstanding Challenges
Q&A
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Experimental Verification
Verified algorithm performance on 10 plantar pressure
datasets One per foot of 5 subjects with distinct gaits profiles
• Different shoe sizes, gender, weights, foot arches
Distinct schedule generated per dataset
Ran cross-validation tests on generated schedules 80% data for training, 20% data for testing
Compared performance to sensor coverage-based CICA
algorithm
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1
Wendt, J.B. and Potkonjak, M. “Medical diagnostic-based sensor selection,” IEEE Sensors, pp.1507-1510, Oct. 2011.
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Results Cover up to 3 times more sensors with entropies ranging between 59%
and 96% of maximum
value for selected spatio-temporal models is above 0.9 for 7 of 10
datasets
Yet offer energy savings between 70% and 178%
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Results Comparison of accuracy under similar power constraints
10% to 40% improvement in Root Mean Squared Error across 3 metrics
Causes for Improvements We sample different sensors at different epoch, CICA samples same sensors at all
epochs
We infer metric at multiple sensors from single sample, CICA does not perform inference
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Outline
Body Area Networks and Applications
Challenges from Power Consumption Problem Formulation
Algorithmic Motivations
Algorithm Outline
Experimental Verification and Results
Outstanding Challenges
Q&A
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Outstanding Challenges
Inference and sparse temporal coverage of a sensor lead
to compounded errors Further exploration of inference models and improved temporal
coverage is necessary
Static vs. dynamic schedules for variable behaviors
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