3.1 – Power Constrained Sensor Sample Selection for Improved Form Factor and Lifetime in Localized BANs

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 (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




Algorithm Outline



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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




Algorithm Outline



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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




Algorithm Outline



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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




Algorithm Outline



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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




Algorithm Outline



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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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Thank You

Questions? [email protected]

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Documents

3.1 – Power Constrained Sensor Sample Selection for Improved Form Factor and Lifetime in Localized BANs