making continuous mobile sensing more energy …...1 making continuous mobile sensing more...

The Opportunity

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Smartphones: A Beehive of sensing activity

*Reproduced from: “Sensing Your World”, Mike Thompson, blogs.synopsys.com

Increasing penetration ofsensors in mobile devices/tablets.

• Accelerometers• Compass• Gyroscope• Barometer

Projected Penetration of Inertial Sensors, reproduced from Yole Developpement Report, 2011

The Applications of Sensing

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Location-based ServicesIndoor Location

Wellness & Lifestyle

PureRunner BeWell

Social Networking & Interaction

WalkBase

Color Postural Recognition (ETH)

ShopKick

The Problem

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The Energy Overheads:• Activating/Sampling the Sensor• Processing the Sensor Data Stream•Transmitting the Results to the “Cloud”

Power Consumption Observed on a Test Samsung Galaxy S3

• OK for ‘intermittent’ sensing.• Need research to address“continuous sensing”!

20-30% increase in overhead of motion-

related activity recognition

The Different Steps in “Making Sense”

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Sensing

Feature Extraction

Classification

Context Deduction

High-Level Query

Mean, Fourier Coefficients, Entropy

Stand, Walk

Queuing, Exercising

Is Archan ‘queuing’ alone or with friends?

Accel(x,y,z)

The Research Threads

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Make the Sensing Process More Adaptive andEfficient

Make the Querying Logic Smarter on anIndividual Phone

Make the Querying Logic Smarter Across ManyPhones

A3R: Adaptive Sensing & Feature Extraction

(accelerometer)

ACQUA : query optimization

Cloud Query Coordination Service

High-Level Query

Research #1: A3R

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A3R: Adaptive Accelerometer-based Activity Recognition

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Key Idea: Adjust accelerometer “parameters” based on the current activity of the individual.

Two parameters:• Accelerometer sampling frequency (SF)• Classification Features (CF)

Goal: reduce energy overhead of activity recognition without sacrificing accuracy

Energy Overhead Variation

• Energy overhead increases with SF.• Non-linear increase when frequency-domain features (CF)

are selected along with time-domain features.

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Graph of Energy Consumptionon Samsung S2

Classification Accuracy• Different combination of Activites (Aggregated)

– <sampling frequency, feature choice>

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• Activity-specific– separate study for each activity

A3R Algorithm for Continuous Activity Recognition

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Activity Unknown<Fmax, T+F>

Stand<16,T>

Sit-relax<5,T>

Slow Walk

<16,T>

Escalator down

<100,T+F>

Ave_Conf> ∆

Ave_Conf< ∆

Ave_Conf> ∆

Ave_Conf> ∆

Ave_Conf> ∆

Ave_Conf< ∆

Ave_Conf< ∆

Ave_Conf< ∆

• The initial (SF, CF) default: highest sampling freq (SF) & richest feature set (CF)

• Classifier confidence: conf_vector = [p1, p2, …, pn]Average the confidence over a window & find the max

If ave-conf < Δconf

– use maximum (SF,CF)else

– use smart (SF,CF)

A3R: Insight into Activity Behavior of Real Users

user1 user2 user3 user4 user5 user60

2

4

6

8

10

12

14

16

x 104

coun

t of a

ctiv

ities

sit sitRelax normalWalk slowWalk stand stairs

Nokia N95 data: 6 users, 2-4 weeks each Non-adaptive vs. A3R

Full100, Full50, Full16: both {time + freq} features A3R: adaptive feature & sampling frequency

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Evaluation II: In-Situ Study for Android Users

Continuous study on two android phones User 1: Samsung Galaxy II User 2: HTC Nexus I

Battery Remaining Each day (3 cases) A3R: adaptive feature & sampling frequency Non-adaptive: {50}Hz + {time+freq} features No activity recognition

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Research #2: ACQUA:

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ACQUA= Acquisition-Cost Aware Continuous Query Adaptation

ACQUA: Historical Scenario

SPO2

ECG

HR

Temp.

Acc.

...

IF Avg(Window(HR)) > 100AND Avg(Window(Acc)) < 2 AND AVG(Window(Tep))>80F

THEN SMS(caregiver)

Body-worn health & wellness sensors

Phone runs a complex event processing (CEP)

engine with rules for alerts

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Can we save ‘energy’ by avoiding the need to download all data from all sensors, all the time?

ACQUA: Dynamically Changing Order of Retrieving Sensor Data

Predicate Avg(HR,5)>100

Max(SpO2,10)<90

Acquisition 5 * .02 = 0.1 nJ 10 * .008 = 0.08 nJPr(false) 0.95 0.8

if Avg(Heart-Rate, 5)>100 AND Max(Sp02,10)<90 then ALERT (STRESS).

Acq./Pr(f) 0.1/0.95 0.08/0.8

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Query Sp02 first or HR first?• Different events are less “likely” (at present)• Different sensors need different amount of

energy to transfer data

#1: Evaluate predicates with lowest energy consumption first:

{Sp02, HR}

#2: Evaluate predicates with highest false probability first

{HR, Sp02}

#3: Evaluate predicate with lowest normalized acquisition cost first (#1/#2): {Sp02, HR}

ACQUA Architectural Components

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Asynchronous Event Engine • Maintains partial query evaluation state

Dynamic QueryEvaluation Optimizer• Determines retrieval sequence for sensor streams

Query Logic Specification Module• Subset of Stream-SQL query syntax

Cost Modeler• External specification of sensor-specific trx. Cost model• Dynamic evaluation of stream selectivity

C(.); P(.)Normalized Query Syntax

Push/Pull, Batch commands

Dynamic Sensor Control (DSC)

ACQUA Components on a

Single Phone

Example: ω(Evaluation Period)=3

• Time 5: P2,P1,P3• Time 8: acquisition cost for A becomes

cheaper, because some tuples are already in buffer P1, P2, P3

Acquisition cost depends on state of the buffer at time t

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P1

P2 P3

Dynamics of SelectivityThe sequence decision is made at EVERY

evaluation instant!

Performance Results (Simulations)Bluetooth 802.11

Ener

gyBy

tes

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Research #3: Cloud Coordinated Mobile Sensing

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Illustrating the Coordination Service

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Inform when – “At least 3 PhD students of 2012 taking ISM are co-located”, so that – “I can discuss assignment with them”

Let me know if – “when at least one of 3 persons, A, B and C, are back in office and not using their cellphone, so that – “I can discuss assignment with them”

Can we save ‘energy’ by better coordinating the queries across large number of phones?

Sensing Coordination Service

Application 1

<M1,Q1>

Application 2<M2,Q2>

Q1 Q2

Logic Flow of the Coordination Service

Optimize JOINTLY (Q1, Q2):

ACQUA on Multiple Phones

Optimization Energy Savings (compared to ‘all transfer)

Individual Initial 48%

Jointly Additional 27%

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Q1: Inform when – “A is standing and near the security gate”

Q2: Inform when – “One of A or B is standing”

Sensing Coordination Service

StandingA(Accel)StandingB(Accel)

LocationA(Wi-Fi)

Optimize individually:Q1: {locationA(Wi-Fi), StandingA(Accel)}Q2: {StandingB(Accel), StandingA(Accel) } {StandingA(Accel)

locationA(Wi-Fi)

StandingB(Accel)

Preliminary Numerical Investigation

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LiveLabs Cloud Service

5 minutes later

Lifestyle Company

If a group of 4 or more people exit from Café after sitting down for 10 minutes, send

SMS with a “Movie Discount”

10 minutes later

4 in a group sitting down at

a Café

4 in a group left after 10 mins

LiveLabs software continuously monitors (location, activity, …)

Show this notification and get 20% on all

Movies

LiveLabs: A Future Testbed for Mobile Sensing(Jointly with Prof. Rajesh Balan)

LiveLabs: a large-scale research testbed for “mobile systems innovation” let’s companies & researchers run LARGE-SCALE trials and experiments with novel context-based mobile applications and services testbeds at 3 key locations with 30,000 users

Conclusions• Energy is the most critical resource constraint in

mobile sensing.• Advances include

– Adaptive adjustment of individual sensor– Query optimization of queries on individual phone– Joint optimization of queries across multiple phones.

• Ability to predict ‘context’ is the key.– Currently, context is inferred on per-phone basis.– Future—context itself inferred ‘collectively’?– Open issue: scaling context to citizen-scale

environments.

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The End!

Thanks! Questions?

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• “Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach” by Z. Yan, V. Subbaraju, D. Chakraborty, A. Misra and K. Aberer, 16th Annual International Symposium on Wearable Computers (ISWC), June 2012.

• "Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-based Continuous Event Processing", by Archan MISRA and Lipyeow LIM, IEEE Int. Conference on Mobile DataManagement (MDM), May 2011.

• "The Case for Cloud-Enabled Mobile Sensing Services", by Sougata SEN, Archan MISRA, Rajesh Krishna BALAN, and Lipyeow LIM,, Mobile Cloud Computing Workshop (MCC'12), in conjunction with ACM SIGCOMM, August 2012.

making continuous mobile sensing more energy …...1 making continuous mobile sensing more...

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