Battery-free Internet of Thingsmahbub/PDF_Publications/IOT_DSP_KL_2016.pdf · 1. [IC2015]...

Mahbub Hassan IEEE Distinguished Lecture, Malaysia, 26 May 2016

Never Stand Still Faculty of Engineering Computer Science and Engineering

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Never Stand Still Faculty of Engineering Computer Science and Engineering

Battery-free Internet of Things

Making the most of energy harvesting

Mahbub Hassan School of Computer Science and Engineering

University of New South Wales, Sydney, Australia


Welcome to Internet of Things

Welcome to a Smart World


Smart Home


Smart Industry


Smart Farm


Smart Health


Welcome to batteries?


Outline

•  Motivation for energy harvesting •  Part 1 - Energy harvesting IoTs •  Part 2 - Context detection from energy harvesting •  Conclusion and future directions


Typical Energy Harvesting IoT


Energy harvesting sources Temperature sensor using solar EH

Wireless EEG using thermoelectric EH

Vibr

atio

n R

F (T

V si

gnal

)


More EH IoT products (from enocean – industry standard)

Occupancy Sensor (solar powered) Door/Window Sensor (solar powered)

Self-powered wireless switch (pressure powered)


High power consumption for Wearable IoTs •  Continuous activity and context monitoring --- killer app •  Continuous motion sensing à high power consumption •  EH wearable IoTs more challenging


Powering wearable IoT from energy harvesting convert motion energy to electrical energy

Piezoelectric Energy Harvester

Sensors

MCU

Radio

Harvested Power


Scarcity of harvested energy

•  Humans generate a small amount of kinetic energy, but •  Accelerometer sampling is power consuming

Powering perpetual activity detection only using the harvested kinetic energy is a challenging problem


Outline

•  Motivation for energy harvesting •  Part 1 - Energy harvesting IoTs

•  Part 2 - Context detection from energy harvesting •  Conclusion and future directions


Our Idea – KEH as a sensor for context detection

KEH

walking

running

Harvested Power

•  Harvested energy is influenced by human activity, so activity should be detectable

•  Power saving potentials: unlike accelerometer, KEH does not consume power

•  KEH proxying as a motion sensor may have other benefits (simpler, smaller form factor for wearables)


Piezoelectric Energy Harvester (vibration-based)

Mide.com


KEH Data Logger


Exp #1 [IC2015] Human activity recognition – KEH Data Collection


KEH Time Series

Walking

Running

Standing

accelerometer KEH


Does KEH contain information for activity recognition In

form

atio

n G

ain

Features


Activity Recognition Accuracy – 5 activities (W, R, S, SU, SD)

Classifier Activity Recognition Accuracy (%)

Accelerometer-based

KEH-based

Hand Waist Hand Waist

K-nearest neighbour 95.01 98.70 81.13 87.01

Decision Trees 87.91 91.02 79.74 79.86

Multilayer Perceptron

88.25 96.39 78.29 85.52 Low

er th

an a

ccel

erom

eter

, bu

t not

too

bad


Exp #2 [iThings2015] Step counting

4 su

bjec

ts, s

trai

ght a

s w

ell

as tu

rnin

g pa

ths,

pea

k de

tect

ion

algo

rithm

, 570

st

eps,

96%

acc

urac

y


Exp #3 [BODYNET2015]

Calorie burning estimation •  10 subjects, 4 male, 6 female: waist mounted •  Anthropometric data: Age (26-35 years, mean = 29, s.d.

= 3.06), Weight (58-91 Kg, mean = 69.3 s.d. = 10.21), Height (154-185 cm, mean = 168.5, s.d. = 9.98)

•  Two activities: walking and running •  Linear regression model to estimate calorie burning from

energy harvesting samples and anthropometric data •  Leave-one-out cross validation (1 for testing, 9 for

training)


Calorie estimation results - running

Close to accelerometer


Exp #4 [PerCom-WIP2016]

Transport mode detection

Train Car Bus Walking

Running


Pause periods of train (pauses are removed)


2-layer classification

Peak analysis

Mean analysis

Summary of Trace Data


Results

93% 77% 88%

Bus and Car are confused, but not train


Exp #5 [WOWMOM2016]

Hotword detection

3cm

Quiet Room Conditions

Hotword: “OK Google” Non-hotwards: “Good morning”, “how are you”, “fine, thank you” 8 subjects: 4 m, 4 f 60 instances (30 hotword 30 non-hotword) per subject


Speaker orientation


Results

Flat Vertical

Speaker Independent

78% 62%

Speaker Dependent

85% 73%


Conclusion

•  Vibration energy harvesting can detect a wide range of human contexts à power saving opportunity

•  Further research is required to improve context detection accuracy and reduce system power consumption


References 1.  [IC2015] Energy-Harvesting Wearables for Activity-Aware Services," IEEE

Internet Computing, 9(5), 2015 2.  [iThings2015] Step detection from power generation pattern in energy-

harvesting wearable devices,” IEEE iThings 2015

3.  [BODYNET2015] Estimating Calorie Expenditure from Output Voltage of Piezoelectric Energy Harvester - an Experimental Feasibility Study" BODYNETS 2015

4.  [PerCom_WIP2016] Transportation Mode Detection using Kinetic Energy Harvesting Wearables, PerCom Work-in-Progress, 2016

5.  [WOWMOM2016] Feasibility and Accuracy of Hotword Detection using Vibration Energy Harvester, WOWMOM 2016 (accepted).


Questions?

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