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

Click to edit Present’s Name

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

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

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

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

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

Exp #2 [iThings2015] Step counting

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

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?

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

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