Kiran K. Rachuri† Mirco Musolesiψ Cecilia

Mascolo†

† Computer Laboratory, University of CambridgeψSchool of Computer Science, University of St. Andrews

Energy-Accuracy Trade-offs in Querying Sensor Data for

Continuous Sensing Mobile Systems

1

Sense continuously

Plays a central role in many context-aware applications

High level classifiers

Energy-Accuracy Trade-offs

Continuous Sensing Mobile

Systems

2

Mobile Phone Limitations

Energy, processing, and

memory constraints

Google Nexus One, and

HDC HD2 are equipped

with1GHz processor and

512MB RAM

Energy is still a scarce

resource

3

Sensor Sampling

4

Time

Sleep Sense Sleep Sense Sleep

Event

s

Time

Sleep Sense

Event

s

Sense SleepSleep

Sensor Sampling Issues

Continuous sampling degrades battery life

Long sleep durations result in loss of sensor data

Different sensors have different requirements

Accuracy varies with sensors and classifiers

5

Sampling Interval

6

Sleep Sense Once

Sleep 0 ∞Minimum

Sampling

Interval

Maximum

Sampling

Interval

Generally

constant

for a

sensor

Design Methodology

7

Missable Event

Sleep

Not all events are important

E.g.: Microphone recording

when there is no audible sound

•Classify events as Unmissable

and Missable

•Use functions to control the sleep

interval

Back-off Function

E.g.: f(x) = 2x, where x is sleep interval

UnMissable Event

Advance Function

E.g.: f(x) = x/2, where x is sleep

interval

Sense

Back-off and Advance Functions

Type Back-off function Advance function

Linear

Quadratic

Exponential

Minimum N/A Minimum interval

Maximum Maximum

interval

N/A

k

x

2x

xe

x

8 x: sleep

interval

Dynamic Adaptation

9

Dynamically switch functions from least to most

aggressive

Missable Event Sleep

Sequence Count

Sense

Linear back-off function

Quadratic back-off

function

Exponential back-off

function

Update

Sleep

Interval

< Linear

Threshold

< Quadratic

Threshold

> Quadratic

Threshold

Evaluation – Sensor Traces

Ground truth traces - 10 users for 24 hours

Continuous sampling of Accelerometer, Bluetooth, and Microphone sensors

Events in the trace files are classified as “missable” and “unmissable” events

The classifiers are based on the EmotionSensesystem10

Event Classification

Microphone

Missable: Silence data

Unmissable: Some audible voice data

Based on silence detection technique in EmotionSense

Bluetooth

Missable: No change in co-location

Unmissable: Change in co-location

Accelerometer

Missable: Stationary

Unmissable: Moving

11

Results

Bluetooth linear threshold – Dynamic adaptation

algorithm

12

Results

Bluetooth – minimum interval variation

13

Dynamic algorithm is more accurate than

exponential_linear by a factor of 5, whereas the gain ratio

w.r.t. to Energy is only 1.5

Results

Accelerometer – minimum interval variation

14

Difference in accuracy is non-negligible whereas

difference in energy consumption is insignificant

Results

Microphone – minimum interval variation

15

linear_exponential is a better option for microphone

sensor as the energy savings are higher than the benefit

w.r.t. accuracy

Summary

Continuous sensing mobile systems

Function based sensor sampling rate control

Dynamic adaptation

Different sensors have different requirements

16

Discussion

Dynamic adaptation to unknown classifiers

Unknown sensors

Mobile phones differ in capabilities and

resources

17

18

Questions?

Thank You

Kiran Rachuri

kkr27@cam.ac.uk

EmotionSense

Demo: Mon 27 Sep, Blixen room, 12:30 to 14:00

Talk: Wed 29 Sep 13:30

http://www.cl.cam.ac.uk/research/srg/netos/emotionsense/