Long-term outdoor localisation with battery-powered de- vices remains an unsolved challenge, mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, but they only provide coarse-grained control over GPS activity. In this paper, we introduce our feature-rich lightweight Ca- mazotz platform as an enabler of Multimodal Activity-based Localisation (MAL), which detects activities of interest by combining multiple sensor streams for fine-grained control of GPS sampling times. Using the case study of long-term fly- ing fox tracking, we characterise the tracking, connectivity, energy, and activity recognition performance of our module under both static and 3-D mobile scenarios. We use Cama- zotz to collect empirical flying fox data and illustrate the utility of individual and composite sensor modalities in clas- sifying activity. We evaluate MAL for flying foxes through simulations based on retrospective empirical data. The re- sults show that multimodal activity-based localisation re- duces the power consumption over periodic GPS and single sensor-triggered GPS by up to 77% and 14% respectively, and provides a richer event type dissociation for fine-grained control of GPS sampling.
Coefficient of Thermal Expansion and their Importance.pptx
Camazotz: Multimodal Activity-based GPS Sampling
1. Camazotz:
Mul,modal
Ac,vity-‐Based
GPS
Sampling
AUTONOMOUS
SYSTEMS
LABORATORY
|
ICT
CENTRE
Raja
Jurdak
|
Philipp
Sommer
|
Branislav
Kusy
|
Navinda
Ko5ege
|
Christopher
Crossman
|
Adam
McKeown
|
David
Westco5
IPSN
2013,
Philadelphia,
PA,
USA
2. Flying
Foxes,
Megabats,
Fruitbats
Suborder:
Megachiroptera
Family:
Pteropodidae
Size:
6
–
40
cm
Wingspan:
up
to
1.7
m
Weight:
up
to
1.6
kg
Diet:
Fruits,
nectar
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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|
3. Day,me:
Roos,ng
Camps
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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|
5. Tracking
Flying
Foxes
Disease
Vector
• Hendra
Virus
• Ebola
in
Asia/Africa
Seed
Dispersal
• Bio
Security
Behaviour
• Not
well
understood
• Threatened
species
InteracUon
• With
other
flying
foxes
• With
other
animals
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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6. Con,nental-‐Scale
Tracking
of
Flying
Foxes
• Long-‐term
tracking
of
flying
foxes
• Discovery
of
new
camps
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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|
7. Delay-‐Tolerant
Networking
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
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Philipp
Sommer
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• Individuals
travel
between
different
camps
and
other
locaUons
• Store
sensor/posiUon
samples
locally
in
flash
• Upload
using
short-‐range
radio
to
gateway
(3G)
at
known
camps
Base
A
Base
B
Base
C
DB
8. Known
Camps
in
North
Queensland
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
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Sommer
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9. PlaTorm
Requirements
• Weight
limit:
30-‐50
g
(max.
5%
of
body
weight)
• Long
term
operaUon
(months-‐
years)
• Short-‐range
radio
communicaUon
in
camps
• Delay
tolerant
networking
• GPS
locaUon
every
few
hours
• Context
(inerUal,
pressure,
audio)
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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11. Camazotz:
Hardware
Architecture
• Low-‐power
architecture
• TI
CC430
System-‐on-‐Chip
(MCU
+
Radio)
• ConUki
OS
• Power
Supply
• Li-‐Ion
ba5ery
(3.8V,
300
mAh)
+
solar
panels
• Data
Storage
•
64-‐MBit
flash
chip
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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|
Texas
Instruments
CC430F5137
Ublox
Max 6
Bosch
BMP085
ST Micro
LSM303
Audio MicAtmel
AT25DF
I2C
SPI
ADC
Power
Supplies
Solar
Panels
Li-Ion
Charger
Li-Ion
Battery
Serial
Flash
Low Power
GPS
CC430F5137
System-on-
Chip:
MCU/Radio
Pressure
sensor
3-D Inertial
sensors
Microphone
12. Camazotz:
On-‐Board
Sensors
• MulUmodal
sensing
plakorm
• GPS
(u-‐blox
MAX6)
• InerUal
(accelerometer,
magnetometer)
• Pressure
and
temperature
• Microphone
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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|
Texas
Instruments
CC430F5137
Ublox
Max 6
Bosch
BMP085
ST Micro
LSM303
Audio MicAtmel
AT25DF
I2C
SPI
ADC
Power
Supplies
Solar
Panels
Li-Ion
Charger
Li-Ion
Battery
Serial
Flash
Low Power
GPS
CC430F5137
System-on-
Chip:
MCU/Radio
Pressure
sensor
3-D Inertial
sensors
Microphone
13. Energy
Profiling:
Solar
Input
• Harvested
energy
depends
on
orientaUon
of
solar
panels
• EsUmated
solar
current:
~3
mA
for
12
hours
/
day
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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12:00:00 13:00:00 14:00:00 15:00:00 16:00:00
Local Time
0
10
20
30
35
25
15
5
40
Current(mA)
Bat
Static Node
Average
Average
power
input
for
a
full
day:
5.7
mW
14. Energy
Profiling:
Output
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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• Long-‐term
operaUon
target
• Energy
neutral
operaUon:
Avg.
input
power
(5.7mW)
=
Avg.
output
power
• Schedule
sensing
tasks
according
to
the
current
energy
budget
15. GPS-‐based
Localisa,on
• Duty-‐cycling
GPS
receivers
• Coldstart
(no
previous
informaUon):
minutes
• Warmstart:
(rough
Ume+posiUon):
a
few
10
seconds
• Hotstart:
(accurate
Ume+posiUon):
a
few
seconds
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
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Sommer
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0 10 20 30 40 50 60
GPS off time (min)
0
5
10
15
20
25
30
Timetofirstfix(s)
16. GPS
Sampling
How
do
we
schedule
GPS
samples
to
capture
movement
paerns
at
minimum
energy
cost?
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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17. Sensor-‐triggered
GPS
Sampling
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
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Activity
Sensors Timing
Audio Inertial
Air
Solar
Event Event GPS Sampling
Pressure Duration Frequency Period
Flying X X hours daily high
Interacting X X seconds frequent on event
Urinating/Defecating X seconds frequent on event
Grooming X X seconds very frequent none
Resting X X X hours daily infrequent
Table 4: Key activities of flying foxes, their timing profile, and the sensors we use to detect them
el(dB)
0.8
1.0
rmance
Accuracy
Precision
0.8
1.0
• Use
one
or
more
of
the
low-‐power
on-‐board
sensors
to
detect
acUviUes
of
interest
trigger
GPS
samples
• Some
acUviUes
of
interest
• InteracUng
with
other
flying
foxes
(disease
spread,
social
dynamics)
• UrinaUng/defecaUon
(disease
spread,
seed
dispersal)
18. Understanding
Ac,vi,es
CapUve
flying
foxes:
3
hours
of
sensor
samples
and
video
footage
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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19. Sensor-‐triggered
GPS
Samples
(Accelerometer)
• Compute
average
vector
at
rest
gravity
• Compute
angle
between
current
vector
and
gravity
• Detect
sustained
angular
shios
above
90o
• 100%
accuracy
in
detecUng
11
true
events
• Video
footage
as
ground
truth
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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1.4 1.5 1.6 1.7 1.8 1.9 2
x 10
5
−2
0
2
4
Sample
Accelerationprojectionon
meanvector(G)
1.4 1.5 1.6 1.7 1.8 1.9 2
x 10
5
0
100
200
Sample
Angle−currentand
gravity(degrees)
20. Sensor-‐triggered
GPS
samples
(Audio)
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
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Sommer
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Time (s)
RelativeSoundLevel(dB)
Frequency(kHz)
Time (s)
SoundLevel(dB)
Time (s)
Normalizedfrequency
Mean
sound level
Duration of
sound event
Mean
normalized
frequency
• Frequency
peaks
at
2-‐4
kHz
• Lightweight
features
are
based
on
calculaUng
the
mean
signal
energy
and
counUng
the
number
of
zero
crossings
of
a
1024
sample
sliding
window
with
an
overlap
of
50%
• Video
footage
as
ground
truth
21. Mul,modal
Event
Dissocia,on
• When
one
sensor
is
insufficient
to
capture
event-‐
of-‐interest
• Example:
How
to
dissociate
interacUon
events
involving
a
collared
animal
from
interacUon
events
involving
nearby
animals
only?
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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0 400 800 1200
−2
0
2
Time (seconds)
Acceleration
ACC
X
ACC
Y
ACC
Z Detected Interaction Events
0 400 800 1200
0
50
100
150
Time (sec)
Angle(degrees)
Changes in mean
angular shift
Angular shift
Time (s)
400 800 12000
Meansoundlevel(dB)
0
200 600 1000
Acousticactivity
22. Mul,modal
Ac,vity-‐based
Localisa,on
Collared events Nearby events Power consumption
DetectedEvents
AveragePowerConsumption(mW)
Accelerometer MAL
collared only
MAL
nearby only
MAL
all events
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
9
8
7
6
5
4
2
0
1
3
Audio
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
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Sommer
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Localisation Approach
Animal interactions
Collared All Dissociated
Duty cycled GPS X
Accelerometer-triggered X
Audio-triggered X
Accel. AND Audio X
Accel. OR Audio X X
Table 5: MAL can detect all events and dissociate
interaction event involving collared animal or nearby
animals.
in our simulations. We compare a baseline approach of a
duty cycled GPS with a period of 20 s with triggered GPS
sampling approaches based on the accelerometer only, audio
only, or on the combination of audio and accelerometer sen-
sors. We group all detected ground truth interactions into
events that meet the 25 s to 1 min duration constraint. A
successful detection in our simulation is when the algorithm
obtains at least one GPS sample during the event.
During the given time window, the duty cycled GPS mod-
ule remains active for a total of 451 s (including lock times)
and successfully obtains GPS samples during each of the
four events of interest, yielding an overall node power con-
sumption of around 33 mW. Figure 13 summarises the re-
sults of sensor-triggered GPS sampling. The accelerometer-
triggered GPS manages to detect only two events (only the
events from the collared bat) with a cumulative GPS active
Figure 13: Performance of MAL
accelerometer- and audio-triggered GP
can be tuned to capture either interactio
of the collared animal, or nearby interactio
only. MAL can also detect and dissoci
types of interaction events with comparab
consumption to audio.
alongside GPS. The ZebraNet project [5] reports
position records for zebras every few minutes. I
make the energy problem more tractable ZebraN
include a solar panel, which assume that the pan
silient to normal animal activities. Positioning
GPS only, and the nodes propagate their infor
flooding in order to facilitate data acquisition by
sink. Dyo e al. [3] use a heterogeneous sensor net
23. Radio
Communica,on
under
3D-‐Mobility
• UAV-‐based
experiments
to
evaluate
radio
performance
Camazotz:
MulUmodal
AcUvity-‐based
GPS
Sampling
|
Philipp
Sommer
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