![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
|
Philipp
Sommer
22
|
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](https://image.slidesharecdn.com/ipsnpres13-160518011801/75/camazotz-multimodal-activitybased-gps-sampling-22-2048.jpg?cb=1668178982)
Camazotz: Multimodal Activity-based GPS Sampling
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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.
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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 2 |
- 3. Day,me: Roos,ng Camps Camazotz: MulUmodal AcUvity-‐based GPS Sampling | Philipp Sommer 3 |
- 4. NighNme: Feeding Sessions Camazotz: MulUmodal AcUvity-‐based GPS Sampling | Philipp Sommer 4 |
- 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 5 |
- 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 6 |
- 7. Delay-‐Tolerant Networking Camazotz: MulUmodal AcUvity-‐based GPS Sampling | Philipp Sommer 7 | • 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 | Philipp Sommer 8 |
- 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 9 |
- 10. Hardware Camazotz PlaTorm Camazotz: Bat god in Mayan mythology
- 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 11 | 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 12 | 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 13 | 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 14 | • 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 | Philipp Sommer 15 | 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 16 |
- 17. Sensor-‐triggered GPS Sampling Camazotz: MulUmodal AcUvity-‐based GPS Sampling | Philipp Sommer 17 | 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 18 |
- 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 19 | 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 | Philipp Sommer 20 | 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 21 | 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 | Philipp Sommer 22 | 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 23 |
- 24. Wireless Channel Characteris,cs • Whip antenna outperforms chip antennas • No dependency on speed Camazotz: MulUmodal AcUvity-‐based GPS Sampling | Philipp Sommer 24 | 100 80 60 40 20 0 -40 -50 -60 -70 -80 -90 -100 PacketLoss(%) RSSI(dBm) 0 10 20 30 40 50 60 70 80 Range (m) ChipS Loss ChipL Loss Whip Loss ChipS RSSI ChipL RSSI Whip RSSI -50 -55 -60 -65 -70 -75 -80 -85 -90 RSSI(dBm) Range (m) RSSI (0 ms-1 ) RSSI (2 ms-1 ) RSSI (4 ms-1 ) 0 10 20 30 40 50 60
- 25. Outlook CollaboraUve LocalisaUon • Use short-‐range radio to detect nearby animals • Share GPS locaUon amongst neighbors to save energy Upcoming Deployments with non-‐capUve Flying Foxes • Stage 1: 30 nodes (April 2013) • Stage 2: 150 nodes (May 2013) • Stage 3: 1000 nodes (late 2013) Camazotz: MulUmodal AcUvity-‐based GPS Sampling | Philipp Sommer 25 |
- 26. Philipp Sommer Postdoctoral Fellow t +61 7 3327 4076 e philipp.sommer@csiro.au w www.csiro.au AUTONOMOUS SYSTEMS LABORATORY | ICT CENTRE Thank you
- 27. Al,tude using rela,ve air pressure ConUnental Scale Flying Fox Monitoring| Raja Jurdak 27 | Time (min) 0 1 2 3 4 5 Heightabovebasenode(m) 0 50 100 150 200 GPS verUcal accuracy is +/-‐22m (datasheets)