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Camazotz: Multimodal Activity-based GPS Sampling

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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.

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. 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. 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. 3. Day,me:  Roos,ng  Camps   Camazotz:  MulUmodal  AcUvity-­‐based  GPS  Sampling    |    Philipp  Sommer  3    |  
  4. 4. NighNme:  Feeding  Sessions   Camazotz:  MulUmodal  AcUvity-­‐based  GPS  Sampling    |    Philipp  Sommer  4    |  
  5. 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. 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. 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. 8. Known  Camps  in  North  Queensland   Camazotz:  MulUmodal  AcUvity-­‐based  GPS  Sampling    |    Philipp  Sommer  8    |  
  9. 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. 10. Hardware   Camazotz  PlaTorm     Camazotz:  Bat  god  in  Mayan  mythology  
  11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 23. Radio  Communica,on  under  3D-­‐Mobility   •  UAV-­‐based  experiments  to  evaluate  radio  performance   Camazotz:  MulUmodal  AcUvity-­‐based  GPS  Sampling    |    Philipp  Sommer  23    |  
  24. 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. 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. 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. 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)  

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