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Adaptive GPS Duty Cycling with Radio Ranging for
Energy-Efficient Localization
Raja Jurdak
Peter Corke
Dhinesh Dharman
Guillaume Salagnac
CSIRO ICT Centre
Queensland University of Technology
INSA Lyon
Motivation
• Localization systems need
absolute position references
• GPS
• GPS is energy-expensive
• Key ideas of this work
• Duty cycle GPS
• Complement with energy-
inexpensive signals
• Radio beacons
• Accelerometers
• Magnetometers
• Domain problems:
• Herd behaviour
• Grazing patterns
• Social interaction
Cattle sensor networks
GPS + RF antennas
Virtual Fencing: Environmental protection
Design Considerations
Cows are slow!GPS lock times loosely
depend on off time
GPS Duty Cycling
1. GPS acquires lock
GPS chip
uncertainty
2. GPS powered off3. Uncertainty grows
while cow moves around Error: If real position
outside uncertainty
region at next GPS lock
Success: real position
within uncertainty
bound at next GPS lock
X
X Assumed position
Real position
Uncertainty
4. GPS turns on prior to
reaching AAU
5. Node acquires GPS
lock again
AAU
GPS Duty Cycling Strategy
Varying the AAU according to
the cow’s distance from the
fence Speed models
AAU: absolute acceptable uncertainty
Ugps: GPS chip uncertainty
s: assumed speed
tL: lock time
Static AAU
• Simulations based on 2-day empirical cow position dataset
• 30 cows, 1-second granularity for GPS positions
GPS Duty Cycling Performance
Dynamic AAU
Exploiting Radio Proximity Data
Cows naturally herd closely
together
GPS duty cycling vs GPS
DC and contact logging
Combining GPS duty cycling
with short range radio beaconing
A Visual Simulator
First
node
gets
GPS
lock
Second
node
gets
GPS
lock
Nodes send regular
beacons while growing
their uncertainty
Black lines indicate real
node displacement
A third node
enters
neighborhood
and gets lock
Beacon from new node useful
for reducing uncertainty of first
2 nodes
A fourth node
shows up
It’s out of
range of the
other 3 nodes
Contact Radius
• Static or dynamic?
Use RSSI for bounding contact
distance
Effect of contact radius on
energy and error rate
Beacon Period
• Static or dynamic?
Send radio beacons only when
local uncertainty drops
Effect of beacon scheduling on
energy and error rate
Summary of results
Event-driven with 5m contact radius provides
best balance for our application
Adaptive Duty Cycling
• Define error rate and energy targets
• Nodes keep track of their error rate and energy
• If error rate is high OR node has reserve energy, increase speed
estimate
• If error rate is low, decrease speed estimate
• User preference to break ties
User favors accuracy User favors energy
Conclusion
• Strategy for energy efficient localization
• GPS duty cycling
• Contact logging
• Use dynamic configuration
• Dynamic AAU (depending on application)
• Dynamic speed
• Event-driven beacons
• RSSI-based range bounding
• Future work
• Estimating error rates with sparse sampling
• Using inertial sensors as motion triggers
• Leveraging group and mobility models
• Exploring multi-hop contact logging
Thank you
CSIRO ICT Centre
Raja Jurdak
Principal Research Scientist
Phone: +61 (0)7 3327 4059
Email: raja.jurdak@csiro.au

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Adaptive GPS Duty Cycling and Radio Ranging for Energy-efficient Localization

  • 1. Adaptive GPS Duty Cycling with Radio Ranging for Energy-Efficient Localization Raja Jurdak Peter Corke Dhinesh Dharman Guillaume Salagnac CSIRO ICT Centre Queensland University of Technology INSA Lyon
  • 2. Motivation • Localization systems need absolute position references • GPS • GPS is energy-expensive • Key ideas of this work • Duty cycle GPS • Complement with energy- inexpensive signals • Radio beacons • Accelerometers • Magnetometers
  • 3. • Domain problems: • Herd behaviour • Grazing patterns • Social interaction Cattle sensor networks GPS + RF antennas
  • 5. Design Considerations Cows are slow!GPS lock times loosely depend on off time
  • 6. GPS Duty Cycling 1. GPS acquires lock GPS chip uncertainty 2. GPS powered off3. Uncertainty grows while cow moves around Error: If real position outside uncertainty region at next GPS lock Success: real position within uncertainty bound at next GPS lock X X Assumed position Real position Uncertainty 4. GPS turns on prior to reaching AAU 5. Node acquires GPS lock again AAU
  • 7. GPS Duty Cycling Strategy Varying the AAU according to the cow’s distance from the fence Speed models AAU: absolute acceptable uncertainty Ugps: GPS chip uncertainty s: assumed speed tL: lock time
  • 8. Static AAU • Simulations based on 2-day empirical cow position dataset • 30 cows, 1-second granularity for GPS positions GPS Duty Cycling Performance Dynamic AAU
  • 9. Exploiting Radio Proximity Data Cows naturally herd closely together GPS duty cycling vs GPS DC and contact logging Combining GPS duty cycling with short range radio beaconing
  • 10. A Visual Simulator First node gets GPS lock Second node gets GPS lock Nodes send regular beacons while growing their uncertainty Black lines indicate real node displacement A third node enters neighborhood and gets lock Beacon from new node useful for reducing uncertainty of first 2 nodes A fourth node shows up It’s out of range of the other 3 nodes
  • 11. Contact Radius • Static or dynamic? Use RSSI for bounding contact distance Effect of contact radius on energy and error rate
  • 12. Beacon Period • Static or dynamic? Send radio beacons only when local uncertainty drops Effect of beacon scheduling on energy and error rate
  • 13. Summary of results Event-driven with 5m contact radius provides best balance for our application
  • 14. Adaptive Duty Cycling • Define error rate and energy targets • Nodes keep track of their error rate and energy • If error rate is high OR node has reserve energy, increase speed estimate • If error rate is low, decrease speed estimate • User preference to break ties User favors accuracy User favors energy
  • 15. Conclusion • Strategy for energy efficient localization • GPS duty cycling • Contact logging • Use dynamic configuration • Dynamic AAU (depending on application) • Dynamic speed • Event-driven beacons • RSSI-based range bounding • Future work • Estimating error rates with sparse sampling • Using inertial sensors as motion triggers • Leveraging group and mobility models • Exploring multi-hop contact logging
  • 16. Thank you CSIRO ICT Centre Raja Jurdak Principal Research Scientist Phone: +61 (0)7 3327 4059 Email: raja.jurdak@csiro.au

Editor's Notes

  1. Introduce mysel!