Adaptive GPS Duty Cycling and Radio Ranging for Energy-efficient Localization
This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting applicationspecific positioning criteria.
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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
- 4. Virtual Fencing: Environmental protection
- 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
- Introduce mysel!