Energy Efficient Location Sensing
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Transcript of Energy Efficient Location Sensing
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Energy Efficient Location SensingBrent HorineMarch 30, 2011
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Citation
Jeongyeup Paek, Joongheon Kim, Ramesh Govindan, “Energy-Efficient Rate-Adaptive GPS-based Positioning for Smartphones,” In Proc. Of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys), 2010, pp. 299-314.
Authors are affiliated with the Embedded Networks Laboratory, Computer Science Department, University of Southern California
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Motivation Many smartphone applications require location services GPS is very power hungry (0.37 W on Nokia N95) Not all applications require the accuracy that GPS
provides Nor do they all the most recent position In urban canyons, GPS is all that accurate anyway A system which can tradeoff position accuracy with
power consumption could improve battery life with no discernable sacrifice in application usability
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RAPS
Rate Adaptive Positioning System for smartphones
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Duty Cycle Impact of GPS
But how do you match up the duty cycle with user mobility and ensure a bounded error?
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Significant Contributions
Cheap (computationally) algorithm to infer whether and when GPS activations are necessary
Integrates many previously disclosed techniques in a unified platform
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Algorithm Description
Learn user velocities at a given location and time of day
Correlate to accelerometer readings to estimate consistency
Use this information to predict the likelihood of a position update requirement
Senses signal strength and tower ID and queries history for likelihood of successful fix info
Checks local Bluetooth radios for a opportunistic location fix
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General Experimental Results
Nokia N95 smartphones deployed on campus 3.8X better lifetime than continuous GPS 1.9X better lifetime than periodic GPS scheme
with comparable error rate
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GPS Inaccuracies in Urban Area 1 week data logger at 1 sec interval Shows ghost traces and errors
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Average and Max Errors
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GPS Error Budget
Error Source Error Budget UnitsIonospheric effects +/-5 meterOrbit shifts +/-2.5 meterSatellite clock errors +/-2 meterMultipath effects +/-1 meterTropospheric effects +/-0.5 meterRounding errors +/-1 meter
Assumes 4 satellites in view. Degrades significantly with only 3 in sight.
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GPS errors
Change in GPS fix every 180 secondsMostly measuring mobility
Self reported accuracy estimates
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Update Interval vs Delta-fix for periodic updates
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Accelerometer Activity Indicator
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Power Consumption of Accelerometer
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Accelerometer Duty Cycle Analysis
Setting duty cycle at 12.5% reduces power consumption by 8X for 0.01W
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Framework for User Mobility History
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Usefulness of Cell ID?
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Futility of using RSSI to Estimate Movement
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Experiment Matrix
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•6 phones in one bag, carried by author for almost 2 days
•No other apps running on phones
•Measure battery life
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Lifetime
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Event Timeline: BPS
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Celltower Blacklist Effectiveness
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Average GPS Interval
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Average Power Consumption
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Median delta-GPS-Fix
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Avg Position Uncertainty vs GPS Duty Cycle
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Success Ratio Relative to Periodic GPS vs Duty Cycle
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Power Consumption of WPS WPS – WiFi
Positioning Service
Is RAPS compatible?
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Assisted-GPS Analysis
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Errors vs Phone Comparison
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Reflections
Many of these researchers fail to take advantage of the significant literature on GPS inaccuracies and instead take new measurements
They also consider Google Maps to be ground truth, instead of using survey markers
In my professional work with GPS, I routinely smoothed the data with a moving average filter over e.g. 10 samples. I don’t know what is done in the OS, but this is generally required when dealing directly with the e.g. GPRMC sentences
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More Reflections
A statistical approach to design of the experiment (a.k.a. Box & Hunter or Taguichi) would have produced much more confidence in the empirically derived conclusions for the same effort
Industry versus Academic (most of their conclusions are well known to an engineer working in the GPS field)
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Questions & Comments