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Improving Energy Efficiency of Location Sensing on Smartphones

Improving Energy Efficiency of Location Sensing on Smartphones. Samori Ball EEL 6788. Smartphone Energy Consumption. Location may be the largest energy hog in sensing applications Most smartphones have two location sensing mechanisms GPS Network Based Triangulation (Net).

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Improving Energy Efficiency of Location Sensing on Smartphones

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  1. ImprovingEnergyEfficiency of Location Sensing on Smartphones Samori Ball EEL 6788

  2. Smartphone EnergyConsumption • Location maybe the largestenergyhog in sensing applications • Most smartphones have two location sensingmechanisms • GPS • Network Based Triangulation (Net)

  3. GPS EnergyConsumption Test • Without GPS a cellphonebatterylevel drops to 94% in an hour • With GPS turned on a cellphonebatterylevel drops to 79% in an hour

  4. GPS EnergyConsumption Test

  5. Network Based Triangulation(NET) • Collects information about reachablecelltowers (or WiFiaccess points) to determine location by retrieving a location database • Uses lessenergythan GPS • Causes a cellphonebatterylevel to drop to 93% in 1 hour • Lessaccuratethan GPS

  6. GPS vs Net • GPS canachieveaccuracy up to 10m • Net achievesaccuracy of about 30m to 100m • Net canbe more accurateunder certain circumstances • Net or GPS canbeunavailable in different areas

  7. GPS vs Net

  8. GPS vs Net

  9. ReducingEnergyConsumption • Test wasdone on an android phone • For mostsensing applications energy management comes down to GPS usage management • Most sensing applications don’t manage theirenergy usage well • No applications coordinatewithother applications to manage GPS usage

  10. System Architecture

  11. ReducingEnergyConsumption • Sensing Substitution (SS) • Sensing Suppression (SR) • SensingPiggybacking (SP) • Sensing Adaptation (SA)

  12. ReducingEnergyConsumption

  13. Sensing Substitution • Android phones allow applications to registerwhich location sensingmechanismtheywant to use whentheyregistertheir locations • There is no way to swichmechanisms on the fly as conditions change

  14. Sensing Substitution

  15. Sensing Substitution • This project uses SS to serve as a middleman to do dynamicselection of mechanisms • It creates a profile of the areas a user travelsthrough and substitutes the optimal mechanismdepending on the profile

  16. Sensing Substitution

  17. Sensing Substitution • A mechanismis optimal if itmeets the accuracyrequrements and uses lessenergy • When GPS isneeded but unavailable Net canbesustituted • Whenonly Net accuracyisneeded and Net isunavailable GPS willbesubstitutedwithreduced update frequency

  18. Sensing Substitution

  19. Sensing Suppression • An application calls the location mechanismevenwhen the user stays in one place for an extendedperiod of time • SS uses the lowerpoweredaccelerometer and orientation sensors to determine the state of mobility • If the mobility state isdetermined to bestatic use of the location sensingissupressed

  20. Sensing Suppression • Supressionisdependant on application requirements • If an application has coarse location needssupressionoccurs more readily • The location mechanismiscalledperiodicallyeven in suppressed mode to validate the state • Users are allowed to manuallyadjust the state • States are determinedwith confidence levelsthat use profiled route information

  21. Sensing Suppression

  22. SensingPiggybacking • Applications don’tsyncronizetheirrequests for the location mechanism • SensingPiggybackingcoordinates the requests of multiple applications to make the least amount of calls to the location mechanisms possible

  23. SensingPiggybacking

  24. SensingPiggybacking • For example,Ifthere are two applications thatregister to use a location mechanism 1 with a 1 minute interval and anotherwith a 2 minute interval the use at 1 minute intervalsisused to satisfy the 2 minute intervalneed

  25. SensingPiggybacking

  26. SensingPiggybacking • GPS and Net requests are consideredseparately, but If there are no other Net requests, a GPS requestcanbesubstituted

  27. SensingPiggybacking

  28. Sensing Adaptation • When the batteryis running lowusersmayacceptloweraccuracy in a trade off for longer phone use time • SA adjusts the intervals of calling the GPS when the batteryislow • The user has the ability to manually input the desired application degrees

  29. Sensing Adaptation • A thresholde.g. 20% is set by the user belowwhich SA kicks in • SA adjusts the intervals of calling the GPS when the batteryislow • The user has the ability to manually input the desired application degrees

  30. Sensing Adaptation

  31. IntegratedOperation • At time T0 user is initially in motion and the battery level is high, SS begins to work • At T1, SP becomes operational • When the user becomes static, SR kicks in • When the battery level becomes low,SA comes into play • As the user starts moving again, SR stops, and SS is invoked if possible

  32. IntegratedOperation

  33. MobilityProfiling • Both SR and SS use the M-Area structure • M-Area is an area, generated by profiling, that has a particularcharacteristic of GPS and Net • Each area is a rectangle with 3 properties: • Boundary-start, end, width • Usage-number of visits, last visit time • Sensingcharistics-availability and accuracy of GPS and Net

  34. MobilityProfiling • SR and SS change states as a user moves from one M-Area to another • There is a tradeoffwith M-Area Size • Larger M-Area, higherSupressionprobability • Smaller M-Area, lessstoragespace and processing time

  35. Results • SR • Effectivelysupresses about half of GPS sensing and improvesbattery life for calls by 400s • SP • Improved call-making time by up to 650s per hour

  36. Results • SA • For everyhour of running a location sensing application about 20 minutes of phone-call time canbesaved • SS • With a 300-meteraccuracyrequirement GPS invocations reduced by 50%

  37. Conclusion • GPS usage reduced by 98% • Improvedbattery life by up to 75% • Androidplatformwaschosen for it’s open nature and popularity • How much the application canaccomplishmaydepend on OS architecture

  38. CurrentWork • Energy-Efficient Rate-Adaptive GPS-basedPositioning for Smartphones • Uses bluetooth and accelerometer to help withpositioning and minimized GPS use • Uses celltower RSS blacklisting to avoid the use of GPS whereitis not availabe

  39. Future Work • Application-awaretuning of location-sensingparameters • Indoor location-sensing (e.g. use of WiFi networks)

  40. Questions?

  41. References Zhenyun Zhuang1∗ Kyu-Han Kim2† Jatinder Pal Singh2, 1Georgia Institute of Technology, Atlanta, GA 30332, U.S.A. 2Deutsche Telekom R&D Laboratories USA, Los Altos, CA 94022, U.S.A., zhenyun@cc.gatech.edu, kyu-han.kim@telekom.com, jatinder.singh@telekom.com, Improving Energy Efficiency of Location Sensing on Smartphones Android programming tutorial [Chau Ngo] iPhone programming tutorial [Jonathan Mohlenhoff] Shane B. Eisenman, EmilianoMiluzzo, Nicholas D. Lane, Ronald A. Peterson, Gahng-SeopAhn, and Andrew T. Campbell, "BikeNet: A Mobile Sensing System for Cyclist Experience Mapping", ACM Transactions on Sensor Networks (TOSN), vol. 6, no. 1, December 2009, Bikenet

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