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Green Computing
Energy in Location-Based
Mobile Value-Added Services
Maziar Goudarzi
2
Outline
• Mobile Value-Added Services• Location-Based Services• Significance of energy • Ways to improve energy in LBS
Mikkel Baun Kjærgaard, Minimizing the Power Consumption of Location-Based Services on Mobile Phones, IEEE Pervasive Computing, Vol. 11, no. 1, 2012.
Mobile Value-Added Services
• Mobile standard service– Mobile voice communication– Often called core service
• Value-Added service– Services available at little or no cost,
to promote the primary business (Wikipedia)
Two 1991 GSM mobile phones with several AC adapters
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Mobile VAS Examples
• Early years– VAS included SMS, MMS, data services
• These days– Basic SMS, MMS, data service capabilities have more and
more become core services– VAS services use above basic capabilities– Examples:
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VAS Categories
• Education• Healthcare• Banking• Payment• News/Information• Marketing• Entertainment• Basically, every app.
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LBS: Location-Based VAS Services
• Identify user location• Provide service based on
location• Examples:
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Energy in LBS VAS Services
• Heavy use of power-consuming features– GPS for positioning– LCD to display map– Radio to send/receive data
• Actual significance depends on– Usage pattern (length/intensity of usage)– Battery recharge options– What phone features are used
LBS Battery Impact• Low
– Geotagging: tag pics with location info
– Reactive LB search: nearest subway station
• Medium– LB games: geocaching (treasure
hunting), Live pac man– Sports tracker: log exercises time
and place
• High– Place & Activity recognition:
record daily activity– Proactive LB search: notify user
of nearby free city bikes– LB social networking: notify user
when near friends power consumption multiplicity factors compared to a 0.05 watt stand-by consumption
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Power Profiling a Mobile Phone
• Phone specs– Caveat: values missing
(e.g. CPU), dynamic aspects
• Measurement– Nokia Power Profiler– Caveat: depends slightly
on battery state
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Dynamic Aspects of Power
profile of a phone running a Python script that every 60 seconds invokes the GPS to produce a single position fix, opens a TCP connection to a server over the 3G radio, sends the position fix and then closes the connection.
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Power Behavior of an LBS Game
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Power Behavior of an LBS Sports App
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Power Behavior of two Map Apps
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Power Behavior of a Proactive Search App.
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Lessons Learned
• LBS consumes lots of power• Especially important in long-running apps
– E.g. Proactive search• Turn off GPS as much as possible• Minimize amount of data transmission
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Minimizing LBS Power Consumption
• Relax required positioning accuracy– In map: based on zoom level
• Street-view, suburb, city-wide
– In LB social networking, or proactive search• Decide based on relative distance• Km vs. m
– Adjust service quality based on battery left• Sports tracker on a faraway field
– Privacy restrictions
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Methods to Reduce Power
1. Minimize needed position fixes
2. Use the least consuming feature for positioning
3. Do on-phone data caching and processing
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1. Minimize position fixes
• Estimate positioning error • Do actual positioning only if error exceeds limit
• Reported implementation (EnTracked) tracks pedestrians – 62.3% power reduction, accuracy limit of 100m– 69.7% power reduction, accuracy limit of 200m– Compared to periodic position reporting
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1. Minimize position fixes
• Server-side – e.g. LB social networking– Reduce number of position requests by server– Report:
• 86% reduction in position requests, accuracy limit of 100m, queried 10 times/sec by different services
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2. Use Least Consuming Method
• Estimate position every 30s– GPS : 0.32W, 10m accuracy– WiFi: 0.094W, 40m accuracy– GSM: 0.064W, 400m accuracy
• Technique– Detect motion by accelerometer– Switch ON GPS only when moved– 85.7% saving compared to periodic reporting
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2. Use Least Consuming Method
• EnLoc system– Switch between GSM, GPS, WiFi– Mobility profiling to minimize needed position fixes
• Guess the possible paths
• LBS where only general area (zone) matters– If within a GSM cell, fully contained in the area,
• Only do positioning if changing the cell
– Up to 80% power reduction
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3. On-Phone Caching/Computing
• Nokia Map vs. Google Map– Need to consult location databases– Cache the database as much as possible
• LBS with computation need on server-side– If server wants only the route taken– Do computation locally on phone as much as
possible
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Design Considerations
• Increase in complexity• Solve the right problem• Real power effect of LBS
• LBS is an active research area!– Indoor positioning– Use other sensors available on modern phones