green computing energy in location-based mobile value-added services maziar goudarzi

Green Computing

Energy in Location-Based

Mobile Value-Added Services

Maziar Goudarzi

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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