Green Computing Energy in Location-Based Mobile Value-Added Services Maziar Goudarzi

Outline Mobile Value-Added Services Location-Based Services Significance of energy Ways to improve energy in LBS 2 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

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: 4

VAS Categories Education Healthcare Banking Payment News/Information Marketing Entertainment Basically, every app. 5

LBS: Location-Based VAS Services 6 Identify user location Provide service based on location Examples:

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 7

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

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 9

Dynamic Aspects of Power 10 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.

Power Behavior of an LBS Game 11

Power Behavior of an LBS Sports App 12

Power Behavior of two Map Apps 13

Power Behavior of a Proactive Search App. 14

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 15

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 16

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 17

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 18

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 19

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 20

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 21

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 22

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 23