1 energy-efficient localization via personal mobility profiling ionut constandache co-authors:...
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Energy-efficient Localization Via Personal Mobility Profiling
Ionut Constandache
Co-authors: Shravan Gaonkar, Matt Sayler, Romit Roy Choudhury and Landon
Cox
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Context
Pervasive wireless connectivity+
Localization technology=
Location-based applications (LBAs) Location-based applications (LBAs)
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Context
Pervasive wireless connectivity+
Localization technology=
(iPhone AppStore: 3000 LBAs, Android: 600
LBAs)
Location-based applications (LBAs) Location-based applications (LBAs)
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Location-Based Applications (LBAs)
Two kinds of LBAs: One-time location information:Geo-tagging, location-based recommendations, etc.
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Location-Based Applications (LBAs)
Two kinds of LBAs: One-time location information:Geo-tagging, location-based recommendations, etc.
Localization over long periods of time:GeoLife: shopping list when near a grocery storeTrafficSense: real-time traffic conditions
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Localization Technology
LBAs rely on localization technology to get user position
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Localization Technology
LBAs rely on localization technology to get user position
Accuracy Technology
10m GPS 20-40m
WiFi 200-400m GSM
Accuracy Technology
10m GPS 20-40m
WiFi 200-400m GSM
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Localization Technology
LBAs rely on localization technology to get user position
LBAs executed on mobile phones
Accuracy Technology
10m GPS 20-40m
WiFi 200-400m GSM
Accuracy Technology
10m GPS 20-40m
WiFi 200-400m GSM
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Localization Technology
LBAs rely on localization technology to get user position
LBAs executed on mobile phones
Accuracy Technology
10m GPS 20-40m
WiFi 200-400m GSM
Accuracy Technology
10m GPS 20-40m
WiFi 200-400m GSM
Energy Efficiency is importantEnergy Efficiency is important (localization for long time)
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Localization Technology
Ideally Accurate and Energy-Efficient Localization
Ideally Accurate and Energy-Efficient Localization
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Energy
… sample every 30s
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
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Energy
… sample every 30s
Battery shared with Talk time, web browsing, photos, SMS, etc.
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
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Energy
… sample every 30s
Battery shared with Talk time, web browsing, photos, SMS, etc.
Localization energy budget only percentage of battery 20% of battery = 2h GPS or 8h WiFi
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
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Energy
… sample every 30s
Battery shared with Talk time, web browsing, photos, SMS, etc.
Localization energy budget only percentage of battery 20% of battery = 2h GPS or 8h WiFi
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h
For limited energy budget what accuracy to expect?For limited energy budget what accuracy to expect?
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L(t0) L(t1) L(t2) L(t3) L(t4)
L(t6)L(t7)
L(t5)
Problem Formulation
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L(t0) L(t1) L(t2) L(t3) L(t4)
L(t6)L(t7)
L(t5)
LocalizationError
t0 t1 t2 t3 t4 t5 t6 t7 Time
Problem Formulation
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L(t0) L(t1) L(t2) L(t3) L(t4)
L(t6)L(t7)
L(t5)
LocalizationError
t0 t1 t2 t3 t4 t5 t6 t7 Time
GPS
Problem Formulation
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L(t0) L(t1) L(t2) L(t3) L(t4)
L(t6)L(t7)
L(t5)
LocalizationError
t0 t1 t2 t3 t4 t5 t6 t7 Time
GPS
Problem Formulation
Accuracy gain from GPSEng.: 1 GPS read
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L(t0) L(t1) L(t2) L(t3) L(t4)
L(t6)L(t7)
L(t5)
LocalizationError
t0 t1 t2 t3 t4 t5 t6 t7 Time
GPS
Accuracy gain from GPSEng.: 1 GPS read
Problem Formulation
Accuracy gain from WiFiEng.: 1 WiFi read
WiFi
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L(t0) L(t1) L(t2) L(t3) L(t4)
L(t6)L(t7)
L(t5)
LocalizationError
t0 t1 t2 t3 t4 t5 t6 t7 Time
GPS
Accuracy gain from GPSEng.: 1 GPS read
Problem Formulation
Accuracy gain from WiFiEng.: 1 WiFi read
WiFi
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Given energy budget B, known Trace T, location readings costs egps , ewifi , egsm :
Schedule location readings to minimize Average Localization Error (ALE)
Given energy budget B, known Trace T, location readings costs egps , ewifi , egsm :
Schedule location readings to minimize Average Localization Error (ALE)
Problem Formulation
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Given energy budget B, known Trace T, location readings costs egps , ewifi , egsm :
Schedule location readings to minimize Average Localization Error (ALE)
Given energy budget B, known Trace T, location readings costs egps , ewifi , egsm :
Schedule location readings to minimize Average Localization Error (ALE)
Problem Formulation
ALE = Avg. dist. between reported and actual location of the user
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Given energy budget B, known Trace T, location readings costs egps , ewifi , egsm :
Schedule location readings to minimize Average Localization Error (ALE)
Given energy budget B, known Trace T, location readings costs egps , ewifi , egsm :
Schedule location readings to minimize Average Localization Error (ALE)
Problem Formulation
ALE = Avg. dist. between reported and actual location of the user
Find the Offline Optimal AccuracyFind the Offline Optimal Accuracy
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Results
B = 25% BatteryOpt. GPS/WiFi/GSM
Trace 1 78.5m
Trace 2 58.6m
Trace 3 62.1m
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B = 25% BatteryOpt. GPS/WiFi/GSM
Trace 1 78.5m
Trace 2 58.6m
Trace 3 62.1m
Offline Optimal ALE > 60mOffline Optimal ALE > 60m
Results
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Offline Optimal ALE > 60mOffline Optimal ALE > 60m
Results
Online Schemes Naturally WorseOnline Schemes Naturally Worse
B = 25% BatteryOpt. GPS/WiFi/GSM
Trace 1 78.5m
Trace 2 58.6m
Trace 3 62.1m
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Our Approach: EnLoc
Reporting last sampled location increases inaccuracy
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Our Approach: EnLoc
Reporting last sampled location increases inaccuracy
Prediction opportunities exist Exploit habitual paths Leverage population statistics when the user has deviated
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Our Approach: EnLoc
Reporting last sampled location increases inaccuracy
Prediction opportunities exist Exploit habitual paths Leverage population statistics when the user has deviated
EnLoc Solution: Predict user location when not sampling Sample when prediction is unreliable
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EnLoc: Overview
Deviations
EnLoc
Habitual Paths
E.g. Regular path to office E.g. Going to a vacation
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EnLoc: Overview
Deviations
EnLoc
Habitual Paths
E.g. Regular path to office
Per-user Mobility ProfilePer-user Mobility Profile
E.g. Going to a vacation
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EnLoc: Overview
Deviations
EnLoc
Habitual Paths
E.g. Regular path to office E.g. Going to a vacation
Per-user Mobility ProfilePer-user Mobility Profile Population StatisticsPopulation Statistics
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Profiling Habitual Mobility
Intuition: Humans have habitual activities Going to/from office Favorite grocery shop, cafeteria
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Profiling Habitual Mobility
Intuition: Humans have habitual activities Going to/from office Favorite grocery shop, cafeteria
Habitual activities translate into habitual paths E.g. path from home to office
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Profiling Habitual Mobility
Intuition: Humans have habitual activities Going to/from office Favorite grocery shop, cafeteria
Habitual activities translate into habitual paths E.g. path from home to office
Habitual paths may branch E.g., left for office, right for grocery Q: How to solve uncertainty? A: Schedule a location reading after the branching point.
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Per-User Mobility Graph
User Habitual Paths
Graph of habitual visited GPS coordinates
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Per-User Mobility Graph
User Habitual Paths Logical Representation
Graph of habitual visited GPS coordinates
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Per-User Mobility Graph
Graph of habitual visited GPS coordinates Sample location after branching points Predict between branching points # of BPs < # of location samples(BP = branching point)
User Habitual Paths Logical Representation
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Evaluation: Habitual Paths
30 days of traces, loc. battery budget 25% per day
Assume phone speed known
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Evaluation: Habitual Paths
30 days of traces, loc. battery budget 25% per day
Assume phone speed known
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Evaluation: Habitual Paths
30 days of traces, loc. battery budget 25% per day
Assume phone speed known
Average ALE 12m
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Predict based on population statistics If user on a certain street, at the next intersection
predict the most probable turn.
Deviations from habitual paths
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Predict based on population statistics If user on a certain street, at the next intersection
predict the most probable turn. Probability Maps computed from Google Map simulation
Deviations from habitual paths
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Predict based on population statistics If user on a certain street, at the next intersection
predict the most probable turn. Probability Maps computed from Google Map simulation
Deviations from habitual paths
Goodwin & Green
U-Turn Straight Right Left
E on Green 0 0.881 0.039 0.078
W on Green 0 0 0.596 0.403
N on Goodwin
0 0.640 0.359 0
S on Goodwin
0 0.513 0 0.486
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Evaluation: Population Statistics
OptX: report last sampled location using sensor X (offline)
EnLoc-Deviate: Equally spaced GPS + population statistics (online). ALE ~ 32m
OptX: report last sampled location using sensor X (offline)
EnLoc-Deviate: Equally spaced GPS + population statistics (online). ALE ~ 32m
B = 25% Battery
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Future Work/Limitations
Assumed phone speed known Infer speed using accelerometer Energy consumption of accelerometer relatively small
Deviations from habitual paths Quickly detect/switch to deviation mode
Probability Map hard to build on wider scale Statistics from transportation departments
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Conclusions
Location is not for free Phone battery cannot be invested entirely into localization
Offline optimal accuracy computed For specified energy budget Known mobility trace
However, online localization technique necessary
EnLoc exploit prediction to reduce energy Personal Mobility Profiling Population Statistics
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Questions?
Thank You!
Visit the SyNRG research group @http://synrg.ee.duke.edu/