1 energy-efficient localization via personal mobility profiling ionut constandache co-authors:...

1

Energy-efficient Localization Via Personal Mobility Profiling

Ionut Constandache

Co-authors: Shravan Gaonkar, Matt Sayler, Romit Roy Choudhury and Landon

Cox

2

Context

Pervasive wireless connectivity+

Localization technology=

Location-based applications (LBAs) Location-based applications (LBAs)

3

Context

Pervasive wireless connectivity+

Localization technology=

(iPhone AppStore: 3000 LBAs, Android: 600

LBAs)

Location-based applications (LBAs) Location-based applications (LBAs)

4

Location-Based Applications (LBAs)

Two kinds of LBAs: One-time location information:Geo-tagging, location-based recommendations, etc.

5

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

6

Localization Technology

LBAs rely on localization technology to get user position

7

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

8

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

9

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)

10

Localization Technology

Ideally Accurate and Energy-Efficient Localization

Ideally Accurate and Energy-Efficient Localization

11

Energy

… sample every 30s

Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h

Battery Lifetime: GPS ~ 10h WiFi ~ 40h GSM ~ 60h

12

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

13

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

14

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?

15

L(t0) L(t1) L(t2) L(t3) L(t4)

L(t6)L(t7)

L(t5)

Problem Formulation

16

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

17

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

18

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

19

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

20

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

21

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

22

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

23

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

24

Results

B = 25% BatteryOpt. GPS/WiFi/GSM

Trace 1 78.5m

Trace 2 58.6m

Trace 3 62.1m

25

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

26

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

27

Our Approach: EnLoc

Reporting last sampled location increases inaccuracy

28

Our Approach: EnLoc

Reporting last sampled location increases inaccuracy

Prediction opportunities exist Exploit habitual paths Leverage population statistics when the user has deviated

29

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

30

EnLoc: Overview

Deviations

EnLoc

Habitual Paths

E.g. Regular path to office E.g. Going to a vacation

31

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

32

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

33

Profiling Habitual Mobility

Intuition: Humans have habitual activities Going to/from office Favorite grocery shop, cafeteria

34

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

35

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.

36

Per-User Mobility Graph

User Habitual Paths

Graph of habitual visited GPS coordinates

37

Per-User Mobility Graph

User Habitual Paths Logical Representation

Graph of habitual visited GPS coordinates

38

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

39

Evaluation: Habitual Paths

30 days of traces, loc. battery budget 25% per day

Assume phone speed known

40

Evaluation: Habitual Paths

30 days of traces, loc. battery budget 25% per day

Assume phone speed known

41

Evaluation: Habitual Paths

30 days of traces, loc. battery budget 25% per day

Assume phone speed known

Average ALE 12m

42

Predict based on population statistics If user on a certain street, at the next intersection

predict the most probable turn.

Deviations from habitual paths

43

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

44

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

45

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

46

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

47

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

48

Questions?

Thank You!

Visit the SyNRG research group @http://synrg.ee.duke.edu/