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MOBISYS 2011 MOBISYS 2011 The 9th International Conference on - - PowerPoint PPT Presentation

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MOBISYS 2011 MOBISYS 2011 The 9th International Conference on Mobile System, Applications, and Services Tracing a Missing Mobile Phone using Daily Observations Hyojeong Shin*, Yohan Chon, Kwanghyo Park, Hojung Cha Hyojeong Shin

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SLIDE 1

MOBISYS 2011 MOBISYS 2011

The 9th International Conference on Mobile System, Applications, and Services

Tracing a Missing Mobile Phone using Daily Observations Hyojeong Shin*, Yohan Chon, Kwanghyo Park, Hojung Cha

Hyojeong Shin (hjshin@cs.yonsei.ac.kr) Mobile Embedded System Lab. Yonsei University Hyojeong Shin (hjshin@cs.yonsei.ac.kr) Mobile Embedded System Lab. Yonsei University

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SLIDE 2

Motivation (1) Motivation (1) ( ) ( )

A mobile device is a precious one. A mobile device is a precious one. The device carries personal information The device carries personal information

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

The device carries personal information. The device carries personal information.

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SLIDE 3

Motivation (2) Motivation (2) ( ) ( )

  • A device will be unexpectedly lost . So …
  • Service Coverage is critical.

– A service should cover Indoor space.

  • GPS coverage : only 20 %

– A service should employ well-deployed infrastructure.

Place coverage [*]

– A service should consider various target devices.

  • Smartphones, feature phones, laptops, tablets at others
  • Accuracy: Indoor search requires room-level accuracy.
  • Also, the service should consider multiple-story building.

p y g

  • Energy: A mobile device has limited power.
  • Searching Time: A lost device should be found in very short time

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

  • Searching Time: A lost device should be found in very short time.

[*]

  • Y. Chon, et. al., "Autonomous Management of Personalized Location Provider for Mobile Services," IEEE T SMC-C 2011
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SLIDE 4

Motivation (3) Motivation (3) ( ) ( )

  • Our answer is employing Wi-Fi fingerprints.

SSID RSSI 1 AP1

  • 60dBm

– Well deployed in indoor space. – Well deployed in diverse mobile device.

2 AP2

  • 90dBm

3 AP3

  • 45dBm

4 AP4

  • 77dBm

– Wi-Fi fingerprints are unique and stable for space and time.

  • Problems

– Generally, an indoor floor plan is not available. – Wi-Fi fingerprint does not carry location information. g y

  • FindingMiMo

– In our daily life, a device records Wi-Fi fingerprints in daily basis. In our daily life, a device records Wi Fi fingerprints in daily basis. – When it is lost, a chaser application searches the missing device by tracing the series of Wi-Fi fingerprints.

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

g g p

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SLIDE 5

Related Work Related Work

  • Find-A-Lost-Phone Services

Limitations – Apple Mobile Me & MS Window Lives – Localization service (WPS)

: Service Coverage

  • Infrastructure-based positioning approach

– Bluetooth-tag, RFID-tag, Ubisense (UWB),

: Supervised Area

g, g, ( ), Indoor GPS

p : Installation Cost

  • Mobile-based positioning approach

– Place Learning : SensLoc, iLoc, SoundSense, SurroundSense and Jigsaw

: Training Phase is required. : Energy Cost

SurroundSense and Jigsaw – Geometric localization(Radio): Radar, PlaceLab – Geometric localization(Sensor): MEMS, Greefield

: Energy Cost

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

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SLIDE 6

FindingMiMo FindingMiMo Architecture Architecture g

Missing-Mobile Part Chaser Part

Missing‐Mobile Chaser

g Missing-Mobile Chaser

I t ll d t d i Daily basis Wi-Fi logging Background service Low energy consumption Installed on an extra device. (old smartphone, laptop, tablets…) Device tracking application. Wi Fi INS GPS enabled

LifeMap SmartSLAM

Wi-Fi, INS, GPS enabled.

SmartSLAM

User Context Monitor Place Learning Movement Tracking Pedestrian Tracking Constructing a floor plan Movement Tracking

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*Y. Chon,et.al.,"LifeMap: A Smartphone-based Context Provider for Location-based Service", IEEE Pervasive Comp.2011 **H. Shin, et. al. "SmartSLAM: Constructing an Indoor Floor Plan using Smartphone" Yonsei University, 2011

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SLIDE 7

Ambient Log Ambient Log

  • Inertial sensor is not available. (Energy issue, device diversity)
  • utdoor

indoor

  • Log: GPS(Long./Lat.), APs, Status, Accuracy, POI label, timestamp
  • utdoor

indoor

( )

A bi L (? ?)

SSID RSSI

(x,y)

Ambient Log (?,?) The log does not reveal the exact

1 AP1

  • 60dBm

2 AP2

  • 90dBm

3 AP3

  • 45dBm

4 AP4 77dB

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

location of the missing device.

4 AP4

  • 77dBm
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SLIDE 8

FindingMiMo FindingMiMo Scenario Scenario g

Cold Warm progress vicinity vicinity

  • The ambient log does not contain the location information.

– Analyzing the log, the chaser application displays “warm/cold” signs. y g g, pp p y g – The system signs out “warm” when a chaser is headed in the right direction and “cold” when he is not, while he is searching. f W /C ld h f h ld

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

– cf. Warm/Cold game, a treasure hunt game for children

[Thank you for a reviewer’s comment]

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SLIDE 9

Design Issue Design Issue g

Missing-Mobile Part Chaser Part g

  • Energy Limitation
  • No energy issue (rechargeable)

– In normal operation: Energy efficient logging – In missing:

  • Signal Processing

– How to generate the tracking i f i – In missing: NOT rechargeable – Adaptive sensing schedule information – Wi-Fi similarity (warm/cold) – Searching Progress

  • Storage Limitation

– Storage complexity Searching Progress

  • Searching a device

– How to search to the device – Massive APs are observed. – Linear-scale growth is not feasible. – Log reduction method – Warm/cold game – User Interface

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

– Log reduction method

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Missing Missing-Mobile : Energy (1) Mobile : Energy (1) g gy ( ) gy ( )

  • Adaptive Sensing Scheduling

p g g

– Moving-State monitoring (w/o sensor)

  • When radio signal becomes stable: stationary state

g y

  • When radio signal rapidly changes: moving state

– GPS : GPS :

  • Turns off GPS in stationary state

– Wi-Fi: Wi Fi:

  • Increases sample interval in stationary state

[*] Ti C [Fi di MiM ] Ti C GPS GSM Wi-Fi Coverage 4 5% 99 6% 94 5% [*] Time Coverage move stay Coverage 13% 87% [FindingMiMo] Time Coverage

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

Coverage 4.5% 99.6% 94.5% [*] LaMarca, A.a et al., “Place lab: Device positioning using radio beacons in the wild”, pervasive 2005 Coverage 13% 87%

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Missing Missing-Mobile : Energy (2) Mobile : Energy (2) g gy ( ) gy ( )

– People spent approximately 13% of a day to move.

Average movement time vs. stationary time Average energy consumption

– Adaptive Sensing: 3.7kJ (vs. Continuous sensing : 41.1 kJ [*])

  • reducing the battery’s lifetime by 14% in average.
  • The result depends on individual usage patterns.

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

[*] D. H. Kim, et. al., SensLoc: Sensing Everyday Places and Paths using Less Energy, Sensys 2010.

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Missing Missing-Mobile : Storage (1) Mobile : Storage (1) g g ( ) g ( )

  • Storage Complexity for Logging

h b l d ll d – The missing-mobile periodically scan GPS and Wi-Fi. – The log tends to grow in linear scale. – When a user visit known place, the log becomes useless.

  • c.f. LifeMap provides user’s POI (Point of Interest) and GPS

– The redundant log is flushed. – Log: GPS(Long./Lat.), Status, Accuracy, POI label, timestamp, APs

Ambient Log Redundant Log

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Missing Missing-Mobile : Storage (2) Mobile : Storage (2) g g ( ) g ( )

When a user visit a known place, the log is flushed

Mbytes)

4 5

the log is flushed.

d Storage (M

1 2 3

Time (Hour)

6 8 10 12 14 16 18 20 22 24

Used

  • Storage Complexity

All scan data 4 5 MB to 22 3 MB in a day (1 500 APs) – All scan data : 4.5 MB to 22.3 MB in a day (1,500 APs) – The storage usage was around 5 MB. (empirical data)

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

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Chaser : Signal Processing Chaser : Signal Processing g g g g

Missing-mobile Missing mobile

SSID RSSI 1 AP1

  • 60dBm

2 AP2 90dB

chaser

  • Wi-Fi signal comparison
  • Si il it T i

t C ffi i t

2 AP2

  • 90dBm

3 AP3

  • 45dBm

4 AP4

  • 77dBm
  • Similarity: Tanimoto Coefficient
  • Warm/cold: the similarity of the best-match observation
  • Progress: the index of the best match observation in the log

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

  • Progress: the index of the best-match observation in the log
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SLIDE 15

Chaser : Searching a Device Chaser : Searching a Device g

G A E G D F F

larity

A G D

simil

B C C D

time(sec)

B A E

  • Chasing Strategy

C

  • Chasing Strategy

– Visit a unvisited place in vicinity – Warm : Visit a next place

Mobile Embedded System Lab. Yonsei University Mobile Embedded System Lab. Yonsei University

Wa : p – Cold: Come back to the previous place / and visit another place.

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SLIDE 16

Chaser GUI Chaser GUI

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SLIDE 17

Experiments Experiments p

  • Environment

– Android Platform (ver. 2.2) – HTC Hero, HTC Desire, Samsung Galaxy A/S, G l N O /S d P h Si i Google Nexus One/S, and Pantech Sirius – in 4 buildings

  • Missing-Mobile

– Log size – Energy consumption

  • Chaser part

p

– Hide & Seek Game – Shopping mall case

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pp g

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SLIDE 18

Hide Hide-

  • and

and-

  • Seek Game

Seek Game

  • Game design

– To remove the memory-effect – Hider : hides a device – Chaser group : searches the hidden device

  • using the ambient log.
  • Environment

– 4 multi-story buildings (w/ 6 ~ 9 floors) y g

Game 1 2 3 4 Building A B C D Square (m2) 6505 5366 3482 3646 q ( ) Moving distance (hider, m) 116 183 105 117 Number of floors 9 6 9 8 Number of observed APs 70 206 139 122

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Number of observed APs 70 206 139 122 Number of participants 6 9 9 8

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SLIDE 19

Hide Hide-

  • and

and-

  • Seek Game : Result

Seek Game : Result

20 Set1

20 meter

12 14 16 18 nce (m) Set 1 Set 2 Set 3

20 meter ce (m)

6 8 10 12 approach dista Set 4

ch distanc

2 4 300 600 900 1200 a

4 meter approac

  • Approaching distance

300 600 900 1200 chasing time (sec)

chasing time (sec)

Approaching distance

– In 4 meters distance: the chaser can see the device. – In 20 meters distance: the chaser can hear ring sound from the device.

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g

  • Failure : 3 / 32 trials
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SLIDE 20

Shopping Mall Shopping Mall pp g pp g

  • Scenario:

T t j d h i f 3 h – Testers enjoyed shopping for 3 hours – 6 possible missing points are extracted from the log. (took a break) – The tester finds a missing point with the randomly selected log g p y g

  • Environment

– COEX convention center, Seoul, Korea – One of the largest shopping mall in Korea ( 195 000 m2) – Multi-storied building ( d d ) (underground )

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SLIDE 21

Shopping Mall : Result Shopping Mall : Result pp g pp g

( min: sec ) Place SP* (min:sec) (distance(m)) FindingMiMo (increment) B

4 00 (360) 6 26 (2 26)

Beverage

4 : 00 (360) 6 : 26 (2 : 26)

Shop 1

4 : 05 (368) 6 : 14 (2 : 09)

O h On a path

4 : 23 (395) 6 : 13 (1 : 50)

Restaurant

6 : 01 (541) 8 : 20 (2 : 19)

Shop 2

5 : 48 (523) 13: 3 (7 : 15)

Rest Room

6 : 18 (567) 15: 33 (9 : 15)

  • The chaser finished each search in 9 min. in average.

h h

* SP (shortest path): time to walk to the destination via the shortest path.

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  • The pure searching time is 4 : 30 in average.
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SLIDE 22

FindingMiMo FindingMiMo Features Features g

  • Non-intrusive operation

The ambient logger totally non intrusive background service – The ambient logger: totally non-intrusive background service – Energy consumption for daily-basis logging : Insurance fee

  • Tracking a moving device

Tracking a moving device

– Tracking a moving object is possible. – A missing-mobile continuously updates current ambient log. g y g – cf. Finding missing children

  • Protecting privacy

– FindingMiMo employs the minimum sensors. – Some sensors could invade user’s privacy.

  • Microphone a wiretap Camera a candid camera
  • Microphone: a wiretap, Camera: a candid camera
  • Limitations

– Service Coverage: the solution employs Wi-Fi fingerprints.

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Service Coverage: the solution employs Wi Fi fingerprints. – cf. rural environment, basement (no GPS, no Wi-Fi)

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SLIDE 23

Conclusion Conclusion

  • Contribution

– Finding a lost personal property – Expanding the Service Coverage

  • Indoor / Diverse device / No additional infrastructure

– (missing-mobile) Non-intrusive ambient loggers – (chaser app.) Fast search, Room-level accuracy

Spinoza said, “Even if the end of the world were to come tomorrow, I will plant an apple tree.” p pp I will say, “Even if my phone were to go tomorrow, I will collect a W-Fi log ”

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I will collect a W Fi log.

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SLIDE 24

g{tÇ~ lÉâ 4 g{tÇ~ lÉâ 4

hjshin@cs yonsei ac kr hjshin@cs.yonsei.ac.kr

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