FindingMiMo: Tracing a Missing Mobile Phone using Daily - - PowerPoint PPT Presentation

Tracing a Missing Mobile Phone using Daily Observations

FindingMiMo:

Hyojeong Shin, Yohan Chon, Kwanghyo Park and Hojung Cha Department of Computer Science Yonsei University, Seoul, Korea

Presentation by Łukasz Piątkowski for Distributed Systems Course, University of Warsaw 7 Nov 2012, Warsaw

Mobile devices

• expensive
• small
• carrying private data
• energy-dependent

If you lose your mobile device

• you need to buy another one
• somebody can get your private data
• somebody can use your phone number
• you disrupt your daily communications
• your day is wasted

Introducing FindingMiMo

• 1. Outline of the problem
• 2. Related work
• 3. Solution
• 4. Implementation
• 5. Advantages
• 6. Disadvantages

Outline of the problem

• the user has lost his mobile phone in indoor

environment with wireless LAN network

• the phone is switched on and there is an

application preinstalled on it

• the user has access to another mobile

phone

Outline of the problem

• the user has lost his mobile phone in indoor

environment with wireless LAN network

○ floor plans, GPS signals, pre-learned radio maps, and non-standard hardware are usually not available ○ the building might have multiple aboveground and underground floors

Outline of the problem

• the phone is switched on and there is an

application preinstalled on it

○ the application that monitors the device must be working in order to send data to another device ○ if the phone runs out of battery after being lost there should be a possibility to find it ○ the phone has access to the internet

Outline of the problem

• the user has access to another mobile

phone

○ the device can be easily recharged ○ the device has access to the internet ○ the user can install an application on it

Related work

• "find a lost phone" services in Windows

Mobile by Microsoft and MobileMe by Apple

• SensLoc and iLoc
• SoundSense, SurroundSense and Jigsaw
• UbiSense

Related work

• "find a lost phone" services in Windows

Mobile by Microsoft and MobileMe by Apple

○ displays the approximate location of the remote device on a map ○ sets a pass code lock for wiping out the storage remotely for security reasons ○ uses GPS, cell-tower-based estimation and Wi-Fi fingerprints ○ accurate but insufficient in indoor

Related work

• SensLoc and iLoc

○ use GSM and Wi-Fi ○ detect movement, the entrance and departure time

• f meaningful places

Related work

• SoundSense, SurroundSense and Jigsaw

○ uses Wi-Fi, accelerometers, microphones, and cameras to generate an ambient fingerprint of places

Related work

• UbiSense

○ support precise real-time position referencing, long lifetime, and customized applications ○ deploys Ultra Wide-Band (UWB) antennas in a building and receives periodically emitted signals from mobile UWB tags ○ accurate and energy efficient ○ building has to have special infrastructure

Solution

• use case
• missing mobile application
• chasing mobile application
• results

Solution

• use case

○ mobile device collects daily ambient observations and logs them ○ chaser device remotely receives the log from the lost phone ○ the chaser traces the device by determining the places where the log is taken

Solution

• missing mobile application

○ LifeMap ■ continuously monitors a user’s physical location using GPS ■ writes down ambient fingerprint from GSM and Wi-Fi ■ uses activity-based sensor selection

Solution

• missing mobile application

○ MissingMobile ■ collects information from LifeMap on the path from the last known place to an unknown location ■ transmits the stored information to the chasing device ■ causes the missing mobile to make a loud sound after manual request from the chaser device

Solution

• chasing mobile application

○ SmartSLAM ■ traces pedestrian movement in indoor environments and simultaneously constructs an indoor floor plan ■ employs an accelerometer and a magnetometer in smartphone device to estimate the user’s walking path

Solution

• chasing mobile application

○ Chaser ■ measures the Wi-Fi conditions and compares them with the observations in the log ■ shows progress bar and "warm/cold" indicator

Solution

• missing mobile application
• chasing mobile application

Solution

• use case

Solution

• results

○ typical users spend approximately 13% to 20% of their time outdoors ○ the Wi-Fi logger may reduce the battery’s lifetime by 14% in average ○ the average amount of data for a day is 4.5MB to 22.3MB but the typical storage usage is around 5MB ○ the average approach distance is 4 meters

Solution

• results

Implementation

• Tanimoto coefficient function to evaluate the similarity

between two input vectors

• GPS is activated for 30 seconds every 2 minutes outdoor
• stores Wi-Fi vectors every 5 seconds in moving state
• ... and for 30 seconds scans the surrounding APs every

10 seconds in stationary state

• the sampling interval is set at 2 minutes
• the similarity threshold of the Wi-Fi vector is set at 0.7

Advantages

• energy efficient
• accurate
• no need for special infrastructure
• even better results when using asymmetry

multicore processor (AMP) technology

• sends information to server in case of

discharging

• can be used to find child or pet

• Wi-Fi infrastructure dependent
• requirements for chasing device
• misleading GPS readings
• misleading Wi-Fi signal from open space

Disadvantages

The end