Post on 03-Jun-2018
8/12/2019 551 Smartphone
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Dong Xuan (CSE/OSU) / 2009
Design and Implementation of
Smartphone-based Systems andNetworking
Dong Xuan
Department of Computer Science and Engineering
The Ohio State University, USA
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Dong Xuan (CSE/OSU) / 2010 2
Outline
Smartphones Basics
Mobile Social Networks
E-Commerce
E-Health
Safety Monitoring
Future Research Directions
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Dong Xuan (CSE/OSU) / 2010
A smartphoneis a mobile phone offering advancedcapabilities, often with PC-like functionality
Hardware (Apple iPhone 3GS as an example)
CPU at 600MHz, 256MB of RAM
16GB or 32GB of flash ROM
Wireless: 3G/2G, WiFi, Bluetooth
Sensors: camera, acceleration, proximity, light
Functionalities
Communication
News & Information
Socializing
Gaming
Schedule Management etc.
Smartphone Basics
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Dong Xuan (CSE/OSU) / 2010
Smartphones are popular and will become more popular
Smartphone Popularity
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Dong Xuan (CSE/OSU) / 2010
Smartphone Accessories
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Dong Xuan (CSE/OSU) / 2010 6
Smartphone Features
Communication/Sensing/Computation
Inseparable from our human life
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7/82Dong Xuan (CSE/OSU) / 2010
OurSmartphone Systems
E-SmallTalker [IEEE ICDCS10]:senses information published byBluetooth to help potential friends findeach other (written in Java)
E-Shadow [IEEE ICDCS11]: enablesrich local social interactions with
local profiles and mobile phone
based local social networking tools
P3
-Coupon [IEEE Percom11]:automatically distributes electronic
coupons based on an probabilistic
forwarding algorithm
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8/82Dong Xuan (CSE/OSU) / 2010
OurSmartphone Systems
Drunk Driving Detection [Per-Health10]: uses smartphone (GoogleG1) accelerometer and orientationsensor to detect
Stealthy Video Capturer [ACMWiSec09]: secretly senses itsenvironment and records video viasmartphone camera and sends it to athird party (Windows Mobileapplication)
Download & Run Video sent by Email Captured Video
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9/82Dong Xuan (CSE/OSU) / 2010
Exemplary System I:E-SmallTaker
Small Talk
A Nave Approach
Challenges
System Design
Implementation and Experiments
Remarks
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10/82Dong Xuan (CSE/OSU) / 2010
Small Talk
People come into contact opportunistically
Face-to-face interaction
Crucial to people's social networking
Immediate non-verbal communication
Helps people get to know each other
Provides the best opportunity to expand social network
Small talk is an important social lubricant
Difficult to identify significant topics
Superficial
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11/82Dong Xuan (CSE/OSU) / 2010
A Naive Approach of Smartphone-
based Small Talk Store all users information, including each users full contact
list
User report either his own geo-location or a collection ofphone IDs in his physical proximity to the server using internet
connection or SMS
Server performs profile matching, finds out small talk topics
(mutual contact, common interests, etc.)
Results are pushed to or retrieved by users
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12/82Dong Xuan (CSE/OSU) / 2010
However
Require costly data services (phones internet
connection, SMS)
Require report and store sensitive personalinformation in 3rdparty
Trusted server may not exist
Server is a bottleneck, single point of failure, target ofattack
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13/82Dong Xuan (CSE/OSU) / 2010
E-SmallTalkerA Fully
Distributed Approach No Internet connection required
No trusted 3rdparty
No centralized server
Information stored locally on mobile phones
Original personal data never leaves a users phone
Communication only happens in physical proximity
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14/82Dong Xuan (CSE/OSU) / 2010
Two Challenges
How to exchange information without establishing a Bluetoothconnection Available data communication channels on mobile phones
Cellular network (internet, SMS, MMS), Bluetooth, WiFi, IrDA
Bluetooth is a natural choice
Bluetooth connection needs users interaction due to security reasons
How to find out common topics while preserving users privacy No pre-shared secret for strangers
Bluetooth Service Discovery Protocol can only transfer limited serviceinformation
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15/82Dong Xuan (CSE/OSU) / 2010
System Architecture Context exchange
Context encoding and matching
Context data store
User Interface
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16/82Dong Xuan (CSE/OSU) / 2010
Context Exchange
Exploit Bluetooth service discovery protocol No Bluetooth connection needed
Publish encoded contact data (non-service related) as (virtual) service attributes
Limited size and number( e.g. 128 bytes max each attribute)
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17/82Dong Xuan (CSE/OSU) / 2010
Context Encoding
Example of Alices Bloom
filter
Alice has multiple contacts,
such as Bob, Tom, etc.
Encode contact strings,
Firstname.lastname@phone
_number, such as
Bob.Johnson@5555555555
and
Tom.Mattix@6141234567
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mailto:Firstname.lastname@phone_numbermailto:Firstname.lastname@phone_numbermailto:Firstname.lastname@phone_numbermailto:Firstname.lastname@phone_numberhttp://images.google.com/imgres?imgurl=http://people.cohums.ohio-state.edu/gurney13/Pictures/Ohio_State_Logo.jpg&imgrefurl=http://people.cohums.ohio-state.edu/gurney13/&h=177&w=177&sz=57&hl=en&start=2&um=1&tbnid=yq7bubUHHFO5QM:&tbnh=101&tbnw=101&prev=/images%3Fq%3Dthe%2Bohio%2Bstate%2Buniversity%2Blogo%26um%3D1%26hl%3Den%26sa%3DNhttp://images.google.com/imgres?imgurl=http://people.cohums.ohio-state.edu/gurney13/Pictures/Ohio_State_Logo.jpg&imgrefurl=http://people.cohums.ohio-state.edu/gurney13/&h=177&w=177&sz=57&hl=en&start=2&um=1&tbnid=yq7bubUHHFO5QM:&tbnh=101&tbnw=101&prev=/images%3Fq%3Dthe%2Bohio%2Bstate%2Buniversity%2Blogo%26um%3D1%26hl%3Den%26sa%3DN8/12/2019 551 Smartphone
18/82Dong Xuan (CSE/OSU) / 2010
Implementation
J2ME about 40 java classes, 127Kb jar file
On real phones Sony Ericsson (W810i), Nokia (5610xm, 6650, N70, N75,
N82)
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19/82Dong Xuan (CSE/OSU) / 2010
Experiments
Settings 6 phones, n=150, k=7, m=1024 bits, default distance=4m, average of
10 runs
Performance Metrics Discovery time: the period from the time of starting a search to the time of
finding someone with common interest, if there is any
Discovery rate:percentage of successful discoveries among all attempts
Power consumption
Factors Bluetooth search interval Number of users
Distance
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20/82Dong Xuan (CSE/OSU) / 2010
Experiment Results
Minimum, average and maximum discovery time are
13.39, 20.04 and 59.11 seconds respectively
Always success if repeat searching, 90% overall ifonly search once
Nokia N82 last 29 hours when discovery interval is
60 seconds
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21/82Dong Xuan (CSE/OSU) / 2010
Related Work Social network applications on mobile phones
Social Serendipity
Centralized, Bluetooth MAC and profile matching, SMS, strangers
PeopleTones, Hummingbird, Just-for-Us, MobiLuck, P3 Systems, Micro-Blog,and Loopt
Centralized, GPS location matching, Internet, existing friends
Nokia Sensor and PeopleNet
Distributed, profile, Bluetooth / Wifi connection, existing friends
Private matching and set intersection protocols
Homomorphic encryption based
Too much computation and message overhead for mobile phone Limitations
Require costly data services (phones internet connection, SMS)
Require report and store sensitive personal information
Bottleneck, single point of failure, target of attack
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22/82Dong Xuan (CSE/OSU) / 2010
Remarks
Propose, design, implement and evaluate the E-SmallTalker
system which helps strangers initialize a conversation
Leveraged Bluetooth SDP to exchange these topics without
establishing a connection
Customized service attributes to publish non-service related
information.
Proposed a new iterative commonality discovery protocol based on
Bloom filters that encodes topics to fit in SDP attributes to achieve a
low false positive rate
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Dong Xuan (CSE/OSU) / 2010
Exemplary System II:E-Shadow
Concept
Application Scenario
Goals and Challenges
System Design
Implementation and Experiments
Remarks
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Dong Xuan (CSE/OSU) / 2010
Concept
Motivation
Importance of Face-to-Face Interaction
Prevalence of mobile phones Distributed mobile phone-based local social
networking system
Local profilesMobile phone based local social interaction tools
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Dong Xuan (CSE/OSU) / 2010
Application Scenario: Conference
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Dong Xuan (CSE/OSU) / 2010
Goals and Challenges
Design GoalsFar-reaching and Unobtrusive
Privacy and Security
Auxiliary Support for Further InteractionsBroad Adoption
Challenges
Lack of Communication SupportPower and Computation Limitation
Non-pervasive Localization Service
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Dong Xuan (CSE/OSU) / 2010
Layered Publishing
Spatial Layering
WiFi SSID at least 40-50 meters, 32 Bytes
Bluetooth Device (BTD) Name 20 meters, 2k Bytes
Bluetooth Service (BTS) Name 10 meters, 1k Bytes
Temporal LayeringFor people being together long or repeatedly
Erasure Code
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Dong Xuan (CSE/OSU) / 2010
E-Shadow Publishing Procedure
Valve Generator
Information
Filter
Database
Sensor
Feedback
User
Maual
Input
Online
Data
Mining
BT
Device
BT
Service
WiFi
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Dong Xuan (CSE/OSU) / 2010
Matching E-Shadow with its Owner
Intuitive Approach: Localization
However, imprecision beyond 20-25 meters
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Dong Xuan (CSE/OSU) / 2010
Human Direction-driven Localization
Direction more important than distance Human observation
A new range-free localization technique RSSI comparison: Less prone to errors
Space partitioning: Tailored for direction decision
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Dong Xuan (CSE/OSU) / 2010
Walking Route and Localization We allow users to walk a distance
Triangular route: A->B->C in (a), for illustration purposes
Semi-octogonal route: A->B->C->D->E in (c), more natural
Take measurements on turning points
Calculate the direction through RSSI comparison and space
partitioning
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Dong Xuan (CSE/OSU) / 2010
Implementation
InformationPublishing Module
Database
Generator
Buffers
Control Valve
Broadcasting
Interfaces
Retrieval &
Matching Module
Receivers
Localization
Decoding & Storage
Sensing Module
User Interface
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Dong Xuan (CSE/OSU) / 2010
Evaluations (1)-Time & Energy
E-Shadow Collection Time
WiFi SSID: 2 seconds
BTD: 12-18 seconds
BTS: 25-35 seconds
E-Shadow Power
Consumption
3 hours in full performanceoperation
>12 hours in typical situation
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Dong Xuan (CSE/OSU) / 2010
Evaluations (2)-Localization
3 Outdoor Experiments:
Open field campus
2 Indoor Experiments:
Large classroom
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Dong Xuan (CSE/OSU) / 2010
Evaluation (3)-Simulations
Large-Scale Simulations:
Angle deviation CDFs
12 times of exemplary
direction decisions
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Dong Xuan (CSE/OSU) / 2010
Related Work
Centralized mobile phones applications Social Serendipity
Centralized, Bluetooth MAC and profile matching, SMS, strangers
Decentralized mobile phone applications
Nokia Sensor
Distributed, profile, Bluetooth / Wifi connection, existing friends
E-Smalltalker Distributed, no Bluetooth / Wifi connection, strangers
Localization techniques for mobile phones applications
GPS
Virtual Compass
peer-based relative positioning system using Wi-Fi and Bluetooth radios
Limitations Privacy compromise
Unable to capture the dynamics of surroundings
No mapping between electronic ID and human face
Localization techniques either not pervasive or not accurate for long range
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Dong Xuan (CSE/OSU) / 2010
Remarks
Propose, design, implement and evaluate the E-Shadow
system which lubricates local social interactions
E-Shadow concept
Layered publishing to capture the dynamics of surroundings
Human-assisted matching that works for mapping E-Shadow with its
owner in a fairly large distance
Implementing and evaluating E-Shadow on real world mobile phones
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Dong Xuan (CSE/OSU) / 2010
Exemplary System III:P3-Coupon
Coupon Distribution
A Nave Approach
Challenges
System Design
Implementation and Experiments
Remarks
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Dong Xuan (CSE/OSU) / 2010
Electronic Coupon Distribution
Electronic coupons
Similar to paper coupons
Can be stored on mobile phones
Two distribution methods Downloading from Internet websites
Need to define target group
Limited coverage
Hard to maintain dynamic preferences lists on central databases
Peer to Peer Distribution No special destination/target group
More coverage
More flexible user-maintained preferences list
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Dong Xuan (CSE/OSU) / 2010
A Naive Approach of Peer-to-Peer
Coupon Distribution A store periodically broadcast the coupon
Users within broadcast range receive the coupon
User can decide whether to use, forward or discard the coupon Users forward the coupon to others in physical proximity
Forwarders IDs are recorded in a dynamically expanding list
The coupon is used by some user
The store reward all users who have forwarded the coupon
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Dong Xuan (CSE/OSU) / 2010
However
Require manually establishing wireless connections Cumbersome
Not prompt
Not possible for coupon forwarding among strangers
Require recording the entire forwarding path Potential privacy leakage
Discourage users forwarding incentives
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Dong Xuan (CSE/OSU) / 2010
Challenge
How to design a prompt coupon distribution
mechanism that
Incentivize coupon forwarder appropriately for keeping thecoupons circulating
Preserve the privacy of coupon forwarders
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Dong Xuan (CSE/OSU) / 2010
P3-CouponA Probabilistic
Coupon Forwarding Approach Probabilistic sampling on forwarding path
Keep only one forwarder for each coupon: NO privacy leakage
Probabilistically flip ownership at each hop
Accurate approximation of coupon rewards plenty of chances of interpersonal encounters
Accurate bonus distribution with 50 coupons and 5000 people
Adaptive to different promotion strategies
Flip-once model Always-flip model
No manual connection establishment Connectionless information exchange via Bluetooth SDP
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Dong Xuan (CSE/OSU) / 2010
System Architecture Store Side
A central server for broadcasting and redeeming coupons
Client side Coupon forwarding manager, coupon exchange, coupon data store, user
interface
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Dong Xuan (CSE/OSU) / 2010
Probabilistic Forwarding Algorithm
Always-Flip Model
The coupon ownership keeps flipping with certain probability at each hop.
Good at assigning relative bonuses affected by the whole path lengths
E.g. the parent forwarder receives k times the bonus given to children forwarders
The flip probability can be calculated in advance by the store, once k is fixed, using
the following formula
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Dong Xuan (CSE/OSU) / 2010
Probabilistic Forwarding Algorithm
Extension: Flip-Once Model
Once flipped, a coupons ownership remain the same in a forwarding path.
Good at assigning absolute bonuses irrelevant of the number of followingforwarders
E.g. hop 1 user gets 10%, hop 2 user gets 5%, etc.
The flip probability can be calculated in advance by the store using the followingformula
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Dong Xuan (CSE/OSU) / 2010
Coupon Format
Coupon description Product description
Discounts
Coupon issuer
Coupon code Start/end date
Coupon forwarder information The current owner
Digital signature Prevent forging fraud coupons
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Dong Xuan (CSE/OSU) / 2010
Implementation
J2ME about 17 java classes, 1390Kb jar file
On real phones Samsung (SGH-i550), Nokia (N82, 6650, N71x)
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Dong Xuan (CSE/OSU) / 2010
Experiments
Experimental evaluations Coupon forwarding time
Power consumption
Simulation evaluation Number of Coupon holders vs. Time
Distribution saturation time vs. Number of Seeds
Coupon ownership distribution for probabilistic sampling
Deviation between theoretical and actual bonus (Always-Flip, Flip-Once)
Factors Number of coupons
Number of users
Number of initial coupon holders
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Dong Xuan (CSE/OSU) / 2010
Experiment Results
Average coupon forwarding time is 33.52 seconds
Nokia N82 last 25 hours with P3-Coupon running in
background
One coupon could be delivered to 5000 people within 32 hours
Very small deviation between theoretical and actual bonus
distribution with 50 coupons circulating among 5000 people
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Dong Xuan (CSE/OSU) / 2010
Remarks
Propose, design, implement and evaluate the P3-Coupon
system which helps prompt and privacy preserving coupon
distribution
Probabilistic one-ownership coupon forwarding algorithm
Implement the system on various types of mobile phones
Extensive experiments and evaluations show that our approach
accurately approximate the theoretical coupon distribution in which the
whole forwarding path needs to be recorded
Practical for real-world deployment
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Dong Xuan (CSE/OSU) / 2010
Exemplary System IVDrunk Driving
DetectionMotivation
Our Contributions
Detection CriteriaOur System
Related Work
Implementation and EvaluationRemarks
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Dong Xuan (CSE/OSU) / 2010
MotivationCrashes caused by alcohol-impaired driving pose a
serious danger to the general public safety and health
13,041 and 11,773 driving fatalities happened in 2007 and
2008*
32% of the total fatalities in these two years*
Drunk driving also imposes a heavy financial burden on
the whole society
Annual cost of alcohol-related crashes totals more than $51billion*** Data from U.S. NHTSA (National Highway Traffic Safety Administration)
** Data from U.S. CDC (Central of Disease Control)
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Dong Xuan (CSE/OSU) / 2010
Motivation
Detection of drunk driving so far still relies on visual
observation by patrol officers
Drunk drivers usually make certain types of dangerous maneuvers
NHTSA researchers identify cues of typical drunk driving behavior
Visual observation is insufficient to prevent drunk driving
The number of patrol officers is far from enough
The guidelines are only descriptive and qualitative
Usually, it is too late when drunk drivers are stopped by officersIt is essential to develop systems actively monitoring drunk
driving and to prevent accidents
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Dong Xuan (CSE/OSU) / 2010
Our Contributions
Propose utilizing mobile phones as a platform for
active drunk driving detection system
Design a real-time algorithm for drunk driving
detection system using mobile phones
Simple sensors required only
i.e., accelerometers and orientation sensors
Design and implement a mobile phone-based activedrunk driving detection system
Reliable, Non-intrusive, Lightweight and power efficient, and
No extra hardware and service cost
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Dong Xuan (CSE/OSU) / 2010
Cues for Drunk Driving DetectionCues related to lane position maintenance problems
E.g., weaving, drifting, swerving and turning with a wide radius
Cues related to speed control problems
E.g., accelerating or decelerating suddenly, and braking erratically
Cues related to judgment and vigilance problems
E.g., driving with tires on lane marker, slow response to traffic signals
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Dong Xuan (CSE/OSU) / 2010
Drunk Driving Detection Criteria
Extract fundamental detection criteria from these
cues
Capture the acceleration features
E.g., for the lane position maintenance problems
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Dong Xuan (CSE/OSU) / 2010
Drunk Driving Detection Criteria
Focus on the first two categories of cues
They correspond to higher probabilities of drunk driving
Map them into patterns of acceleration
Probability of drunk driving detection goes higher while
the number of observed cues increases
Drivers
problems in
maintaining lane
position
Abnormal lateral
movements
Patterns oflateral
acceleration of
vehicles
Drivers
problems in
controlling
speed
Abrupt speed
variations
Patterns of
longitudinal
acceleration of
vehicles
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Dong Xuan (CSE/OSU) / 2010
Our System
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Dong Xuan (CSE/OSU) / 2010
Implementation
Develop the prototype system on Android G1 phone with
accelerometer and orientation sensor
Implement the prototype in Java, with Eclipse and Android 1.6
SDK
The whole prototype system can be divided into five major
components
User interface System configuration Monitoring daemon
Data processing Alert notification
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Dong Xuan (CSE/OSU) / 2010
Evaluation - Testing Data Collection
Test data
72 sets of data with simulated drunk driving related behaviors
- Weaving, swerving, turning with a wide radius
- Changing speed erratically (accelerating or decelerating)
22 sets of data for regular driving
- Each one for 5 to 10 minutes
Mobile phone positions in the vehicle
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Dong Xuan (CSE/OSU) / 2010
Evaluation - Detection Performance
Study the accuracy of detecting drunk driving related behaviors
In terms of false negative and false positive
Study performance in the special case, such as the phone slides in the vehicle
during driving
Slides has obvious impacts on detection accuracy
May add additional calibration procedure to solve it (future work)
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Dong Xuan (CSE/OSU) / 2010
EvaluationEnergy Efficiency
Curves of battery level states during mobile phone running
Phone runs without drunk driving detection system
Monitoring daemon of system keeps running, sensing and doing the pattern
matching on the monitoring results
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Dong Xuan (CSE/OSU) / 2010
Related WorkDriver vigilance monitoring and driver fatigue prevention
Monitoring the visual cues of drivers to detect fatigue in driving
Installed cameras just in front of drivers are potential safety hazard
Monitoring through vehicle-human interfaceCapture fatigued or drunk driving through monitoring interactions
Low compatibility, vehicles need to couple with auxiliary add-ons
Detect abnormal driving through GPS and acceleration dataPattern matching with GPS and acceleration data
However, GPS data are not always available
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Dong Xuan (CSE/OSU) / 2010
Remarks
First to propose utilizing mobile phones as a platform for
developing active drunk driving detection system
Design and implement an efficient detection system based on
mobile phone platforms
Experimental results show our system achieves good detection
performance and power efficiency
In the future work, to improve the system with additional
calibration procedure and by integrating all available sensingdata on a mobile phone such as camera image
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Dong Xuan (CSE/OSU) / 2010
Exemplary System V: Stealthy Video
Capturer Background
SVC Overview
Challenges
Our Approaches
Experimental Evaluations
Remarks
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Dong Xuan (CSE/OSU) / 2010
Background
More and more private information is entrusted to
our friend, the 3G Smartphone, which is getting
more and more powerful in performance and
diversified in functionality.
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Dong Xuan (CSE/OSU) / 2010
System Architecture
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Dong Xuan (CSE/OSU) / 2010
Challenges
Stealthily install SVC into 3G SmartphonesWindows Hiding
Infection Method
Collect the video information from 3GSmartphones DirectShow Controls
Data Compressing Send the video file to the SVC intender
File Sending
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Dong Xuan (CSE/OSU) / 2010
Infection Method
To embed SVC in a 3G Smartphone is called a
infection process.
We employ Trojan horse for downloads as theinfection approach.
Our experimental SVC is hidden in the game
of tic-tac-toe that we develop in WindowsMobile environment.
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Dong Xuan (CSE/OSU) / 2010
The Scenario of Tic-Tac-Toe
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Dong Xuan (CSE/OSU) / 2010
Triggering Schemes
Triggering Algorithm is designed to determine when
to turn on the video capture process and send the
captured video to make SVC stealthier and get more
useful information.
Three scenarios are under consideration.
The first scenario is tracking.
The second scenario is related with political or businessespionage.
The third scenario is a hybrid one, where SVC is used for
much diversified everyday purposes.
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Dong Xuan (CSE/OSU) / 2010
Applications
Suspects tracking
Kids care
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Dong Xuan (CSE/OSU) / 2010
Kids tracking
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Dong Xuan (CSE/OSU) / 2010
Implementation
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Dong Xuan (CSE/OSU) / 2010
Experimental Evaluations:
Power Consumption Power curve
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Dong Xuan (CSE/OSU) / 2010
Experimental Evaluations:
CPU and Memory Usage CPU and Memory
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Dong Xuan (CSE/OSU) / 2010
Remarks
The initial study exploited from SVC will draw wide
attentions on 3G Smartphones privacy protection and
open a new horizon on 3G Smartphones security
research and applications.
We are currently investigating the modeling of smart
spyware from the study of spear and shield.
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Dong Xuan (CSE/OSU) / 2010
A Summary
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Dong Xuan (CSE/OSU) / 2010
Future Research Directions
Smartphone-based Systems and Networking
Mobile social networking, e-commerce, e-health, safetymonitoring etc.
Easy to start and exciting but too many competitors, lack ofscientific depth
Smartphone Core Improvement
Multitasking, power management, efficient local
communication protocol, accurate localization,security/privacy protection
Deep but hard to start
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82/82
Final Remarks
Smartphones have brought significant impacts
to our daily life.
We present five exemplary systems on mobilesocial networking, e-commerce, e-health and
safety.
Research and development on smartphoneswill be hot.
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