Design and Implementation of Smartphone-based Systems and Networking - PowerPoint PPT Presentation

Design and Implementation of Smartphone-based Systems and Networking

Dong Xuan

Department of Computer Science and Engineering

The Ohio State University, USA

Outline

• Smartphones Basics
• Mobile Social Networks
• E-Commerce
• E-Health
• Safety Monitoring
• Future Research Directions

• A smartphone is a mobile phone offering advanced capabilities, 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.

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Smartphones are popular and will become more popular

4

Smartphone Accessories

Smartphone Features

• Communication/Sensing/Computation
• Inseparable from our human life

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• E-SmallTalker [IEEE ICDCS10]:

senses information published by Bluetooth to help potential friends find each other (written in Java)

• E-Shadow [IEEE ICDCS11]: enables rich 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

Drunk Driving Detection [Per-Health10]: uses smartphone (Google G1) accelerometer and orientation sensor to detect

Stealthy Video Capturer [ACM WiSec09]: secretly senses its environment and records video via smartphone camera and sends it to a third party (Windows Mobile application)

Download & Run

Video sent by Email

Captured Video

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• Small Talk
• A Naïve Approach
• Challenges
• System Design
• Implementation and Experiments
• Remarks

• 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

• Store all user’s information, including each user’s full contact list
• User report either his own geo-location or a collection of phone 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

• Require costly data services (phone’s internet connection, SMS)
• Require report and store sensitive personal information in 3rd party
• Trusted server may not exist
• Server is a bottleneck, single point of failure, target of attack

• No Internet connection required
• No trusted 3rd party
• No centralized server
• Information stored locally on mobile phones
• Original personal data never leaves a user’s phone
• Communication only happens in physical proximity

• How to exchange information without establishing a Bluetooth connection
• Available data communication channels on mobile phones
• Cellular network (internet, SMS, MMS), Bluetooth, WiFi, IrDA
• Bluetooth is a natural choice
• Bluetooth connection needs user’s 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 service information

• Context exchange
• Context encoding and matching
• Context data store
• User Interface

• 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)

• Example of Alice’s 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”

• J2ME
• about 40 java classes, 127Kb jar file
• On real phones
• Sony Ericsson (W810i), Nokia (5610xm, 6650, N70, N75, N82)

• 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

• Minimum, average and maximum discovery time are 13.39, 20.04 and 59.11 seconds respectively
• Always success if repeat searching, 90% overall if only search once
• Nokia N82 last 29 hours when discovery interval is 60 seconds

• 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 (phone’s internet connection, SMS)
• Require report and store sensitive personal information
• Bottleneck, single point of failure, target of attack

• 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

Concept

Application Scenario

Goals and Challenges

System Design

Implementation and Experiments

Remarks

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• Motivation
• Importance of Face-to-Face Interaction
• Prevalence of mobile phones
• Distributed mobile phone-based local social networking system
• Local profiles
• Mobile phone based local social interaction tools

• Design Goals
• Far-reaching and Unobtrusive
• Privacy and Security
• Auxiliary Support for Further Interactions
• Broad Adoption
• Challenges
• Lack of Communication Support
• Power and Computation Limitation
• Non-pervasive Localization Service

• 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 Layering
• For people being together long or repeatedly
• Erasure Code

Sensor

Help

Decide

Decide

User Maual Input

Online Data Mining

Feedback

Valve

Generator

Filter

Information

BT

Device

Database

BT

Service

WiFi

Intuitive Approach: Localization

However, imprecision beyond 20-25 meters

• 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

• 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

• Information Publishing Module
• Database
• Generator
• Buffers
• Control Valve
• Broadcasting Interfaces
• Retrieval & Matching Module
• Receivers
• Localization
• Decoding & Storage
• Sensing Module
• User Interface

• E-Shadow Collection Time
• WiFi SSID: 2 seconds
• BTD: 12-18 seconds
• BTS: 25-35 seconds
• E-Shadow Power Consumption
• 3 hours in full performance operation
• >12 hours in typical situation

3 Outdoor Experiments:

Open field campus

2 Indoor Experiments:

Large classroom

Large-Scale Simulations:

Angle deviation CDFs

12 times of exemplary direction decisions

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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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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Coupon Distribution

A Naïve Approach

Challenges

System Design

Implementation and Experiments

Remarks

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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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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

Forwarder’s 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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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 user’s forwarding incentives

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• How to design a prompt coupon distribution mechanism that
• Incentivize coupon forwarder appropriately for keeping the coupons circulating
• Preserve the privacy of coupon forwarders

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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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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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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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Extension: Flip-Once Model

Once flipped, a coupon’s ownership remain the same in a forwarding path.

Good at assigning absolute bonuses irrelevant of the number of following forwarders

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 following formula

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• 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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J2ME

about 17 java classes, 1390Kb jar file

On real phones

Samsung (SGH-i550), Nokia (N82, 6650, N71x)

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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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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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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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Motivation

Our Contributions

Detection Criteria

Our System

Related Work

Implementation and Evaluation

Remarks

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• Crashes 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 $51 billion**

* Data from U.S. NHTSA (National Highway Traffic Safety Administration)

** Data from U.S. CDC (Central of Disease Control)

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• 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 officers
• It is essential to develop systems actively monitoring drunk driving and to prevent accidents

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• 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 active drunk driving detection system
• Reliable, Non-intrusive, Lightweight and power efficient, and No extra hardware and service cost

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• Cues 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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• Extract fundamental detection criteria from these cues
• Capture the acceleration features
• E.g., for the lane position maintenance problems

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Abrupt speed variations

Abnormal lateral movements

Driver’s problems in controlling speed

Patterns of longitudinal acceleration of vehicles

Driver’s problems in maintaining lane position

Patterns of lateral acceleration of vehicles

• 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

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• 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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• 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 onefor 5 to 10 minutes
• Mobile phone positions in the vehicle

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• 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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• 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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• Driver 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 interface
• Capture fatigued or drunk driving through monitoring interactions
• Low compatibility, vehicles need to couple with auxiliary add-ons
• Detect abnormal driving through GPS and acceleration data
• Pattern matching with GPS and acceleration data
• However, GPS data are not always available

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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 sensing data on a mobile phone such as camera image

• Background
• SVC Overview
• Challenges
• Our Approaches
• Experimental Evaluations
• Remarks

• 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.

• Almost every 3G Smartphone is equipped with a camera and the wireless options, such as 3G networks, BlueTooth, WiFi or IrDA.
• These wireless connections are good enough to handle certain types of video transmission.
• We turn 3G Smartphones into an online stealthy video-recorder.

• Stealthily install SVC into 3G Smartphones
• Windows Hiding
• Infection Method
• Collect the video information from 3G Smartphones
• DirectShow Controls
• Data Compressing
• Send the video file to the SVC intender
• File Sending

• To embed SVC in a 3G Smartphone is called a infection process.
• We employ Trojan horse for downloads as the infection approach.
• Our experimental SVC is hidden in the game of ”tic-tac-toe” that we develop in Windows Mobile environment.

• 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 business espionage.
• The third scenario is a hybrid one, where SVC is used for much diversified everyday purposes.

• Suspects tracking
• Kids care

• Power curve

• CPU and Memory

• The initial study exploited from SVC will draw wide attentions on 3G Smartphone’s 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”.

• Smartphone-based Systems and Networking
• Mobile social networking, e-commerce, e-health, safety monitoring etc.
• Easy to start and exciting but too many competitors, lack of scientific depth
• Smartphone Core Improvement
• Multitasking, power management, efficient local communication protocol, accurate localization, security/privacy protection
• Deep but hard to start

Smartphones have brought significant impacts to our daily life.

We present five exemplary systems on mobile social networking, e-commerce, e-health and safety.

Research and development on smartphones will be hot.

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