551 smart phone long handout
TRANSCRIPT
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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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Outline
Smartphones Basics
Mobile Social Networks
E-Commerce
E-Health
Safety Monitoring
Future Research Directions
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A smartphone is 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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Smartphones are popular and will become more popular
Smartphone Popularity
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Smartphone Accessories
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Smartphone Features
Communication/Sensing/Computation
Inseparable from our human life
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Our Smartphone 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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Our Smartphone Systems
Drunk Driving Detection [Per-Health10]: uses smartphone (GoogleG1) accelerometer and orientationsensor to detect
Stealthy Video Capturer [ACMWiSec09]: secretly senses itsenvironment and records video via
smartphone camera and sends it to athird party (Windows Mobileapplication)
Download & Run Video sent by Email Captured Video
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Exemplary System I:E-SmallTaker Small Talk
A Nave Approach
Challenges
System Design
Implementation and Experiments
Remarks
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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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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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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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E-SmallTalker A 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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Two Challenges 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 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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System Architecture Context exchange Context encoding and matching
Context data store
User Interface
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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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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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Experiments Settings
6 phones, n=150, k=7, m=1024 bits, default distance=4m, average of10 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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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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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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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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Exemplary System II:E-Shadow
Concept
Application Scenario
Goals and Challenges
System Design
Implementation and Experiments
Remarks
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Concept 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
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Application Scenario: Conference
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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 Layering For people being together long or repeatedly
Erasure Code
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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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Matching E-Shadow with its Owner
Intuitive Approach: Localization
However, imprecision beyond 20-25 meters
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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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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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Implementation
Information
Publishing Module
Database
Generator
Buffers
Control Valve Broadcasting
Interfaces
Retrieval &
Matching Module
Receivers
Localization
Decoding & Storage
Sensing Module
User Interface
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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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Evaluations (2)-Localization
3 Outdoor Experiments:
Open field campus
2 Indoor Experiments:
Large classroom
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Evaluation (3)-Simulations
Large-Scale Simulations:
Angle deviation CDFs
12 times of exemplary
direction decisions
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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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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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Exemplary System III:P3-Coupon
Coupon Distribution
A Nave Approach
Challenges
System Design
Implementation and Experiments
Remarks
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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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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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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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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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P3-Coupon A 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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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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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, usingthe following formula
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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 following
formula
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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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Implementation
J2ME about 17 java classes, 1390Kb jar file
On real phones Samsung (SGH-i550), Nokia (N82, 6650, N71x)
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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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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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Exemplary System IV Drunk Driving
DetectionMotivation
Our Contributions
Detection Criteria
Our System
Related Work
Implementation and EvaluationRemarks
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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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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 officers
It is essential to develop systems actively monitoring drunk
driving and to prevent accidents
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Our Contributions
Propose utilizing mobile phones as a platform for
active drunk driving detection system
Design a real-time algorithm for drunk drivingdetection 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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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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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
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Driversproblems in
maintaining lane
position
Abnormal lateral
movements
Patterns oflateral
acceleration of
vehicles
Drivers
problems in
controllingspeed
Abrupt speed
variations
Patterns of
longitudinal
acceleration ofvehicles
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Our System
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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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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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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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Evaluation Energy 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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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 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 dataPattern matching with GPS and acceleration data
However, GPS data are not always available
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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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Exemplary System V: Stealthy Video
Capturer Background
SVC Overview
Challenges
Our Approaches
Experimental Evaluations
Remarks
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Background
More and more private information is entrusted to
our friend, the 3G Smartphone, which is getting
more and more powerful in performance anddiversified in functionality.
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SVC Overview
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.
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System Architecture
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Challenges
Stealthily install SVC into 3G Smartphones Windows 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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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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The Scenario of Tic-Tac-Toe
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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 moreuseful 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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Applications
Suspects tracking
Kids care
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Kids tracking
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Implementation
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Experimental Evaluations:
Power Consumption Power curve
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Experimental Evaluations:
CPU and Memory Usage CPU and Memory
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Remarks
The initial study exploited from SVC will draw wide
attentions on 3G Smartphones privacy protection and
open a new horizon on 3G Smartphones securityresearch and applications.
We are currently investigating the modeling of smart
spyware from the study of spear and shield.
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A Summary
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Future Research Directions
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 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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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.