A Survey of Mobile Phone

matchmoaningAI and Robotics

Nov 17, 2013 (3 years and 8 months ago)

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A Survey of Mobile Phone
Sensing

Michael Ruffing

CS 495

Paper Info


Published in September 2010


Dartmouth College


joint effort between
graduate students and professors (Mobile
Sensing Group)

Outline



Current Mobile Phone Sensing


Hardware


Applications


Sensing Scale and Paradigms


Architectural Framework for discussing
current issues and challenges

Smartphone Technological Advances



Cheap embedded sensors



Open and programmable


Each vendor offers an app store


Mobile computing cloud for offloading
services to backend servers


iPhone 4
-

Sensors

Future Sensors


Barometer


Temperature


Humidity



To early to tell


cost and form factor will drive
the availability of new sensors

Applications


Transportation


Traffic conditions (MIT VTrack, Mobile Millennium
Project)


Social Networking


Sensing Presence (Dartmouth’s CenceMe project)


Environmental Monitoring


Measuring pollution (UCLA’s PIER Project)


Health and Well Being


Promoting personal fitness (UbiFit Garden)



Application Stores


Multiple vendors


Apple AppStore


Android Market


Microsoft Mobile Marketplace


Developers


Startups


Academia


Small Research laboratories


Individuals


Critical mass of users


Application Stores


Current issues and challenges


User selection


Validation


Privacy of users


Scaling and data management



Sensing Scale

Sensing Scale


Personal Sensing


Generate data for the sole consumption of the user,
not shared


Group Sensing


Individuals who participate in an application that
collectively share a common goal, concern, or interest


Community Sensing


Large
-
scale data collection, analysis, and sharing for
the good of the community

Sensing Paradigms


Opportunistic Sensing
-

data collection is fully
automated with no user interaction


Lowers burden placed on the user


Technically hard to build


people underutilized


Phone context problem


Participatory Sensing
-

user actively engages in
the data collection activity


Supports complex operations


Quality of data dependent on participants



Mobile Phone Sensing Architecture


Goal


architectural model for discussion


Components


Sense


Learn


Inform, Share, Persuasion

Sense


Programmability


Mixed API and OS support for low
-
level sensors


Difficult to port application to multiple vendors


Continuous Sensing


Resource demanding


Low energy algorithms


Trade
-
off between accuracy and energy cost


Phone Context


Dynamic environments


Super
-
sampling using nearby phones


Learn: Interpreting Sensor Data
(Human Behavior)


Current applications are very much people centric


Learning algorithms


fits a model to classes
(behavior)


Supervised


data is hand labeled


Semi
-
supervised


some of the data is labeled


Unsupervised


none of the data is labeled


Inferring human behavior via Sensors


GPS


Microphone


Scaling Models


Scalability Key: Generalized design techniques that take
into count large communities (millions of people)


Models must be adaptive and incorporate people into
the process


Exploit social networks (community guided learning) to
improve data classification and solutions


Challenges:


Common machine learning toolkits


Large
-
scale public data sets


Research sharing and collaboration



Inform, Share, and Persuasion


Sharing


Visualization of the inferred data


Formation of communities around the sensing application and data


Community awareness


Social networks


Personalized Sensing


Voice recognition


Profile user preferences


Personalized recommendations


Persuasion


Persuasive technology


systems that provide tailored feedback with the goal of changing
user’s behavior


Motivation to change human behavior


Games


Competitions


Goal setting


Interdisciplinary research combining behavioral and social psychology with computer science




Privacy


Respecting the privacy of the user is the most
fundamental responsibility of a phone sensing
system


Current Solutions


Cryptography


Privacy
-
preserving data mining


Processing data locally versus cloud services


Group sensing applications is based on user
membership and/or trust relationships



Privacy


Current Challenges


Reconstruction type attacks


Reverse engineering collected data to obtain invasive
information


Second Hand Smoke Problem


How can the privacy of third parties be effectively
protected when other people wearing sensors are
nearby?


How can mismatched privacy policies be managed
when two different people are close enough to each
other for their sensors to collect information?


Stronger techniques for protecting people’s
privacy are needed

Conclusion


Infrastructure has been established


Technical Barrier


How to perform privacy
-
sensitive and resource
-
sensitive reasoning with dynamic data, while
providing useful and effective feedback to users?


Future


Micro and macroscopic views of individuals,
communities, and societies


Converging solutions relating to social networking,
health, and energy