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
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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
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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
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Sharing
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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
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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 resourcesensitive 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