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A Survey of Mobile Phone Sensing

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

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A Survey of Mobile Phone Sensing

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  1. A Survey of Mobile Phone Sensing Michael Ruffing CS 495

  2. Paper Info • Published in September 2010 • Dartmouth College – joint effort between graduate students and professors (Mobile Sensing Group)

  3. Outline • Current Mobile Phone Sensing • Hardware • Applications • Sensing Scale and Paradigms • Architectural Framework for discussing current issues and challenges

  4. Smartphone Technological Advances • Cheap embedded sensors • Open and programmable • Each vendor offers an app store • Mobile computing cloud for offloading services to backend servers

  5. iPhone 4 - Sensors

  6. Future Sensors • Barometer • Temperature • Humidity • To early to tell – cost and form factor will drive the availability of new sensors

  7. 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)

  8. Application Stores • Multiple vendors • Apple AppStore • Android Market • Microsoft Mobile Marketplace • Developers • Startups • Academia • Small Research laboratories • Individuals • Critical mass of users

  9. Application Stores • Current issues and challenges • User selection • Validation • Privacy of users • Scaling and data management

  10. Sensing Scale

  11. 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

  12. 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

  13. Mobile Phone Sensing Architecture • Goal – architectural model for discussion • Components • Sense • Learn • Inform, Share, Persuasion

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

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