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

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