Download - Survey of Mobile Phone Sensing
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A Survey of Mobile Phone Sensing
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Paper Info
• Published in September 2010• IEEE Communications Magazine • Dartmouth College – joint effort between
graduate students and professors (Mobile Sensing Group)
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Outline
• Current Mobile Phone Sensing– Hardware– Applications
• Sensing Scale and Paradigms• Architectural Framework for discussing
current issues and challenges
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Smartphone Technological Advances
• Cheap embedded sensors • Open and programmable• Each vendor offers an app store• Mobile computing cloud for offloading
services to backend servers
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iPhone 4 - Sensors
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Galaxy S4 - Sensors
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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)
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Application Stores• Multiple vendors– Apple AppStore– Android Market– Microsoft Mobile Marketplace
• Developers– Startups– Academia– Small Research laboratories– Individuals
• Critical mass of users
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Application Stores
• Current issues and challenges– User selection– Validation– Privacy of users– Scaling and data management
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Sensing Scale
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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
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Sensing Paradigms• Participatory: user actively engages in the data
collection activity– Example: managing garbage cans by taking photos – Advantages: supports complex operations– Challenges:
• Quality of data is dependent on participants• Opportunistic: automated sensor data collection– Example: collecting location traces from users– Advantages: lowers burden placed on the user– Challenges:
• Technically hard to build – people underutilized• Phone context problem (dynamic environments)
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Sense
Learn
Inform, Share, and Persuasion
Mobile Sensing Architecture
Mobile Computing Cloud
Components
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Sense• Programmability– Managing smartphone sensors with system APIs– Challenges: fine-grained control of sensors, portability
• Continuous sensing– Resource demanding (e.g., CPU, battery)– Energy efficient algorithms– Trade-off between accuracy and energy consumption
• Phone context– Dynamic environments affect sensor data quality– Some solutions:
• Collaborative multi-phone inference• Admission controls for removing noisy data
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Learn: Interpreting Sensor Data (Human Behavior)
• Integrating sensor data– Data mining and statistical analysis
• Learning algorithms – Supervised: data are hand-labeled (e.g., cooking,
driving)– Semi-supervised: some of the data are labeled– Unsupervised: none of the data are labeled
• Human behavior and context modeling– Activity classification– Mobility pattern analysis (place logging)– Noise mapping in urban environments
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Learn: Scaling Models• Scaling model to everyday uses – Dynamic environments; personal differences – Large scale deployment (e.g., 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:– Lack of common machine learning toolkits– Lack of large-scale public data sets– Lack of public sharing and collaboration repositories of
research stuff.
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Inform, Share, and Persuasion• Sharing– Data visualization, community awareness, and social
networks• Personalized services– Profile user preferences, recommendations, persuasion
• Persuasive technology – systems that provide tailored feedback with the goal of changing user’s behavior– Motivation to change human behavior (e.g., healthcare,
environmental awareness)– Methods: games, competitions, goal setting– Interdisciplinary research combining behavioral and social
psychology with computer science
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Privacy Issues
• Respecting the privacy of the user is the most fundamental responsibility of a mobile 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
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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
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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