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A Survey of Mobile Phone Sensing

Survey of Mobile Phone Sensing

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Survey of Mobile Phone Sensing

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Page 1: Survey of Mobile Phone Sensing

A Survey of Mobile Phone Sensing

Page 2: Survey of Mobile Phone Sensing

Paper Info

• Published in September 2010• IEEE Communications Magazine • Dartmouth College – joint effort between

graduate students and professors (Mobile Sensing Group)

Page 3: Survey of Mobile Phone Sensing

Outline

• Current Mobile Phone Sensing– Hardware– Applications

• Sensing Scale and Paradigms• Architectural Framework for discussing

current issues and challenges

Page 4: Survey of Mobile Phone Sensing

Smartphone Technological Advances

• Cheap embedded sensors • Open and programmable• Each vendor offers an app store• Mobile computing cloud for offloading

services to backend servers

Page 5: Survey of Mobile Phone Sensing

iPhone 4 - Sensors

Page 6: Survey of Mobile Phone Sensing

Galaxy S4 - Sensors

Page 7: Survey of Mobile Phone Sensing

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)

Page 8: Survey of Mobile Phone Sensing

Application Stores• Multiple vendors– Apple AppStore– Android Market– Microsoft Mobile Marketplace

• Developers– Startups– Academia– Small Research laboratories– Individuals

• Critical mass of users

Page 9: Survey of Mobile Phone Sensing

Application Stores

• Current issues and challenges– User selection– Validation– Privacy of users– Scaling and data management

Page 10: Survey of Mobile Phone Sensing

Sensing Scale

Page 11: Survey of Mobile Phone Sensing

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

Page 12: Survey of Mobile Phone Sensing

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)

Page 13: Survey of Mobile Phone Sensing

Sense

Learn

Inform, Share, and Persuasion

Mobile Sensing Architecture

Mobile Computing Cloud

Components

Page 14: Survey of Mobile Phone Sensing

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

Page 15: Survey of Mobile Phone Sensing

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

Page 16: Survey of Mobile Phone Sensing

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.

Page 17: Survey of Mobile Phone Sensing

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

Page 18: Survey of Mobile Phone Sensing

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

Page 19: Survey of Mobile Phone Sensing

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

Page 20: Survey of Mobile Phone Sensing

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