survey of mobile phone sensing

A 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)

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

Galaxy S4 - 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

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• 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)

Sense

Learn

Inform, Share, and Persuasion

Mobile Sensing Architecture

Mobile Computing Cloud

Components

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

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

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.

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

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

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