WebCall - A Rich Context Mobile Research Platform

Zhigang Liu, Hawk Yin Pang, Jun Yang,Guang Yang, Peter Boda

(Special thanks to August Joki)

Nokia Research Center

10/27/09

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Outline

• Introduction

• System Architecture

• Features

· Viral Distribution

· Context Sharing

· Localization (Outdoor/Indoor)

· Human-Powered Q&A

· Privacy Control

• Lessons Learned

• Future Work

Introduction

• “Social network” is quite a buzzwordthese days.

• … but, one of the largest and mostimportant social networks has long been inyour pocket - i.e. the phone book or contacts.

• Our goal - further socialize the phone book

· Utilize sensing capabilities on modern mobile phones to share context (beyond content upload/download and status update).

· Enable easy R&D prototyping of mobile social apps for other researchers on a common platform and framework.

WebCall Architecture

• Name of “WebCall” came from pastwork but has since evolved.

• Client-server architecture on Symbian

• Client

· Nokia Web Run-Time (WRT):WebKit-based widget system

· HTTP server in PyS60 to serve local resources

• Server

· Apache HTTP server

· MySQL database

· Django framework in Python to implement service logic

· All logic is implemented on server; client needs no update when new features are added.

Browser/widget (UI & Logic)

Native modules (local resources)

HTTP (Apache)

Service Logic (Django)

Database (MySQL)

WebCall Server WebCall Client

3rd Party Services

People - Places - Stories

04/21/23 © 2008 Nokia / PPB5

STORIESPLACESPEOPLE

[flickr use Savannah Grandfather]

Viral Distribution - Invitation

• Many social networks fail to grow to the “tipping point”.

• WebCall provides an invitation mechanism right in the contacts.

· Inviter sends invitation via SMS with embedded registration link.

· Invitee clicks on link to register and download WebCall app.

· WebCall server keeps track of inviter/invitee relations.

• A crucial way to bootstrap service

Viral Distribution - Registration

• Mobile-optimized registration process

· New user follows link in invitation SMS to registration page.

· Some fields are pre-populated for quick registration.

· Links are provided for easy verification and download.

Context Sharing

• WebCall users may share their contextual information with friends.

• Extensible - new information can be added easily.

• What we have so far

· Location

· Picture

· Business Card

· Calendar

· Weather (3rd party plug-in)

· Activity (3rd party)

· PEIR - Personal EnvironmentalImpact Report (3rd party)

· Advertising (3rd party)

Localization - Indoor

• Power-controllable Bluetooth tags are deployed throughout our office.

· Tag locations are stored in DB.

· Power is controlled to achieve room-level accuracy.

· This was the quickest way for us to experiment.

· Bluetooth LP is expected to come soon.

Localization - Outdoor

• Cell-ID-based localization

· GPS is too power hungry.

· We only need coarse resolution.

• Several open databases are used.

· Open Cell ID

· ZoneTag

· GeoNames (for reverse geo-coding)

• Coarse outdoor location can be used forweather/time-zone mashups, etc.

Human-Powered Q&A

• User may post simple questions to WebCall community.

• Questions are routed to real people who may have the best answers.

· Match personal history and expertise with questions

• Multiple-choice questions make it easy to use on mobile phones.

Privacy Control

• Privacy level of each feature may be individually set.

• Groups may be defined to allow flexibility.

Lessons Learned

• Deployment of indoor localization is challenging

· Bluetooth range (i.e. transmission power) is difficult to fine tune

• Flexible/extensible database design is crucial.

· We face requests of new features throughout development.

· Database schema in constant evolvement.

• Mobile widget systems are still in infancy.

· Nokia WRT 1.0 lacks access to local resources.

· Latest WRT adds certain access but is still limited.

• User study should start early.

Future Work

• User study

• Research trial and scalability study

• Machine learning for better Human Q&A

• User behavior prediction

Video Demo

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