Bosch Mobile Location May2007

8/11/2019 Bosch Mobile Location May2007

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Mobile and Location-Based Services

Jason I. [email protected]

May 04 2007


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The Big Picture

Mobile social computing inTouch: Coordination for Families and Small Groups

Whisper Mobile: Coordinating groups for social events

Large-scale mobile collaboration

Hitchhiking: Estimating busyness of places Mobile data

Gurungo: linking desktop and mobile devices

Usable privacy and security

Contextual Instant Messaging People Finder

Grey: Access control to resources

Memory support

Memory Karaoke


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The Big Picture









Memory support

Memory Karaoke


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inTouch: Coordination for Families

Make it easier to coordinate with others while mobile Better awareness and messaging

Target Users:

Small to med. groups of people

Fluid and demanding scheduleMultiple responsibilities

Examples:

Dual-career familiesWork groups

Ad hoc (ex. conferences)

Carpools

Mobility

AwarenessMessaging


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Dual-Career Families

Coordination breakdowns inevitable Childrens activities change without notice

Parents meetings run over

Impromptu appointments

Unexpected traffic

Result:

High levels of anxiety

Some parents fear about forgetting their children

Need support for awareness and improvisation


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Two week field study with six dual-career families


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Check, Double Check, Triple Check

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Status Confirmation Reminder Planning Schedule Reschedule Recall

Message Type

NumberofMessa

ges


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Key Transition Times

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Hour of the Day (1AM, 2AM, etc)

AvgNumberofMessag

es


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Make it easier to coordinate with others while mobile Better awareness

Contextual messaging

Combines:

Shared calendar

Shared todo lists

Reminders

Real-time location

Proximity


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Project: InTouch

Its 4:30pm and Mom is

stuck in traffic

inTouch checks her calendar

and sees shes supposed to

pick up Cindy from ballet


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Project: InTouch

Moms phone senses that she is

in a traffic jam, and automatically

prepares a status message

Mom hits send, and Cindy sees

that Mom is running late. Cindy

decides to wait inside.


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Contextual Messaging

Using current context to: Select a message template

Fill in the blanks (like a MadLib)

In most cases, can just hit send

When is contextual messaging useful?

Calendar alarms running late, will be there in

Current activity in a meeting now, done at

Daily rhythms picked up kid ok at 3PM Messages received where r u? ->I am at


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Contextual Messaging

Messaging can be linked to calendar or reminders S: Can you get dinner tonight?

J: Ok, I will pick up __________ on my way home

Activate as a reminder when you leave work

Message easy to select around 4PM

Fill in the blank based on patterns

and whats near your home


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Example Mockups

Currently developing working prototypes


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The Big Picture









Memory support

Memory Karaoke


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Whisper Mobile

Goal: Make it easy to find, share, and coordinatefriends going to social events


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Whisper Mobile: Creating an Event

Minimal text input

Use location

Use audio

Use camera


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Continuing Work

Developing working prototype of web site and mobile Web crawler for finding social events

Web site to coordinate on scale of weeks and days

Link with inTouch Coordinate friends

See whos late, where were going next

Mobile to coordinate on scale of hours and minutes
http://maguro.hcii.cs.cmu.edu/whisper/test_site/my_event_list.php


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The Big Picture









Memory support

Memory Karaoke


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Project: Hitchhiking

Most location-based services about where you are Hitchhiking is about the busyness of places

Is the caf busy?

How long are the lines at the airport?

Wheres an empty room?

Is there any parking at the shopping district?


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Estimate number of people in a place by countingthe number of wireless devices there

Periodically upload count + location to our servers

Other people can query our servers
http://images.google.com/imgres?imgurl=http://www.ixbt.com/comm/wireless/usr-5450/5450-front.jpg&imgrefurl=http://www.ixbt.com/comm/wrls-usr-5450.shtml&h=1350&w=1280&sz=54&tbnid=ALW_gkd98jkJcM:&tbnh=150&tbnw=142&hl=en&start=1&prev=/images%3Fq%3Daccess%2Bpoint%26svnum%3D10%26hl%3Den%26lr%3D%26sa%3DN


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How well does Hitchhiking work?


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Privacy? Upload anonymized counts only

Upload from approved places only

Our server shows busyness of a place only

Advantages

Cheap, uses existing devices (everyone is a sensor)

Deployable, dont have to set up lots of new sensors

Privacy

Whats next?

Map visualizations


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The Big Picture









Memory support Memory Karaoke


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GurunGo

Goal: Make it easy to access useful informationwhile mobile

Observation #1: People still tend to print out online

maps, despite having mobile device. Why? Found it via desktop, easier to print than to copy to mobile

Slow or expensive wireless connections

Inconvenient form factor on mobile device

Observation #2: People dont do the same kind of

web browsing on mobile phones as on desktops

Dont have to support all information finding tasks,

just ones more likely to be done when mobile


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GurunGo Scenarios

Idea: Tie mobile more closely with desktop

You find an interesting product while browsing

Use GurunGo to copy-and-paste to mobile

Augments with product reviews Copies to mobile

Kept until explicitly deleted

As you browse web on desktop:

GurunGo scans HTML for maps

Generates speech-based directions

Copies to mobile

Directions eventually discarded after given time


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GurunGo Usage

Acquire Let people explicitly copy-and-paste info to mobile

Let people implicitly copy info via regular web browsing

GurunGo scans pages seen for potentially useful stuff

Augment Look for known data types, make mobile data more useful

Ex. Augment maps with speech-based directions

Copy (to mobile in the background)

Browse

Organize data based on common data types

Street addresses, product comparisons, phone #s


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GurunGo: Speech-based Directions


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Nice Features of GurunGo

Reduces number of clicks to get to useful information Can support specific information finding tasks while mobile

Currently: Directions, products

Future: Movies, phone #s, dates and times, recent emails

Works even if you dont have wide-area wireless

Works disconnected (no network or dont want to pay)

Only needs personal area network (Bluetooth)


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The Big Picture











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The Problem

Mobile devices becoming integrated into everyday life

Mobile communication

Sharing location information with others

Remote access to home

Mobile e-commerce

Managing security and privacy policies is hard

Preferences hard to articulate

Policies hard to specify

Limited input and output

Leads to new sources of vulnerability and frustration


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Our Goal

Develop core set of technologies for managing

privacy and security on mobile devices

Simple UIs for specifying policies

Clear notifications and explanations of what happened

Better visualizations to summarize results

Machine learning for learning preferences

Start with small evaluations, continue with large-scale ones

Large multi-disciplinary team and project Six faculty, 1.5 postdocs, six students

Supported by NSF, CMU CyLab

Roughly 1 year into project


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The Big Picture











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Contextual Instant Messaging

Facilitate coordination and communication by letting

people request contextual information via IM

Interruptibility (via SUBTLE toolkit)

Location (via Place Lab WiFi positioning)

Active window

Developed a custom client and robot on top of AIM

Client (Trillian plugin) captures and sends context to robot

People can query imbuddy411 robot for info

howbusyis username

Robot also contains privacy rules governing disclosure


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Contextual Instant MessagingPrivacy Mechanisms

Web-based specificationof privacy preferences

Users can create groups and

put screennames into groups

Users can specify what eachgroup can see

C


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Notifications of requests

C t t l I t t M i


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Social translucency

C t t l I t t M i


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Audit logs

C t t l I t t M i


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Contextual Instant MessagingEvaluat ion

Recruited ten people for two weeks Selected people highly active in IM (ie undergrads )

Each participant had ~90 buddies and 1300 incoming

and outgoing messages per week

Notified other parties of imbuddy411 service

Update AIM profile to advertise

Would notify other parties at start of conversation

C t t l I t t M i


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Contextual Instant MessagingResults

Total of 242 requests for contextual information 53 distinct screen names, 13 repeat users

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Interruptibility Location Active Window

C t t l I t t M i


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43 privacy groups, ~4 per participant

Groups organized as class, major, clubs,

gender, work, location, ethnicity, family

6 groups revealed no information

7 groups disclosed all information

Only two instances of changes to rules

In both cases, friend asked participant to

increase level of disclosure

C t t l I t t M i


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Likert scale survey at end 1 is strongly disagree, 5 is strongly agree

All participants agreed contextual information sensitive

Interruptibility 3.6, location 4.1, window 4.9

Participants were comfortable using our controls (4.1) Easy to understand (4.4) and modify (4.2)

Good sense of who had seen what (3.9)

Participants also suggested improvements

Notification of offline requests

Better notifications to reduce interruptions (abnormal use)

Better summaries (User x asked for location 5 times today)

C t t l I t t M i


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Contextual Instant MessagingCurrent Status

Preparing for another round of deployment Larger group of people

A few more kinds of contextual information

Developing privacy controls that scale better More people, more kinds of information


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The Big Picture











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People Finder

Location useful for micro-coordination

Meeting up

Okayness checking

Developed phone-based client

GSM localization (Intel)

Conducted studies to see how

people specify rules (& how well) See how well machine learning

can learn preferences

People Finder


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People FinderMach ine Learning

Using case-based reasoning (CBR) My colleagues can only see my location on

weekdays and only between 8am and 6pm

Its now 6:15pm, so the CBR might allow, or interactively ask

Chose CBR over other machine learning

Better dialogs with users (ie more understandable)

Can be done as you go (rather than accumulating

large corpus and doing post-hoc)

People Finder


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People FinderCurrent Wo rk

Small-scale deployment of phone-basedPeople Finder with a group of friends

Still needs more value, people finder by itself not sufficient

Trying to understand pain points on next iteration

Need more accurate location

GSM localization accuracy haphazard

Integration with imbuddy411 Smart phones expensive, IM vastly increases user base


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The Big Picture











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GreyAccess Control to Resources

Distributed smartphone-basedaccess control system

physical resources like office doors,

computers, and coke machines

electronic ones like computer accountsand electronic files

currently only physical doors

Proofs assembled from credentials No central access control list

End-users can create flexible policies

Grey


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GreyCreating Pol ic ies

Proactive policies Manually create a policy beforehand

Alice can always enter my office

Reactive policies Create a policy based on a request

Can I get into your office?

Grey sees who is responsible for resource, and forwards

Might select from multiple people (owner, secretary, etc) Can add the user, add time limits too

Grey


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GreyDeployment at CMU

25 participants (9 part of the Grey team) Floor plan with Grey-enabled Bluetooth doors

Grey


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GreyEvaluat ion

Monitored Grey usage over several months Interviews with each participant every 4-8 weeks

Time on task in using a shared kitchen door

Grey


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GreySurpr ises

Grey policies did not mirror physical keys Grey more flexible and easier to change

Lots of non-research obstacles

user perception that the system was slow system failures causing users to get locked out

need network effects to study some interesting issues

Security is about unauthorized users out, our usersmore concerned with how easy for them to get in

never mentioned security concerns when interviewed

Grey


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GreyCurrent wo rk in Visualizat ions


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The Big Picture

Mobile social computing inTouch: Coordination for Families and Small Groups Whisper Mobile: Coordinating groups for social events

Large-scale mobile collaboration Hitchhiking: Estimating busyness of places

Mobile data Gurungo: linking desktop and mobile devices

Usable privacy and security Contextual Instant Messaging

People Finder




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Memory Karaoke

Phone-based system for preventing cognitive decline Take pictures with camera phone

Tag with location, time

Tell stories about them


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Summary

Mobile social computing Large-scale mobile

collaboration

Mobile data


Memory support

Jason I. Hong

[email protected]

NSF DARPA

Microsoft SenseMap

Motorola

Nokia


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Lots of Large-Scale Mobile Apps

Gawker Stalker


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Lots of Large-Scale Mobile Apps

One-way Matchmaking

Grey


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GreyResu lts of Time on Task of a Shared Kitchen Doo r

Grey


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Grey


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Documents

Bosch Mobile Location May2007