location and context awareness
DESCRIPTION
Location and Context Awareness. Dan Siewiorek June 2012. Outline. Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges. Outline. Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges. - PowerPoint PPT PresentationTRANSCRIPT
11© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Location and Context Location and Context AwarenessAwareness
Dan SiewiorekDan Siewiorek
June 2012June 2012
22© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Outline
Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges
33© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Outline
Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges
44© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
710
1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996
310
410
510
610
Year Samples Introduced
4004 8080
6800
8086
80286
80386
80860 80486
PENT.
6802
6801
68020
68030SLOPE = 10X INCREASE
IN 7 YEARS
68040
PENT. PRO
Num
ber
of
devi
ces
(Source: Walt Davis, Motorola)
Transistors per Processor
Moore’s Law Reigns Supreme
55© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Disk Capacity
Moore’s Law Reigns Supreme
66© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Cost per Megabyte
Moore’s Law Reigns Supreme
77© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Glaring Exception
Adam & Eve 2000 AD
Human Attention
88© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Distraction Matrix
99© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Outline
Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges
1010© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Context Aware Computing
Applications that use context to provide task-relevant information and/or services
Context is any information that can be used to characterize the situation of an entity (person, place, or physical or computational object)
Contextual sensing, adaptation, resource discovery, and augmentation
1111© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Context Aware Service Examples
Primary Services» Location
» Biological measures (heart rate, breathing)
» Position of limbs
Derived Services» Difficulty in performing activity
» Amount of activity for elderly
» “Is Bob coming to the meeting”» Match Making (location, activity, skill level)
1212© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Outline
Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges
1313© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Location Sensing Parameters
Physical (x,y,z) versus Symbolic (room) Absolute (shared reference grid) vs Relative Localized Location Computation - where calc Object Recognition Accuracy, Precision
» Distance, Distribution Scale
» Number of objects per unit infrastructure per time interval
Cost
1414© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Fluctuating signals for a single-location over time in the Wean
Linux Cluster
1515© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Combined data-points from Linux and Windows Cluster in Wean Hall
1616© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Location Sensing Approaches
Triangulation» Lateration – multiple distance measurements
between known points» Angulation – angle or bearing relative to points
with know separation Proximity
» Nearness to known set of points Scene Analysis
» Identify relationship to know points
1717© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Triangulation Technologies
Technology Resolution Example
InfraRed (IR) Room Active Badges
UltraSound cm Bats, Cricket
Visual Light cm Easy Living
Wireless LAN m RADAR, Wireless Andrew
1818© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Location Service Architecture Comparison
Four main types of location architectures (Centralized Push, Centralized Pull, Distributed Push, Distributed Pull)
Location aware messaging as a representative application
Quantified data flow requirements and messages for location based application
Centralized pull model performs better than distributed for location aware messaging
1919© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Location Service Architecture Alternatives
2020© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Location Service Architecture Data
Rates
Location to Server Server to App Client to Client Total Data Rate
Centralized-Pull 0.17 Mbps 2.56 Mbps N/A 2.73 Mbps
Centralized-Push (Time) 0.78 Mbps 2.56 Mbps N/A 3.34 Mbps
Centralized-Push (Event) 0.01 Mbps 2.56 Mbps N/A 2.57 Mbps
Distributed-Pull N/A N/A 93.80 Mbps 93.80 Mbps
Distributed-Push (Time) N/A N/A 14.81 Mbps 14.81Mbps
Distributed-Push (Event) N/A N/A 3.47 Mbps 3.47 Mbps
Comparison of Architectures using Instant Messaging Application Polling/Push Frequency = 1 min ~ 3,000 wireless clients
48 Buddies per User
2121© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Systems Issues (Bats) Aesthetics Distribution of sensors by space usage Physical/symbolic boundaries
» Overlap False negatives
» Not wearing Quiet Zone
» Not tracked Cycle: user participation decreases
application degrades reduced incentive to participate
2222© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Outline
Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges
2323© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Activity Recognition Through Machine
Learning
.
2424© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Model Generation Variables
Window Sizes» 4, 6 Seconds
Features Extracted Model
» k nearest neighbors
» clustering
» Support Vector Machine (SVM)
‘Leave-one-out’ cross validation for optimization and testing
Features Value
mean 10.04361
stdev 0.05040
variance 0.00254
rms 10.04373
cumulative sum 954.14258
mean absolute deviation 0.04104
mean crossing rate 64
2525© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Feature Space After Linear Discriminant Analysis (LDA)
Transformation
2626© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Feature Subsets and Classification for Body
Positions
# Features CPU Cycles Time in μs Classification Accuracy for Body Position
wrist pocket bag necklace shirt belt
F1 All features, all sensors 56242 953.6 87.1% 85.2% 92.8% 86.8% 89.5% 87.0%
F2 All features from accelerometer X 14731 249.8 68.4% 78.6% 85.2% 74.3% 76.1% 72.6%
F3 All features from accelerometer Y 14731 249.8 83.2% 75.4% 86.4% 61.3% 70.1% 70.8%
F4 All features from light 14731 249.8 55.0% 16.7% 18.0% 61.7% 52.2% 48.8%
F5 All features from accelerometer XY (x2 + y2 ) 15049 255.2 76.6% 79.5% 87.2% 72.6% 78.0% 77.2%
F6 prcy(3), rmsxy, prcy (20), prcy (97), rmslight, madx, meany, prcy (10)
12114 205.4 87.0% 80.1% 86.5% 78.6% 79.6% 84.2%
F8 prcy (3), iqry, prcy (10), prcy (97), madx 7651 129.7 82.0% 62.4% 68.9% 56.6% 69.8% 71.7%
F9 rmsxy, qrtx, rmsx, madxy, meanxy 10746 182.2 77.3% 78.2% 80.9% 72.3% 75.4% 76.5%
2727© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Activity Recognition Accuracy at Body
Locations
2828© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Accuracy Classification Recorded Over 100
Minutes
2929© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Audio and Light Sensor Clustering
3030© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
New Sensor Types Increase Range of Activity Recognition
Physi
olo
gic
al Senso
rs
Accu
racy
3131© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Cell Phone Activity Recognition Deployment
3232© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Data Quality
In a real-time wireless environment data centric systems are plagued with lost packets and corrupt data.
A dynamic sensor architecture can mitigate these problems.
3333© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Handling Multiple Sensors
Centralized Architecture
Low Bandwidth Architecture
Dynamic Architecture
Raw Sensor Data
Feature Extraction
Classifier
Decision
Master Device
Sensor Device
Master Device
Sensor Device
Master Device
Sensor Device
Aggregation of N sensors takes place between the two devices.
3434© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Centralized Architecture
Classifier is static.» Requires knowledge of
sensors devices available (N).
Bandwidth utilization will be higher (~500 B/s per sensor) sending all raw data.
Raw Data Aggregator must deal with memory intensive data sets (~1-10 MB)
Raw Sensor DataIndicates wireless link
~500 B/s
Feature Extraction
Classifier
Decision
Raw Data Aggregator
Sensor Device 1
Master Device
~1-10 MB/window
~1 KB/window
~4 B/window
Raw Sensor DataSensor Device N
…
~500 B/s
3535© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Coping with Data loss in the Centralized
Architecture Process 1:
» Buffer data on master device with the Raw Data aggregator.
» Forward incomplete windows at least 2/3 full to Feature Extraction.
» Ex: Forward a set of 40 data points when 60 are expected.
Indicates wireless link…
Raw Sensor Data
Feature Extraction
Classifier
Decision
Raw Data Aggregator
Sensor Device 1
Master Device
~500 B/s
~1-10 MB/window
~ 1KB/window
~4 B/window
Raw Sensor Data
Sensor Device N
~500 B/s
3636© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
GSR Temp ECG Respiration0
100
200
300
400
500
600
700
800
900
Data Windows Collected per Sensor Type
Subject08Subject14Subject16Subject20
Sensor Type
Num
be
r o
f Da
ta W
ind
ow
s C
olle
cte
d
Coping with Data loss in the Traditional Architecture
Ex: With subject 20 a maximum of 604 windows could be classified on.
Process 2: Classify only on windows in which all sensors have an output.
3737© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Low Bandwidth Architecture
Lower wireless bandwidth compared to centralized architecture
Indicates wireless link
~200 B/window
Sensor Device N
Raw Sensor Data
Feature Extraction
Classifier
Decision
FeatureAggregator
Sensor Device 1
Master Device
~1KB/window
~4 B/window
…Raw Sensor Data
Feature Extraction
~200 B/window
~500 B/sec~500 B/sec
3838© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Dynamic Architecture Fuse locally
made classifications from multiple sensors.
» N is dynamic.
Confidence information, the probability of each context, is transmitted to Fuser. (~80 B/window)
Sensor Device N
Raw Sensor Data
Decision
SensorFuser
Sensor Device 1
Master Device
~4 B/window
…
Feature Extraction
Classifier
Raw Sensor Data
Feature Extraction
Classifier
~80 B/window~80 B/window
~200 B/window ~200 B/window
~500 B/sec ~500 B/sec
Indicates wireless link
3939© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Comparison to Centralized Architecture
Utilizes dynamic sensor set» Increased Accuracy
» No static classifier in a central location.
Utilizes heterogeneous algorithms» Best techniques can be used on a per device
basis to address:– Power constraints
– Computation constraints
4040© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
ErgoBuddy – Experimental Setup
11 Subjects» Approximately 22 hours of
data total.
7 Sensor Body Locations» Ankle, Arm, Back, Handheld,
Holster, Lanyard, Wrist
10 Activities» Sitting, Standing, Lifting,
Walking, Running, Carrying, Sweeping/Mopping, Stairs, Laddering, Carting
4141© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Experimental Issues
7 Wearable Sensors for activity recognition communicating over Bluetooth.
Approximately 10% packet loss per sensor with current implementation.
» Low Bandwidth Architecture Reliability ~48%– 1 packet lost = missed classification
– Probability one of seven sensors is down @ 90% reliability:
– 0.9 ^ 7 = 0.48
» Dynamic Architecture Reliability ~99.99%– 1 packet lost = continue with N-1 other sensors.
– Probability all seven sensors are down @ 90% reliability:
– 1-(.1 ^ 7) = .999999
4242© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Model Generation 2 Final Window Sizes
» 4, 6 Seconds
7 Models» 1 model for each body
location
5 Fusion techniques 0%-90% simulated packet
loss environments ‘Leave-one-out’ cross
validation for optimization and testing
Features Value
mean 10.04361
stdev 0.05040
variance 0.00254
rms 10.04373
cumulative sum 954.14258
mean absolute deviation 0.04104
mean crossing rate 64
4343© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Fuser Technique – Lossless Environment
Fuser Method Accuracy
Average of Probability 88.92%
Product of Probability 59.09%
Straight Vote 83.66%
Max Probability 79.34%
Rank 87.89%Raw Sensor Data
Decision
SensorFuser
Sensor Device 1
Master Device
…
Feature Extraction
Classifier
Raw Sensor Data
Sensor Device N
Feature Extraction
Classifier
4444© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Resiliance of Static vs Dynamic Classifiers in Lossy Environments
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Packet Loss Rate
Cla
ssifi
catio
n A
ccu
racy 7 Sensors Static
7 Sensors Dynamic
6 Sensors Dynamic
5 Sensors Dynamic
4 Sensors Dynamic
3 Sensors Dynamic
2 Sensors Dynamic
1 Sensor Dynamic
Performance Results
With same number of sensors in a lossless environment fusion yields results 2% worse than a model with access to all sensor’s raw data.
4545© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Conclusions
In all lossy environments, 10%+, there was better performance using fusion.
» 35% accuracy increase in an environment with 50% packet loss.
The number of sensors can be reduced in low loss environments for power and bandwidth savings.
» For our experiment 3 sensors was ideal.
This technique can also be applied to systems of heterogeneous sensors.
4646© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Outline
Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges
4747© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Context Sensing
Basic context» Location
» Orientation
» Audio samples from the user’s environment
» Static data
» History of user context
Multiple sensors can be used to infer user’s intent» Wireless Network Card, Digital Compass,
Thermometer, Camera
4848© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Example Applications
Notification » Alert a user if they are passing within a certain distance of a task on
their to do list.» SenSay Context Aware Cell Phone
Meeting Reminder » Alerts a user if they are in danger of missing a meeting.
Activity Recommendation » Recommends possible activities/meetings that a user might like to
attend based on their interests.
Proactive Assistant» Answering questions about user’s intent» Proactively preparing user’s workspace based on usage patterns and
behavior
Matchmaking» Locating an entity based upon expertise, skills, proximity and/or
availability
4949© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Distraction Matrix for Portable Help Desk
5050© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Technology
Location Service» Use multiple sources to calculate location (e.g.
wireless access point triangulation, ceiling photo match)
» Give applications simple form
Transcoders» Translate data to form useful on device
Manage Network Disconnects Persistent Proxies for Devices, Users
» Allow policies to be set
» Remove burden from individual applications
5151© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Handy Andy Architecture
InfrastructureLogin/Logout
Waldo PhD Idealink Stalker
User Proxy
Device Proxy
Itsy Jornada Other Devices
SpeechEncode/Decode
Database
Service
Infrastructure
Device
Device Proxy Device Proxy
5252© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
PhD Features and Interaction
User’s List:• Items can be added,moved, and removed• Only “checked” items appear on the map
Description:• Information on the currently selected item • Dynamic information automatically updated
Map:• Dynamic information automatically updated
Map Controls: Zoom & Pan
5353© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
PhD (Personal help Desk)
5454© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
PhD Control of Preferences
5555© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Virtual/Real Synchronously Sharing Information
Real Sites Virtual Sites Example
One One Training
Many One Virtual Control Room
One Many Collaboration
Many Many Command and Control
5656© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Virtuspherehttp://www.virtusphere.com
10-foot hollow sphere that rotates freely in any direction according to the user’s steps.
Wireless, head-mounted display allows user to walk and run being immersed into virtual environment.
User movement replicated in the virtual environment.
5757© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
System Help
CMU SCS Computing Facilities DB Matchmaking : Expert to Problem Facilities people have certain expertise Users report problems Performs Matchmaking Assigns expert to the problem Gets reply/confirmation from expert
5858© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
MatchMaking (MM)
Obtain a list of most relevant experts for this problem
Find out which of these experts are available and if available, after how much time (mean and variance)
Find time to reach location of the problem Using Rules, choose the best expert
» time (mean, var), expert busy?, expert’s score
5959© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Contexts Used
Location of the expert Location of the problem Expert profiles -skills Expert availability, current involvement (busy) Time spent on the problem so far History of maintenance (problems experts) Other simultaneous problems Time of the day
6060© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
0.01
0.1
1
10
100
0 250 500 750 1000 1250 1500 1750 2000
Number of Client Apps.
Wa
it T
ime
in Q
ue
ue
(s
ec
.)
0
20
40
60
80
100
120
140
160
180
12:00 AM 4:00 AM 8:00 AM 12:00 PM 4:00 PM 8:00 PM 12:00 AM
Time of Day
Infe
cted
Wir
eles
s U
sers
U1
U2
U3
U4
G1
G2
F1
F2
S1
S2
6161© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Major Components
6262© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Locator@CMU
Implemented on Wireless Andrew» ~1,000 APs» ~5,000 peak concurrent users
Centralized-Pull architecture using relational database
Provides omniscient view of network usage
6363© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Mobility on Wireless Network
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
1 2 3 4 5 6 7 8 9 10+
Mobility (Access Points per Day)
Per
cen
t of
Use
rs
2003 2005 3.012.012.812005
2.752.182.852003
Standard Deviation
MedianMobility
AverageMobility
3.012.012.812005
2.752.182.852003
Standard Deviation
MedianMobility
AverageMobility
6464© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Location-Based Applications
Where are users now and where have they been (past/present)» Contact/Spread Tracking
Where were users (past) » Unknowing Bystander Service
Where will users be (future)» Crowd Predictor
6565© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Applications Contact/Spread Tracking
Scenario: Someone is ‘infected’ how many people do they spread the disease too in various situations?
Divided wireless users into infecting agents and general users.
Selected 10 infecting agents (4 undergrads, 2 grads, 2 faculty, and 2 staff)
6666© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Contact/Spread Tracking
Direct-Primary
0
20
40
60
80
100
120
140
160
180
12:00 AM 4:00 AM 8:00 AM 12:00 PM 4:00 PM 8:00 PM 12:00 AM
Time of Day
Infe
cted
Wir
eles
s U
sers
U1
U2
U3
U4
G1
G2
F1
F2
S1
S2
6767© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Applications Unknowing Bystander
Scenario….. An event happens at location X. People nearby might not even be aware but can have valuable information.
Event Examples:» Crime (Burglary/Theft/Murder, etc.)» Lost item, pet, or person
Possible Users» Police, Homeland Security (Citizen Watch Corps)» Individuals
Used campus crime data to determine how many network users near area and could be potential witnesses
What percent of time would there be a potential witnesses?
6868© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Unknowing BystanderResults
15 of the 16 crimes had potential witnesses Average value of 12.8 for potential witnesses Median value of 4.5 for potential witnesses Chance of at least 1 witness for 4.5 witnesses with
likelihood of 5% (21%); 10% (38%); 33% (83%)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Number of Wireless Clients
Pro
b. o
f a
t L
ea
st
1 W
itn
es
s
0%
1%
2.50%5%
10%
15%
25%
33%
40%50%
Likelihood of Wireless User Being a Witness
6969© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
ApplicationsCrowd Predictor
Use information from historical data to populate an application to predict future crowds at a location (Neural Network)
Can be used by organizations to find best spot to setup table
Allow for other limited criteria (such as type of space, time of day, day of week)
7070© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Applications Crowd Predictor - Metrics
Select 12 different Access Points » 3 in Classroom Areas» 3 in Office Areas» 3 in Public Areas» 3 in Dorm Areas
Predict wireless crowds at 12 test Access Points at 5 different day/time combinations and compare to observed results
Look at effect of time of day, day, and type of area predicting
7171© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Applications Crowd Predictor
Mudge 211
0.00
2.00
4.00
6.00
8.00
10.00
12.00
11/8/200511:00AM
11/9/20052:20PM
11/10/200510:40PM
11/11/20054:30PM
11/12/20051:45PM
Wir
eles
s U
sers
Predicted
Actual 53.3%
46.7%
59.7%
40.3%
58.6%
41.4%
62.6%
37.4%
0%10%20%30%40%50%60%70%80%90%
100%
Inp
ut
Fa
cto
r Im
po
rta
nc
e
Office Public Classroom Dorm
Area Type
Time of Day Importance Day of Week Importance
7272© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Outline
Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges
7373© 2010-2011-2012 Daniel P. Siewiorek
Mobile Computing
Context Aware Computing Research Challenges
When and How to Interrupt Privacy of Data
» What information is collected
» Who can access information
» How long information is stored
How User Specifies Preferences on Data Availability
User Attention» Charge Market Value to those demanding attention
» Combine theories from social science, cognitive science, and economics