Transcript A Comparison of Alternative Client/Server Architectures

Gary M. Weiss and Jeffrey Lockhart
Fordham University, New York, NY
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Mobile sensors becoming ubiquitous
 Especially via smartphones
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Various architectures are possible ranging
from “smart client” to “dumb client”
 Each architecture has pros and cons
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Worthwhile to enumerate and compare
alternative architectures
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1. Sensor Collection
2. Data Processing and Transformation
3. Decision Analysis/Model Application
4. Data and Knowledge Reporting
Learning/model generation
Only step 1 is required
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Main focus of WISDM lab
Monitors smartphone accelerometer and
uses the data to perform activity recognition
 Activities: walk, jog, stairs, sit, stand, lie down
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Results available via the Web
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Sensor Collection:
 Actitracker client collects raw accelerometer data for
3 axes 20 times per second and transmits to server
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Data Processing and Transformation
 Every 10 sec. server aggregates raw samples into a
single example described by several dozen features
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Decision Analysis/Model Application
 Server applies predictive model to examples; activity
classified and saved to database
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Data and Knowledge Reporting
 User queries server DB any time via web interface
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Client Configurations
Responsibility
CC-1
Dumb
CC-2
CC-3
CC-4
Smart
1
Sensor Collection
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2
Data Transformation
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3
Model Application
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4
Reporting
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Model Generation
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Mobile devices have CPU power to build models
 Only makes sense to build a model on the client device if
will apply it on the client
 Thus model construction on device only for CC-3 or CC-4
 In CC-1 and CC-2 either model hardcoded into client or
downloaded from server
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Data mining not always required
 Can be done dynamically (on client or server) or statically
 Our research shows dynamically generated personal
models outperform general (impersonal) models1
1 Gary M. Weiss and Jeffrey W. Lockhart. The Impact of Personalization on Smartphone-Based Activity Recognition,
Papers from the AAAI-12 Workshop on Activity Context Representation: Techniques and Languages, AAAI Technical
Report WS-12-05, Toronto, Canada, 98-104.
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Resource usage
 battery, CPU, memory, transmission bandwidth
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Scalability
 Support for many mobile devices
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Access to data
 Researchers and others may want raw data
 Transformed data loses information
▪ With raw data can alter features for data mining and
regenerate results
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Privacy/Security
 Users will want to keep data secure and/or private
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User Interface
 Users want aesthetics (screen size) & accessibility
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Crowdsourcing
 Some applications will require a central server in
order to aggregate data from multiple users/devices
▪ Navigation software that tracks traffic
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Resource Usage
 Unclear. Resource usage minimized except heaviest
use of transmission bandwidth (power drain)
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Scalability
 Poor since maximizes server work
 Actitracker’s server can handle 942 simult. users
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Access to Data
 Best since all raw data can be preserved on server
▪ But Actitracker requires 791 MB/month per user.
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Privacy/Security:
 Poor: The more data sent the greater the risk
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User Interface:
 Good: data and results on server and can be viewed
over Internet
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Crowdsourcing
 Best: All data available on server
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Similar to CC-1 except:
 Less data to transmit so bandwidth/energy savings
▪ For Actitracker 95% reduction in data
▪ But more processing which takes up CPU and power
 More scalable (less server work)
 Less access to data (raw data not available)
 Slight improvement in privacy/security (no raw data)
 Minimal impact on user interface (results still on server)
 Crowdsourcing only on aggregated data
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Resource usage:
 more processing on the client (more CPU and power);
but only need to transmit results
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Much more scalable: server only collects results
Access to data: only results available
Much improved security/privacy
 results may not be nearly as sensitive
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Can still view results via web-based interface
Can only crowdsource on results
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About same as CC-3
 not sending results saves little power
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Perfectly scalable: no server
No access to data
Good security/privacy: nothing leaves device
Can only view results on the device
 Not accessible from other places and small screens
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Cannot even crowdsource results
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Resource usage:
Scalability:
Access to data:
Security/Privacy:
User Interface:
Centralized Data:
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One approach: support multiple architectures
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unclear
smart client best
dumb client best
smart client best
smart client worst
dumb client best
 approach taken by our research group
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Go to wisdmproject.com
Actitracker should be ready for beta in 1 month
 Actitracker.com
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Papers available from:
 http://www.cis.fordham.edu/wisdm/publications.php
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My contact info:
 [email protected]
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