普及運算報告
Ubiquitous System Software
Managing Uncertainty:Modeling
Users in Location-Tracking
Applications
Present：研一 張永昌
Chang Yung-Chang
Outline
Ubiquitous System Software
Managing Uncertainty:Modeling Users in
Location-Tracking Applications
普及運算報告
Ubiquitous System Software
Present：研一 張永昌
Chang Yung-Chang
Outline
 Introduction
 THE MOST SENSED CAMPUS
 MONITORING EARTHQUAKEINDUCED
LOADING WITH CAMERA NETWORKS
 MIN: MIDDLEWARE FOR NETWORKCENTRIC UBIQUITOUS SYSTEMS
 DESIGNING UBIQUITOUS SYSTEMS
THROUGH ARCHITECTURAL REFLECTION
 INTERACTION METAPHORS
 APPLICATION MODELING FOR CONTEXT
AWARENESS
Introduction
 THE MOST SENSED CAMPUS
 MONITORING EARTHQUAKEINDUCED
LOADING WITH CAMERA NETWORKS
 MIN: MIDDLEWARE FOR NETWORKCENTRIC UBIQUITOUS SYSTEMS
 DESIGNING UBIQUITOUS SYSTEMS
THROUGH ARCHITECTURAL REFLECTION
 INTERACTION METAPHORS
 APPLICATION MODELING FOR CONTEXT
AWARENESS
THE MOST SENSED CAMPUS
THE MOST SENSED CAMPUS
Michael W. Bigrigg and H. Scott
Matthews, Carnegie Mellon University
most wired→most wireless→most sensed
MONITORING EARTHQUAKEINDUCED
LOADING WITH CAMERA NETWORKS
MONITORING EARTHQUAKEINDUCED
LOADING WITH CAMERA NETWORKS
Tara C. Hutchinson and Falko Kuester,
University of California, Irvine
MONITORING EARTHQUAKEINDUCED
LOADING WITH CAMERA NETWORKS
The investigation has two primary
objectives：
Characterize the seismic response of an
important class of equipment and building
contents
Study the applicability of tracking this response
using arrays of image-based monitoring
systems
MONITORING EARTHQUAKEINDUCED
LOADING WITH CAMERA NETWORKS
 Exploits several issues in designing networked
sensing systems for field applications:
Viability of high-speed networks of sensors under
adverse conditions (in this case, earthquake loads)
Communication with a variety of different sensor types
Interpretation capacity of the sensed information (by a
remote user)
Network latency and failure tolerance under highdemand conditions (high rates of acquisition, through
adverse conditions)
MIN: MIDDLEWARE FOR NETWORKCENTRIC UBIQUITOUS SYSTEMS
Lu Yan, Turku Centre for Computer
Science and Åbo Akademi University
MIN=Formal Methods in Peer-to-Peer
Network
MIN: MIDDLEWARE FOR NETWORKCENTRIC UBIQUITOUS SYSTEMS
systems require
A self-organizing infrastructure
Dynamic topology
A hop connection
Decentralized service
Integrated routing
Context awareness
DESIGNING UBIQUITOUS SYSTEMS
THROUGH ARCHITECTURAL
REFLECTION
DESIGNING UBIQUITOUS SYSTEMS
THROUGH ARCHITECTURAL
REFLECTION
Francesca Arcelli, Claudia Raibulet,
Francesco Tisato, and Marzia Adorni,
Università degli Studi di Milano-Bicocca
DESIGNING UBIQUITOUS SYSTEMS
THROUGH ARCHITECTURAL
REFLECTION
 Several relevant features：
complex multimedia
multichannel
mobile distributed systems
 features：
context awareness
Location awareness
self adaptation
service orientation
quality-of-service support
awareness
negotiation capability(to solve conflict resolution)
DESIGNING UBIQUITOUS SYSTEMS
THROUGH ARCHITECTURAL
REFLECTION
We’ve designed a reflective architecture
for multichannel adaptive information
systems (the MAIS project).
INTERACTION METAPHORS
INTERACTION METAPHORS
Christoph Endres, German Research
Center for Artificial Intelligence Andreas
Butz, Munich University, Germany
INTERACTION METAPHORS
The FLUIDUM project =Flexible User
Interfaces for Distributed Ubiquitous
Machinery
WIMP=Windows、Icon、Menus、
Pointing devices
FLUIDUM addresses instrumented
environments at three different scales—
the desk, room, and building levels。
APPLICATION MODELING FOR
CONTEXT AWARENESS
APPLICATION MODELING FOR
CONTEXT AWARENESS
Maja Vukovic and Peter Robinson,
University of Cambridge
普及運算報告
Managing Uncertainty:Modeling Users
in Location-Tracking Applications
Present：研一 張永昌
Chang Yung-Chang
Outline
Introduction
Modeling users
Collecting user data
Building the user model
Using Bayesian networks
Performance issues
Experimental results
Discussion
Introduction
Applications：
Track elderly people
Provide targeted advertising to mobile users
track moving objects
Modeling users
 main variable types：
Temporal variables represent when events occur,
including the time of year, day of the week, and time of
day.
Spatial variables represent possible RU locations, such
as a building, town, certain part of town, certain road or
highway, and so forth.
Environmental variables represent things such as
weather conditions, road conditions, and special events.
Behavioral variables represent things such as typical
speeds, resting patterns, preferred work areas, and
common reactions in certain situations.
Collecting user data
Divide the data into two categories：
User-specific data consists of personal
information or trip-related information
Environment-specific data describes the
different artifacts of the environments（weather
conditions、traffic conditions、special events
taking place）
Building the user model
Common ways to build user models：
Machine learning
Predicate logic
First-order logic
Building the user model
Using Bayesian networks
Values：
Event
Time of day
Source
Destination
Weather conditions
Route
Speed
Using Bayesian networks
Performance issues
We would maintain the BN, which involves
updating the probabilities associated with
each node based on new observations,
and we’d perform inference given some
observation.
Experimental results
 simulation：
 used these typical speeds to create the user
speeds during a trip as follows:
45 percent of the time, the speed should be within 10 of
the RU’s typical speed under the current circumstances.
25 percent of the time, the speed should be within 20 of
the RU’s typical speed under the current circumstances.
20 percent of the time, the speed should be within 50 of
the RU’s typical speed under the current circumstances.
10 percent of the time, the speed should be within 100
of the RU’s typical speed under the current
circumstances.
Experimental results
Experimental design
100,000 trips and performed 10 queries on
each trip.
the experiment for RIs 5, 10, 15, 20, 30, 50, 100,
150, and 200 time units.
Experimental results
The figure shows that LSR performed
better when the RI was less than 12 time
units, at which point the two techniques
performed equally well.
We can see how our approach
outperforms LSR, especially with high RIs.
Our technique at RI 100 and 200 performs
better than LSR at RI 50 and 100,
respectively.
Experimental results