Transcript Intelligent Environments - Washington State University

Intelligent Environments
Computer Science and Engineering
University of Texas at Arlington
Intelligent Environments
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Course Overview
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Course website
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http://ranger.uta.edu/~holder/courses/cse
6362.html
Major topics
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Sensors, Networks, Database
Prediction, Decision-Making
Robotics
Privacy and Security
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Course Overview
Readings, lectures, quizzes
Homeworks
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HW1:
HW2:
HW3:
HW4:
Sensors
Networks
Database
Prediction and Decision-Making
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Course Overview
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Presentation topics
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Architectural design
Human-computer interfaces
Visualization
Smart materials
Energy efficiency
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Course Overview
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Project
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Simulated intelligent environment
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Sensors
Network
Database
Prediction and decision-making
Scenario-based design
Project demonstration
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Course Overview
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Invited Speakers
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Intelligent Environments
Introduction
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Definitions
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Intelligent
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Environment
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Able to acquire and apply knowledge
Knowledge is more than data
Surroundings
Intelligent Environment
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An environment able to acquire and apply
knowledge about you and your surroundings in
order to improve your experience.
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Definitions
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“Improve your experience”
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Comfort
Security
Efficiency
Productivity
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IE Scenarios
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Your house learns your living patterns in
order to optimize energy efficiency.
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Your house learns that you like to sleep later
on Saturdays.
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Turn down the HVAC when you are gone
Postpone morning events (e.g., coffee-maker,
alarm, shades, …)
Your house adapts to the entertainment
center settings of each inhabitant
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Volume, favorite channels
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IE Scenarios (cont.)
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Your car collects information about its
environment as you drive
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Theatre locations, times, ticket availability
Restaurant locations, cuisine, mean wait
time
Gas stations, facilities
Emergency care, closest, facilities
Recommendations based on learned
preferences and destination prediction
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More IE Scenarios
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???
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Intelligent Environments
Projects
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IE Projects: Academic
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UTA MavHome Smart Home
Georgia Tech Aware Home
MIT Intelligent Room
MIT House_n
Stanford Interactive Workspaces
UC Boulder Adaptive House
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IE Projects: Commercial
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General Electric Smart Home
Microsoft Easy Living
Philips Vision of the Future
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Georgia Tech Aware Home
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Perceive and assist occupants
Aging in Place (crisis support)
Ubiquitous sensing
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Scene understanding, object recognition
Multi-camera, multi-person tracking
Context-based activity
Smart floor
www.cc.gatech.edu/fce/ahri
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MIT Intelligent Room
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Support natural interaction with room
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Speech
Gesture
Movement
Context
Numerous projects
www.ai.mit.edu/projects/iroom
Supported by MIT Project Oxygen (pervasive
computing)
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oxygen.ai.mit.edu
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MIT house_n
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MIT Department of Architecture
Dynamic, evolving places that respond
to the complexities of life
New technologies
New materials
New design strategies
architecture.mit.edu/house_n
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Stanford Interactive
Workspaces
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Large wall and tabletop interactive
displays
Scientific visualization
Mobile computing devices
Computer-supported cooperative work
Distributed system architectures
graphics.stanford.edu/projects/iwork
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UC Boulder Adaptive House
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Infer patterns and predict actions
HVAC, water heater, lighting
Goals
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Reduce occupant manual control
Energy efficiency
Nice simulation
www.cs.colorado.edu/~mozer/house
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General Electric Smart Home
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Appliance control
Climate control
Energy management
Lighting control
Security
Consumer Electronics Bus (CEBus)
www.ge-smart.com
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Microsoft Easy Living
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Camera-based person detection and tracking
Geometric world modeling for context
Sensor fusion
Authentication
Distributed systems
Ubiquitous computing
research.microsoft.com/easyliving
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Philips Vision of the Future
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Less obtrusive technology
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Lots of gadgets
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Heart controller
Interactive wallpaper
Control wands
Intelligent garbage can
www.design.philips.com/vof
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UTA MavHome Smart Home
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Focus on entire home as a rational agent
Goals
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Maximize comfort and productivity of inhabitants
Minimize cost
Ensure security
Reasoning and adaptation
ranger.uta.edu/smarthome
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UTA MavHome Smart Home
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UTA MavHome Projects
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CSE Projects
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MavHome Agent Design (Cook, Holder, Huber, Kamangar)
Predicting inhabitant and house behaviors (Cook, Holder)
Robot assistance (Huber, Cook)
Web monitoring and control (Kamangar)
Distributed sensor fusion (Kamangar)
Database monitoring (Chakravarthy)
Multimedia traffic for entertainment and security (Yerraballi)
Intelligent routing, mobility prediction (Das)
Cross-Disciplinary Projects
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Smart materials and structures (Civil Engineering)
Nano structures (Electrical Engineering)
Device communication (Telcordia Technologies)
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MavHome Sponsors
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National Science Foundation ($1.2M)
UTA to fund house
Nortel, $100K to Das for research
Friendly Robotics, robot donation
Potential
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NIH (assistance for people with disabilities)
DARPA (military applications)
Ericsson, Motorola, Nokia, Dallas Semiconductor
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Proposed MavHome Location
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Southeast corner of UTA Blvd and Davis
Nedderman
Hall
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MavHome FloorPlan (1st floor)
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MavHome FloorPlan (2nd floor)
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Intelligent Environments
Challenges
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IE Challenges
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Sensors
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Type
Number
Interference
Autonomous
Active vs. Passive
Communication
Interface
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IE Challenges
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Networking
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Wired vs. Wireless
Protocol(s)
Bandwidth
Organization
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IE Challenges
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Data storage
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Size
Query rate
Active vs. Passive
Decision-making
Communication
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IE Challenges
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Prediction and Decision-Making
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Dynamic, temporal patterns
Data relevance
Sensor fusion
Real-time
Autonomy
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IE Challenges
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Robotics
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Mechanical capabilities
Learning
Safety
Privacy and Security
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Unwanted surveillance
“Break-ins”
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IE Challenges
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System architecture
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Agent-based vs. monolithic
Hierarchical vs. flat
Distributed vs. centralized control
Systems integration
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Plug-n-play everything
Existing appliances
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IE Design: Smart Home
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Physical home design
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New vs. retrofit
Home architecture
Materials
Sensors, Networking, Database
Prediction and Decision-making
System architecture
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My Smart Home
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