smart system educational content university

smart systems

1. Smart Home Technologies
CSE 4392 / CSE 5392
Spring 2006
Manfred Huber
[email protected]

2. Intelligent Environments

Environments that use technology to
assist inhabitants by automating task
components

Aimed at improving inhabitants’
experience and task performance

NOT: large number of electronic
gadgets

3. Objectives of
Intelligent Environments

Improve Inhabitant experience:

Optimize inhabitant productivity

Minimize operating costs

Improve comfort

Simplify use of technologies

Ensure security

Enhance accessibility

4. Requirements for
Intelligent Environments

Acquire and apply knowledge about
tasks that occur in the environment

Automate task components that
improve efficiency of inhabitant tasks

Provide unobtrusive human-machine
interfaces

Adapt to changes in the environment
and of the inhabitants

Ensure privacy of the inhabitants

5. Examples of
Intelligent Environments

Intelligent Workspaces

Automatic note taking

Simplified information sharing

Optimized climate controls

Automated supply ordering

6. Examples of
Intelligent Environments

Intelligent Vehicles

Location-aware navigation systems

Task-specific navigation

Traffic-awareness

7. Examples of
Intelligent Environments

Smart Homes

Optimized climate and light controls

Item tracking and automated ordering
for food and general use items

Automated alarm schedules to match
inhabitants’ preferences

Control of media systems

8. Existing Projects

Academic

Georgia Tech Aware Home

MIT Intelligent Room

Stanford Interactive Workspaces

UC Boulder Adaptive House

UTA MavHome Smart Home

TCU Smart Home

9. Existing Projects

Industry

General Electric Smart Home

Microsoft Easy Living

Philips Vision of the Future

Verizon Connected Family

10. Georgia Tech Aware Home
Perceive and assist occupants
Aging in Place (crisis support)
Ubiquitous sensing
Scene understanding, object recognition

Multi-camera, multi-person tracking

Context-based activity
Smart floor
http://www.cc.gatech.edu/fce/ahri/

11. MIT Intelligent Room

Support natural interaction with room

Speech-based information access

Gesture recognition

Movement tracking

Context-aware automation

http://www.ai.mit.edu/projects/aire/

12. Stanford Interactive
Workspaces

Large wall and tabletop interactive
displays

Scientific visualization

Mobile computing devices

Computer-supported cooperative work

Distributed system architectures

http://iwork.stanford.edu/

13. UC Boulder Adaptive House

Infer patterns and predict actions

Machine learning for automation

HVAC, water heater, lighting control

Goals:

Reduce occupant manual control

Improve energy efficiency

http://www.cs.colorado.edu/~mozer/house/

14. UTA MavHome Smart Home

Learning of inhabitant patterns

Learn optimal automation strategies

Goals

Maximize comfort and productivity
Minimize cost

Ensure security

http://ranger.uta.edu/smarthome/

15. TCU Smart Home

Inhabitant Prediction

Smart entertainment control

Smart kitchen recipe services

Household staff modeling

http://personal.tcu.edu/~lburnell/
crescent/crescent.html

16. General Electric Smart Home

Appliance control interfaces

Climate control

Energy management devices

Lighting control

Security systems

Consumer Electronics Bus (CEBus)

http://www.geindustrial.com/cwc/home

17. Microsoft Easy Living

Camera-based person detection and tracking

Geometric world modeling for context

Multimodal sensing

Biometric authentication

Distributed systems

Ubiquitous computing

http://research.microsoft.com/easyliving/

18. Philips Vision of the Future

Less obtrusive technology

Technology devices

Interactive wallpaper

Control wands

Intelligent garbage can

http://www.design.philips.com/vof

19. Verizon Connected Family

Remote monitoring of the home

Entry authentication

Integrated, pervasive communications

Centralized data management

20. Challenges in
Intelligent Environments

Home design and sensor layout

Communication and pervasive computing

Natural interfaces

Management of available data

Capture and interpretation of tasks

Decision making for automation

Robotic control

Large-scale integration

Inhabitant privacy

21. Sensors

How many and what type?

How to interpret sensor data?

How to interface with sensors?

Are sensors active or passive?

22. Communications

What medium and protocol?

How to handle bandwidth limitations?

What structure does the
communication infrastructure have?

23. Data Management

How to store all the data?

What data is stored?

How is data distributed to the
pervasive computing infrastructure?

24. Prediction & Decision
Making

How to extract and represent
inhabitants’ task patterns?

What patterns should be maintained?

How to determine the actions to
automate?

To what level should tasks be
automated?

25. Automation

How are the tasks automated?

How are actuators controlled?

How is safety ensured?

26. System Integration

How to achieve extensibility?

Should the system be centralized or
decentralized?

How to integrate existing technology
components?

How to make integration and
interface intuitive?

27. Privacy

How to ensure that inhabitant
information remains private?

What data should be gathered?

How should personal data be
maintained and used?

28. Course Topics

Sensing

Networking

Databases

Prediction and Data Mining

Decision Making

Robotics

Privacy Issues

29. Example Scenario

Smart kitchen item tracking

Sense and monitor items in the kitchen

Predict usage patterns

Automatically generate shopping lists
based on usage patterns

Automatically retrieve replacement items