Smart Home Technologies

1. Smart Home Technologies CSE 4392 / CSE 5392 Spring 2006 Manfred Huber huber@omega.uta.edu

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 ofIntelligent Environments • Improve Inhabitant experience: • Optimize inhabitant productivity • Minimize operating costs • Improve comfort • Simplify use of technologies • Ensure security • Enhance accessibility

4. Requirements forIntelligent 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 inIntelligent 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