1. Intelligent Environments1 Computer Science and Engineering University of Texas at Arlington

2. Intelligent Environments2 Course Overview Course website http://ranger.uta.edu/~holder/courses/cse 6362.html http://ranger.uta.edu/~holder/courses/cse 6362.html Major topics Sensors, Networks, Database Prediction, Decision-Making Robotics Privacy and Security

3. Intelligent Environments3 Course Overview Readings, lectures, quizzes Homeworks HW1: Sensors HW2: Networks HW3: Database HW4: Prediction and Decision-Making

4. Intelligent Environments4 Course Overview Presentation topics Architectural design Human-computer interfaces Visualization Smart materials Energy efficiency …

5. Intelligent Environments5 Course Overview Project Simulated intelligent environment Sensors Network Database Prediction and decision-making Scenario-based design Project demonstration

6. Intelligent Environments6 Course Overview Invited Speakers …

7. Intelligent Environments7 Introduction

8. Intelligent Environments8 Definitions Intelligent Able to acquire and apply knowledge Knowledge is more than data Environment Surroundings Intelligent Environment An environment able to acquire and apply knowledge about you and your surroundings in order to improve your experience.

9. Intelligent Environments9 Definitions “Improve your experience” Comfort Security Efficiency Productivity

10. Intelligent Environments10 IE Scenarios Your house learns your living patterns in order to optimize energy efficiency. Turn down the HVAC when you are gone Your house learns that you like to sleep later on Saturdays. Postpone morning events (e.g., coffee-maker, alarm, shades, …) Your house adapts to the entertainment center settings of each inhabitant Volume, favorite channels

11. Intelligent Environments11 IE Scenarios (cont.) Your car collects information about its environment as you drive 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

12. Intelligent Environments12 More IE Scenarios ???

13. Intelligent Environments13 Intelligent Environments Projects

14. Intelligent Environments14 IE Projects: Academic UTA MavHome Smart Home Georgia Tech Aware Home MIT Intelligent Room MIT House_n Stanford Interactive Workspaces UC Boulder Adaptive House

15. Intelligent Environments15 IE Projects: Commercial General Electric Smart Home Microsoft Easy Living Philips Vision of the Future

16. Intelligent Environments16 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 www.cc.gatech.edu/fce/ahri

17. Intelligent Environments17 MIT Intelligent Room Support natural interaction with room Speech Gesture Movement Context Numerous projects www.ai.mit.edu/projects/iroom Supported by MIT Project Oxygen (pervasive computing) oxygen.ai.mit.edu

18. Intelligent Environments18 MIT house_n 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

19. Intelligent Environments19 Stanford Interactive Workspaces Large wall and tabletop interactive displays Scientific visualization Mobile computing devices Computer-supported cooperative work Distributed system architectures graphics.stanford.edu/projects/iwork

20. Intelligent Environments20 UC Boulder Adaptive House Infer patterns and predict actions HVAC, water heater, lighting Goals Reduce occupant manual control Energy efficiency Nice simulation www.cs.colorado.edu/~mozer/house

21. Intelligent Environments21 General Electric Smart Home Appliance control Climate control Energy management Lighting control Security Consumer Electronics Bus (CEBus) www.ge-smart.com

22. Intelligent Environments22 Microsoft Easy Living Camera-based person detection and tracking Geometric world modeling for context Sensor fusion Authentication Distributed systems Ubiquitous computing research.microsoft.com/easyliving

23. Intelligent Environments23 Philips Vision of the Future Less obtrusive technology Heart controller Lots of gadgets Interactive wallpaper Control wands Intelligent garbage can www.design.philips.com/vof

24. Intelligent Environments24 UTA MavHome Smart Home Focus on entire home as a rational agent Goals Maximize comfort and productivity of inhabitants Minimize cost Ensure security Reasoning and adaptation ranger.uta.edu/smarthome

25. Intelligent Environments25 UTA MavHome Smart Home

26. Intelligent Environments26 UTA MavHome Projects CSE Projects 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 Smart materials and structures (Civil Engineering) Nano structures (Electrical Engineering) Device communication (Telcordia Technologies)

27. Intelligent Environments27 MavHome Sponsors National Science Foundation ($1.2M) UTA to fund house Nortel, $100K to Das for research Friendly Robotics, robot donation Potential NIH (assistance for people with disabilities) DARPA (military applications) Ericsson, Motorola, Nokia, Dallas Semiconductor

28. Intelligent Environments28 Proposed MavHome Location Southeast corner of UTA Blvd and Davis Nedderman Hall

29. Intelligent Environments29 MavHome FloorPlan (1 st floor)

30. Intelligent Environments30 MavHome FloorPlan (2 nd floor)

31. Intelligent Environments31 Intelligent Environments Challenges

32. Intelligent Environments32 IE Challenges Sensors Type Number Interference Autonomous Active vs. Passive Communication Interface

33. Intelligent Environments33 IE Challenges Networking Wired vs. Wireless Protocol(s) Bandwidth Organization

34. Intelligent Environments34 IE Challenges Data storage Size Query rate Active vs. Passive Decision-making Communication

35. Intelligent Environments35 IE Challenges Prediction and Decision-Making Dynamic, temporal patterns Data relevance Sensor fusion Real-time Autonomy

36. Intelligent Environments36 IE Challenges Robotics Mechanical capabilities Learning Safety Privacy and Security Unwanted surveillance “Break-ins”

37. Intelligent Environments37 IE Challenges System architecture Agent-based vs. monolithic Hierarchical vs. flat Distributed vs. centralized control Systems integration Plug-n-play everything Existing appliances

38. Intelligent Environments38 IE Design: Smart Home Physical home design New vs. retrofit Home architecture Materials Sensors, Networking, Database Prediction and Decision-making System architecture

39. Intelligent Environments39 My Smart Home ?