1. Intelligent Light Control using Sensor Networks Vipul Singhvi 1,3, Andreas Krause 2, Carlos Guestrin 2,3, Jim Garrett 1, Scott Matthews 1 Carnegie Mellon University 1 Department of Civil and Environmental Engineering 2 School of Computer Science 3 Center of Learning and Discovery

2. Motivation Current built infrastructure Trillions of dollars investment Cost over the life cycle Research shows potential gains from reducing operating cost and improving occupant performance $10 - $30 billion/yr from reduced energy consumption $20 - $160 billion/yr gained from improvement in comfort leading to better occupant performance Reduction in energy cost related to reduced comfort & performance: Complex tradeoff optimization Life cycle building cost Salary cost over building life cycle Maintenance and operation Construction Sensor networks Smart monitoring and actuation can significantly reduce cost and improve occupant performance

3. 10 Motivating Scenario Louvers All lights 0-10 levels 10 5 5 0 0 5 0 0 0 Operator Controller 0 6 Andy Bob Louvers/ Blinds Feed back Coordinate lighting to make everybody happy Strategy to exploit natural lighting Predictive light control

4. Challenges Knowing the current state Light levels and occupants location Capturing occupant and operator preferences & happiness Optimization of tradeoff Occupants happier OR save more energy

5. Desk Knowing the current state of the world Indoor Environment Light levels Pervasive sensor network Wireless or Wired Tracking occupants Smart tags RFID tags Camera tracking

6. Utility Theory: Framework to compare choices based on preferences Personal preference Attributes: Coolness, Horse Power, Mileage, COST…. Representation complexity of utility function Preferences & Happiness Lamborghini, Second Hand 2003 model, $50,000 Toyota Corolla, New 2006 model,$30,000

7. Occupant preference:Comfort Light level Utility Function Task dependent Light levels Depends on lamp setting Use sensing to learn effect of lamps on person i – Control lamp settings a to max. occupant preferences, a=(a 1,…,a n ), a j – level of lamp j Building Operations: Occupants Bob Andy

8. Building Operations: Operator Operator preference: Cost Operating cost Maintenance Cost Decreases monotonically with the energy expended Utility function a j, j th lamp 100200300 Operating Cost 0.00 0.20 0.40 0.60 0.80 1.00 Normalized utility Cheaper the better

9. Utility Maximization : Tradeoff Maximize system utility: Make occupants and operator happy! a = (a 1,…….a n ) Scalarization technique is the tradeoff parameter OccupantsOperator

10. a6 a5a5 a4 a2a2 a1a1 Utility Maximization: Complexity Evaluating U(a) for combinations of all lamp setting for just 6 lamps the total number is 10 6 Evaluating argmax U(a) is also over that big space Exponential in number of lamps! 10 levels

11. a6 a5 a4 a2 a1a1 Reducing Complexity Exploit problem structure: Zoning Distributed action selection approach (Guestrin 03) Exact solution to the coordination problem

12. Open-loop controller: Coordinated Lighting Control law using Occupant utility and Coordination Graph approach a

13. Test Bed Control Schematics 10 table lamps 12 motes aka occupants Size: 146 * 30 in., 7 zones 146 in. 1 2 3 4 5 6 7

14. Coordinated Lighting: Results Comparison to greedy approach Each occupant comes and actuates the light Caveat: cannot reduce the level of a already ON light At = 0.4, reduction in comfort = 7% but reduction in energy cost = 30% Greedy Heuristic Energy Cost Measured utility 30% 0.4 Coordinated Illumination

15. Coordinated Lighting Performs significantly better than typical greedy approach Solves the complex optimization using the structure of the problem (zoning) Coordinated Lighting Natural Lighting Predictive light control

16. Closed-loop controller: Daylight Harvesting Control law a Online sensing using sensor network Current Light Level Sense natural light levels Actuate lamps to compensate for extra light

17. Variability using the real sunlight data from Pittsburgh Day Light Harvesting: Sun Simulation Simulated sun using overhead lamps Real sun intensities Measured intensities at center Sun Lamps

18. Daylight Harvesting: Utility Redefined Represents the sunlight intensity at time t and point in space x, New utility definition Maximization problem Sun Lamps

19. Running the Simulations

20. Day Light Harvesting: Evaluation Gamma values (0.01, 0.4), same setup Gamma = 0.01, 15% of energy savings Gamma = 0.4, 55% of energy savings Loss in occupant utility due to too much light Shading, Louvers Measured Utility Energy Cost Measured Utility Energy Cost

21. Day light harvesting Builds on the coordinated lighting approach Saves significant (~50%)energy cost during sun time Long term sensor deployment: battery life Sensor scheduling Save battery life Coordinated Lighting Natural Lighting Predictive light control

22. Spatial correlation in sunlight distribution Temporal correlation in sunlight intensity Use only a small number of sensor Estimate the light levels at other times and locations Active Sensing aka Sensor Scheduling Desk ? ? ? ? When and Where to sense!

23. Active Sensing: Scheduling Use sunlight observation (samples) to estimate the current sunlight intensity distribution The utility formulation then changes to conditional expected utility Choose a set of observations that yields best maximum expected utility values Sunlight Distribution Conditioned on observation

24. Active Sensing Calculating a set of observation that maximize More observation: better accuracy but high battery cost Constraint the observations to a budget Allocate strategically to max. EU

25. Active Sensing: Single Sensor Optimal solution for single sensor budget allocation in polynomial time (Krause & Guestrin 05) X i where i is the time step, (5 times steps, Budget 2) For just 2 sensors: complexity is NP-hard X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 X2X2 X3X3 X4X4 X5X5 X1X1 X2X2 X3X3 X4X4 X5X5 Y1Y1 Y2Y2 Y3Y3 Y4Y4 Y5Y5 X1X1 X2X2 X3X3 X4X4 X5X5 Y1Y1 Y2Y2 Y3Y3 Y4Y4 Y5Y5 X1X1 X2X2 X3X3 X4X4 X5X5 Y1Y1 Y2Y2 Y3Y3 Y4Y4 Y5Y5

26. Heuristic for solving multiple sensor Coordinate ascent scheme (uses optimal solution for single sensor) Guaranteed to improve score on each iteration, guaranteed to not perform worse than independent scheduling Can be used for more than 2 sensors Active Sensing: Heuristic X1X1 X2X2 X3X3 X4X4 X5X5 Y1Y1 Y2Y2 Y3Y3 Y4Y4 Y5Y5 Optimize sensor 1 X1X1 X2X2 X3X3 X4X4 X5X5 Y1Y1 Y2Y2 Y3Y3 Y4Y4 Y5Y5 X1X1 X2X2 X3X3 X4X4 X5X5 Y1Y1 Y2Y2 Y3Y3 Y4Y4 Y5Y5 Optimize sensor 2

27. Active Sensing: Results 3 sensors, upto 10 readings per sensor in a day Energy saving are close approximation compared to sensing continuously Even a small number of readings (3) provides results as good as continuous Energy Cost Measured Utility No sensing 1 obs./sensor 10 obs./sensor

28. Active Sensing for Daylight Harvesting Exploit temporal correlation in sunlight intensity to schedule sensing Significant reduction in sensing requirement for comparable performance Can be integrated in the coordinated lighting formulation Coordinated Lighting Natural Lighting Predictive light control

29. Predictive light control Probabilistic model on mobility People move independent of each other Modeled using a random walk Stay in same position Move left, move right Zone 1 Zone 2

30. Integrating mobility Assuming full observability Computing expected utility Probability of motion

31. Predictive Lighting: Results 20 step random walk Total utility increase of about 25% Low values of trade-off parameter, system prefers occupants comforts Occupant Utility Energy Cost Total Utility Normalized Scale Occupant Utility Energy Cost Using prediction Without prediction

32. Conclusion Coordinated lighting strategy Maximizes happiness using utility maximization Solves complex coordination problem Day light harvesting Exploits natural light sources using sensors 50-70% reduction in energy consumption Active sensing Sensor scheduling using sunlight distribution Substantial increase in network life time Predictive Light control Captures occupant mobility Higher total utility for the system