1. FixtureFinder: Discovering the Existence of Electrical and Water Fixtures Vijay Srinivasan*, John Stankovic, Kamin Whitehouse University of Virginia *(Currently affiliated to Samsung)

2. Motivation For Fixture Monitoring Cooking Toileting Home Healthcare Applications 7 KW hours400 liters Resource conservation applications

3. Fixture Monitoring Using Smart meters Whole house power or water flow Time Power meter Water meter BathroomKitchen BedroomLivingroom 2000 W 100 W 100 litres/hour Poor accuracy for low power or low water flow fixtures False positive noise Identical fixtures

4. Existing Fixture Monitoring Techniques Direct metering on each fixtureIndirect sensing + smart meter Single-Point Infrastructure sensing Images courtesy: HydroSense and Viridiscope (Ubicomp 2009) Requires users to: Identify each fixture, and for each fixture: Install a sensor, or Provide training data

5. FixtureFinder Power meter Water meter BathroomKitchen BedroomLivingroom Automatically: – Identify fixtures – Infer usage times – Infer resource consumption 2 PM 5 PM … Single-Point Infrastructure sensing Training data 7 KW hours 400 liters Home security or automation sensors Light and motion + Lights, sinks and toilets

6. FixtureFinder Insights BathroomKitchen BedroomLivingroom Fixtures identical in meter data Unique in (meter, sensor) data 100 W 100 W, 30 lux 100 W, 50 lux Light sensor Power meter Water meter

7. FixtureFinder Insights BathroomKitchen BedroomLivingroom 100 W, 30 lux 100 W, 50 lux Light sensor False positive noise in meter and sensor data 1.Eliminate noise events in one stream when no activity in other stream 2.Eliminate unmatched noise Power meter Water meter ON-OFF pattern Bedroom light sensor data Power meter data

8. Outline FixtureFinder algorithm Case studies Experimental setup Evaluation results Conclusions

9. FixtureFinder Algorithm Inputs Stream 1 Stream 2 Power meter Water meter Light or motion sensors or Four step algorithm

10. Step 1 – Event Detection Stream 1 Stream 2 Time ON OFF ON OFF 40 500 60 40 140 60200 Stream 1 Stream 2 False positives events: True positive events: 40 lux 100 Watts For example: Edge detection algorithms Key challenge: Large number of false positives 100 40 100 Light sensor Power meter

11. Step 2 – Data fusion Stream 1 Stream 2 Time ON OFF ON OFF 40 100 500 60 40 140 100 60200 40 Stream 1 Stream 2 40 lux 100 Watts For example: Light sensor Power meter Fixture use creates events in multiple streams simultaneously Compute event pairs Eliminate temporally isolated false positives

12. Step 3 – Matching Stream 1 Stream 2 Time ON OFF ON OFF 40 100 500 60 40 140 100 60200 40 Stream 1 Stream 2 40 lux 100 Watts For example: Light sensor Power meter Fixture use occurs in an ON-OFF pattern Match ON event pairs to OFF event pairs Eliminate unmatched false positives High match probability

13. Step 3 – Matching Stream 1 Stream 2 Time ON OFF ON OFF 40 100 60 100 60 40 Stream 1 Stream 2 40 lux 100 Watts For example: Light sensor Power meter High match probability Two ON-OFF event pairs: (40,100) or (40,60) ? True event pairs are more likely than noisy event pairs High pair probability Use both match and pair probabilities to compute ON-OFF event pairs Soft clustering and Min Cost Bipartite matching (Described in paper) Low pair probability All false positives eliminated in this example!

14. Step 4 – Fixture Discovery Stream 1 (Light) intensity Stream 2 (Power) intensity ON TimeOFF Time 411025 PM6 PM 621035:30 PM6:15 PM 43998 PM10 PM 601017 PM8 PM 611009 PM10 PM Step 3: Matching ON-OFF events Clustering Clustering based on: (stream 1 intensity, stream 2 intensity) 40 lux, 100 watts 60 lux, 100 watts Fixtures discovered

15. Outline FixtureFinder algorithm Case studies Experimental setup Evaluation results Conclusions

16. Light Fixture Discovery Power meter Water meter BathroomKitchen BedroomLivingroom Apply FixtureFinder algorithm on every (light sensor, power meter) 40 lumens, 100 watts 40 lumens, 150 watts Unique fixture usage defined by: Light sensor location Light intensity Power consumption

17. Light Fixture Discovery Bedroom light sensor data Bedroom light fixture ON- OFF events Power meter data Large number of false positives after step 1 False positives eliminated after steps 2 and 3

18. Water Fixture Discovery Power meter Water meter BathroomKitchen BedroomLivingroom Fused motion sensor stream Apply FixtureFinder algorithm on (fused motion sensor, power meter) Unique fixture usage defined by: Motion sensor signature Flow rate 100 litres/hour 300 litres/hour

19. Water Fixture Discovery Two toilets with the same flow signature but different motion signatures

20. Water Fixture Discovery Two toilets with the same motion signature but different flow signatures Use event pair probability to pair simultaneous toilet events with correct rooms

21. Outline FixtureFinder algorithm Case studies Experimental setup Evaluation results Conclusions

22. In-Situ Sensor Deployments in Homes Power meter (TED 5000) Water meter (Shenitech) X10 motion Custom light sensing mote One per room in a central location (Except in 3 large rooms where two sensors were used) One per home

23. In-Situ Sensor Deployments in Homes Smart switch Smart plug Contact switches on water fixtures Ground truth for light fixtures Ground truth for water fixtures All sensors deployed in 4 homes for 10 days (Except water meter deployed in 2 homes for 7 days)

24. Outline FixtureFinder algorithm Case studies Experimental setup Evaluation results Conclusions

25. Fixture Discovery Results Discovered all sinks and toilets across 2 homes Discovered 37 out of 41 light fixtures across 4 homes Undiscovered lights: - All in large kitchens - Task lighting or under-cabinet lighting - Used rarely (1-3 times) - Low energy consumption One false positive light with negligible energy consumption

26. Fixture Usage Inference Results Recall: % of ground truth fixture events detected by Fixture Finder Precision: % of detected fixture events that are supported by ground truth Results shown for light fixtures 99% precision 64% recall True positive ON-OFF events from fixtures Single-Point Infrastructure sensing Training data High precision usage data

27. Fixture Usage Inference Results Recall: % of ground truth fixture events detected by Fixture Finder Results shown for light fixtures 92% precision 82% recall Balanced precision and recall Home Activity Monitoring applications Precision: % of detected fixture events that are supported by ground truth

28. Analysis of FixtureFinder Steps Step 1: Event Detection – ME: Meter event detection – SE: Sensor event detection Step 3: Matching – MM: Meter event matching – SM: Sensor event matching Step 2: Data Fusion – SMF: Sensor meter data fusion FixtureFinder Small reduction in recall Significant increase in precision with steps 2, 3, and FixtureFinder Results shown for light fixtures

29. Light Fixture Energy Estimation 91% average energy accuracy for top 90% energy consuming fixtures

30. Water Consumption Estimation 81.5% accuracy in Home 3 89.9% accuracy in Home 4 Home 3Home 4 B – Bathroom K – Kitchen S – Sink F – Flush

31. Outline FixtureFinder algorithm Case studies Experimental setup Evaluation results Conclusions

32. FixtureFinder combines smart meters with existing home security sensors to automatically: – Identify fixtures – Infer usage times – Infer resource consumption Demonstrated for light and water fixtures Complements other fixture monitoring techniques by providing training data without manual effort

33. Future Improvements Expand scope to include: – Additional electrical appliances and water fixtures – Additional sensing modalities such as routers, smart switches, infrastructure sensors Extend algorithm to multi-state appliances – Not just two-state ON-OFF Explore temporal co-occurrence over multiple timescales

34. Thanks Questions?

35. FixtureFinder Approach Power meter Water meter Home security or automation sensors + Automatically discover low power or low water flow fixtures – Lights, sinks, and toilets BathroomKitchen BedroomLivingroom Light and motion

36. Step 3 – Bayesian Matching Two matches possible – (40,100) or (40,60) Assumption: Edge pairs from true fixtures are more frequent than noisy edge pairs – P(40,100) >> P(40,60) Stream 1 Stream 2 Time ON OFF ON OFF 40 100 60 100 60 40 Hidden variables Stream 1 cluster Stream 1 edge Stream 2 edge Stream 2 cluster Observed variables

37. Step 3 – Bayesian Matching Incorporate edge pair probability into a match weight function Perform optimal bipartite matching based on match weight function Eliminate unlikely matches Stream 1 Stream 2 Time ON OFF ON OFF 40 100 60 100 60 40