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FixtureFinder: Discovering the Existence of Electrical and Water Fixtures

FixtureFinder: Discovering the Existence of Electrical and Water Fixtures. Vijay Srinivasan*, John Stankovic, Kamin Whitehouse University of Virginia *(Currently affiliated to Samsung). Motivation For Fixture Monitoring. Home Healthcare Applications. Cooking. Toileting.

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FixtureFinder: Discovering the Existence of Electrical and Water Fixtures

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  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 Home Healthcare Applications Cooking Toileting Resource conservation applications 7 KW hours 400 liters

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

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

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

  6. FixtureFinder Insights Unique in (meter, sensor) data Fixtures identical in meter data Water meter Power meter Bathroom Kitchen Light sensor Bedroom Livingroom 100 W 100 W, 50 lux 100 W, 30 lux

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

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

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

  10. Step 1 – Event Detection For example: Stream 1 40 lux 100 Watts Light sensor ON 40 40 Stream 1 Edge detection algorithms OFF 40 140 Time Key challenge: Large number of false positives 60 ON 100 40 500 Stream 2 200 60 100 OFF False positives events: Stream 2 True positive events: Power meter

  11. Step 2 – Data fusion For example: Stream 1 40 lux 100 Watts Light sensor ON 40 40 Stream 1 Fixture use creates events in multiple streams simultaneously OFF 40 140 Time 60 ON 100 40 500 Compute event pairs Stream 2 200 60 100 OFF Eliminate temporally isolated false positives Stream 2 Power meter

  12. Step 3 – Matching For example: Stream 1 40 lux 100 Watts Light sensor High match probability ON 40 Fixture use occurs in an ON-OFF pattern Stream 1 OFF 40 140 Time Match ON event pairs to OFF event pairs 60 ON 100 40 500 Stream 2 200 60 100 OFF Eliminate unmatched false positives Stream 2 Power meter

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

  14. Step 4 – Fixture Discovery Step 3: Matching ON-OFF events Fixtures discovered 40 lux, 100 watts Clustering 60 lux, 100 watts Clustering based on:(stream 1 intensity, stream 2 intensity)

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

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

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

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

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

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

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

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

  23. In-Situ Sensor Deployments in Homes Ground truth for light fixtures Smart plug Smart switch Ground truth for water fixtures Contact switches on 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 One false positive light with negligible energy consumption 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

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

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

  28. Analysis of FixtureFinder Steps • Results shown for light fixtures • 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

  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 B – Bathroom K – Kitchen S – Sink F – Flush Home 3 Home 4

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

  32. Conclusions • 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. ThanksQuestions?

  35. FixtureFinder Approach • Automatically discover low power or low water flow fixtures • Lights, sinks, and toilets + Light and motion Home security or automation sensors Water meter Power meter Bathroom Kitchen Bedroom Livingroom

  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 ON 40 OFF 40 Time 60 ON 100 Stream 1 cluster Stream 2 cluster 60 100 OFF Hidden variables Stream 2 Stream 1 edge Stream 2 edge 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 ON 40 OFF 40 Time 60 ON 100 60 100 OFF Stream 2

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