1. Towards Automatic Spatial Verification of Sensor Placement Dezhi Hong Jorge Ortiz, Kamin Whitehouse, David Culler

2. Why do we care? Huge amount of sensors, meters… Building setup changes Metadata management & maintenance Automated verification process

3. Motivation Huge amount of sensors, meters… Building setup changes Metadata management & maintenance Automated verification process

4. Before set off Statistical boundary? Discoverability? Convergence/Generalizability?

5. Methodology Empirical Mode Decomposition (EMD) Intrinsic Mode Function (IMF) re-aggregation Correlation analysis Thresholding

6. Methodology EMD

14. IMF: (1)Same # of extrema and zero-crossings (2)Extrema symmetric to zero

16. Methodology An example of EMD on a sensor trace

17. Methodology IMF re-aggregation 2 temp. in diff. rms2 sensors in a rm

18. Setup 5 rooms, 3 sensors/room Sensor type: temperature, humidity, CO 2 Over a one-month period

19. Results Distribution generation

20. Results Receiver Operating Characteristic We choose the 0.2 FPR point as the boundary threshold for each room. TPR: 52%~93%, FPR: 5%~59% On the mid IMF bandOn the raw traces

21. Results Convergence The threshold values converge to a similar value – 0.07 Indicating generalizability

22. Results Clustering results (thresholding based) 14/15 correct = 93.3%

23. Results Clustering results (MDS + k-means) On corrcoef from EMD-based 12/15 correct = 80% On corrcoef from raw traces 8/15 correct = 53.3%

24. Conclusion A statistical boundary Discoverable Empirically generalizable

25. On-going & Future Work Larger sets Data from different platforms K-means directly on the IMFs

26. Qs? Thank You