Towards Automatic Spatial Verification of Sensor Placement

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

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

Before set off • Statistical boundary? • Discoverability? • Convergence/Generalizability?

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

IMF: Same # of extrema and zero-crossings Extrema symmetric to zero

Methodology • An example of EMDon a sensor trace

Methodology • IMF re-aggregation 2 temp. in diff. rms 2 sensors in a rm

Setup • 5 rooms, 3 sensors/room • Sensor type: temperature, humidity, CO2 • Over a one-month period

Results • Distribution generation

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

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

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

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%

Conclusion • A statistical boundary • Discoverable • Empirically generalizable

Qs? Thank You

Towards Automatic Spatial Verification of Sensor Placement