Towards Automatic Spatial Verification of Sensor Placement

Dezhi HongJorge 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:(1) Same # of extrema and zero-crossings(2) Extrema symmetric to zero

Methodology• An example of EMD on 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

• We choose the 0.2 FPR point as the boundary threshold for each room.

• TPR: 52%~93%, FPR: 5%~59%

On the mid IMF band On the raw traces

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