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

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