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Towards Automatic Spatial Verification of Sensor Placement. Dezhi Hong Jorge Ortiz, Kamin Whitehouse, David Culler. Why do we care?. Huge amount of sensor s , meters… Building setup changes Metadata management & maintenance Automated verification process . Before set off. - PowerPoint PPT Presentation
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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