towards automatic spatial verification of sensor placement
DESCRIPTION
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 PresentationTRANSCRIPT
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