Towards Automatic Spatial Verification of Sensor Placement

Dezhi Hong * +

Jorge Ortiz +

Kamin Whitehouse * ^

David Culler +

*University of Virginia+UC Berkeley

^ Microsoft Research

Evolution of Buildings

Evolution of Buildings

Hypothesis

The physical boundary between roomsis detectable

as a statistical boundary in the data.

Challenge

Temp from different rooms Humidity/CO2 from same room

ApproachTemp from different rooms Humidity/CO2 from same room

ApproachTemp from different rooms Humidity/CO2 from same room

• 5 rooms, 3 sensors/room• Sensor type: temperature, humidity, CO2

• Over a one-month period

Data Set

CDFIn the same room

In different rooms!

correlation coefficient correlation coefficient

Inter/Intra Correlation

Mid band correlation Raw data traces

Threshold Analysis

Convergence

14/15 correct = 93.3%

*A-B-C-D-E is used to denote the ground truth location of sensors

Clustering

Mid-band Frequencies

12/15 correct = 80%

Raw data traces

8/15 correct = 53.3%

Clustering

Future Work

• Extended from 5 rooms to ~100 rooms– It didn’t work

• Open questions: – What new techniques can improve results?– What is the boundary that can be found?

Related Work

• Strip, Bind, Search - IPSN’13– Fontugne, et al

• Smart Blueprints - Pervasive’12– Lu, et al

• SMART - Ubicomp’12– Kapitanova, et al

• Wireless Snooping Attack – UbiComp’08– Srinivasan, et al

Summary

• A statistical boundary emerges in the early study on a small data set

• The method may be empirically generalizable• Extensions and modifications to the solution

are needed to verify the generalizability

Questions?

Thank You

Well…

• The early promising results from a small data set are not conclusive due to– Location of the room– Usage of the room– # of rooms

Questions@a large scale

• “Noise” from the same type of sensors– Same type of sensors correlate highly

Humidity Temperature

Room ID Room ID

Corrcoef across rooms

*Both the X and Y axes are arranged by room ID in the same order