TY - GEN
T1 - Towards automatic spatial verification of sensor placement in buildings
AU - Hong, Dezhi
AU - Ortiz, Jorge
AU - Whitehouse, Kamin
AU - Culler, David
N1 - Funding Information:
Thanks to our shepherd, Kin Cheong Sou, and the anonymous reviewers for helpful comments. Thanks to Albert Goto, for making the first author’s stay at UC Berkeley comfortable. Sincere gratitude goes to Kamin and David, for letting this collaboration happen. This work was partly funded by the NSF grants EFRI-1038271 and CPS-1239552.
Publisher Copyright:
© 2013 ACM.
PY - 2013/11/11
Y1 - 2013/11/11
N2 - Most large, commercial buildings contain thousands of sensors that are manually deployed and managed. These sensors are used by software and firmware processes to analyze and control building operations. Many such processes rely on sensor placement information in order to perform correctly. However, as buildings evolve and building subsystems grow and change, managing placement information becomes burdensome and error-prone. An automatic verification process is needed. We investigate empirical methods to automate spatial verification. We find that a spatial clustering algorithm is able to classify relative sensor locations - for 15 sensors, spread across five rooms in a building - with 93.3% accuracy, 13% better than a k-means clustering-based baseline method. Analysis on the raw time series data has a classification accuracy of only 53%. By decomposing the signal into intrinsic modes and performing correlation analysis, an observable, statistical boundary emerges that corresponds to a physical one. These results may suggest that automatic verification of placement information is possible.
AB - Most large, commercial buildings contain thousands of sensors that are manually deployed and managed. These sensors are used by software and firmware processes to analyze and control building operations. Many such processes rely on sensor placement information in order to perform correctly. However, as buildings evolve and building subsystems grow and change, managing placement information becomes burdensome and error-prone. An automatic verification process is needed. We investigate empirical methods to automate spatial verification. We find that a spatial clustering algorithm is able to classify relative sensor locations - for 15 sensors, spread across five rooms in a building - with 93.3% accuracy, 13% better than a k-means clustering-based baseline method. Analysis on the raw time series data has a classification accuracy of only 53%. By decomposing the signal into intrinsic modes and performing correlation analysis, an observable, statistical boundary emerges that corresponds to a physical one. These results may suggest that automatic verification of placement information is possible.
KW - Clustering
KW - Correlation Coefficient
KW - Empirical Mode Decomposition
KW - Sensor Placement
UR - http://www.scopus.com/inward/record.url?scp=84934294631&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84934294631&partnerID=8YFLogxK
U2 - 10.1145/2528282.2528302
DO - 10.1145/2528282.2528302
M3 - Conference contribution
AN - SCOPUS:84934294631
T3 - BuildSys 2013 - Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
BT - BuildSys 2013 - Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
PB - Association for Computing Machinery, Inc
T2 - 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, BuildSys 2013
Y2 - 11 November 2013 through 15 November 2013
ER -