Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Speech pitch determination based on Hilbert-Huang transform
Signal Processing
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Extending IP to Low-Power, Wireless Personal Area Networks
IEEE Internet Computing
Automatic detection of ECG wave boundaries using empirical mode decomposition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
sMAP: a simple measurement and actuation profile for physical information
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Smart blueprints: automatically generated maps of homes and the devices within them
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Being SMART about failures: assessing repairs in SMART homes
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Strip, bind, and search: a method for identifying abnormal energy consumption in buildings
Proceedings of the 12th international conference on Information processing in sensor networks
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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.