Validating sensors in the field via spectral clustering based on their measurement data

In this paper we introduce a spectral-based method for validating sensor nodes in the field via clustering of sensors based on their measurement data. We formalize the notion of peer consistency in measurement data by introducing a notion called ¿sensor indexing¿ and model the problem of identifying...

Full description

Saved in:
Bibliographic Details
Main Authors: Kung, H.T., Vlah, D.
Format: Conference Proceeding
Language:English
Subjects:
Battery charge measurement
Calibration
Data engineering
Heart
Indexing
Instruments
Peer to peer computing
Protection
Sensor phenomena and characterization
Sensor systems
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we introduce a spectral-based method for validating sensor nodes in the field via clustering of sensors based on their measurement data. We formalize the notion of peer consistency in measurement data by introducing a notion called ¿sensor indexing¿ and model the problem of identifying bad sensors as a problem of detecting peer inconsistency. Suppose all sensors have peers. Then by examining a certain number of leading eigenvectors of the measurement data matrix, we can identify those bad sensors which are inconsistent to peer sensors in their reported measurements. Further, we show that by deemphasizing or removing measurements obtained from these bad sensors we can improve the performance of sensor-based applications. We have implemented this spectral-based peer validation method and measured its performance by simulation. We report the effectiveness of the method in identifying bad sensors, and demonstrate its use in deriving accurate solutions in a localization application.
ISSN:2155-7578
2155-7586
DOI:10.1109/MILCOM.2009.5379940