IEEE Transactions on Computers
Distributed fault detection of wireless sensor networks
DIWANS '06 Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks
Fault Detection in Distributed Systems by Representative Subspace Mapping
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Fault detection of wireless sensor networks
Computer Communications
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
PeerWatch: a fault detection and diagnosis tool for virtualized consolidation systems
Proceedings of the 7th international conference on Autonomic computing
Unknown Fault Diagnosis for Nonlinear Hybrid Systems Using Strong State Tracking Particle Filter
ISDEA '10 Proceedings of the 2010 International Conference on Intelligent System Design and Engineering Application - Volume 02
Alarm processing with model-based diagnosis of event discrete systems
Proceedings of the AI for an Intelligent Planet
Motion fault detection and isolation in Body Sensor Networks
Pervasive and Mobile Computing
A hidden Markov model-based algorithm for fault diagnosis withpartial and imperfect tests
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper describes a data-driven approach to sensor data validation. The data originates from a network of sensors embedded in an indoor environment such as an office, home, factory, public mall or airport. Data analysis is performed to automatically detect events and classify activities taking place within the environment. Sensor failure and in particular intermittent failure, caused by electrical interference, undermines the inference processes. PCA and CCA are compared for detecting intermittent faults and masking such failures. The fault detection relies on models built from historical data. As new sensor observations are collected the model is updated and compared to that previously estimated, where a difference is indicative of a failure.