Wireless integrated network sensors
Communications of the ACM
The Byzantine Generals Problem
ACM Transactions on Programming Languages and Systems (TOPLAS)
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Fault Tolerance in Collaborative Sensor Networks for Target Detection
IEEE Transactions on Computers
IEEE Transactions on Computers
An analysis of a large scale habitat monitoring application
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Proceedings of the 3rd international conference on Embedded networked sensor systems
On Distributed Fault-Tolerant Detection in Wireless Sensor Networks
IEEE Transactions on Computers
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Fidelity and yield in a volcano monitoring sensor network
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Real-Time Implementation of Fault Detection in Wireless Sensor Networks Using Neural Networks
ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
Sensor network data fault types
ACM Transactions on Sensor Networks (TOSN)
Declarative support for sensor data cleaning
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Designing secure sensor networks
IEEE Wireless Communications
Proceedings of the 10th ACM symposium on Performance evaluation of wireless ad hoc, sensor, & ubiquitous networks
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Current sensor networks experience many faults that hamper the ability of scientists to draw significant inferences. We develop a method to systematically identify when these faults occur so that proper corrective action can be taken. We propose an adaptable modular framework that can utilize different modeling methods and approaches to identifying trustworthy sensors. We focus on using hierarchical Bayesian space-time (HBST) modeling to model the phenomenon of interest, and use maximum a posteriors selection to identify a set of trustworthy sensors. Compared to an analogous linear autoregressive system, we achieve excellent fault detection when the HBST model accurately represents the phenomenon.