Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Proceedings of the 3rd international conference on Embedded networked sensor systems
Towards correcting input data errors probabilistically using integrity constraints
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Blind calibration of sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
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
Sensor network data fault types
ACM Transactions on Sensor Networks (TOSN)
A collaborative approach to in-place sensor calibration
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Declarative support for sensor data cleaning
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
A pragmatic testing approach for wireless sensor networks
Proceedings of the 6th ACM workshop on QoS and security for wireless and mobile networks
Online anomaly detection for sensor systems: A simple and efficient approach
Performance Evaluation
Ontology and automation technique
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
Packet-level attestation (PLA): A framework for in-network sensor data reliability
ACM Transactions on Sensor Networks (TOSN)
On the complexity of energy efficient pairwise calibration in embedded sensors
Applied Soft Computing
Root cause detection in a service-oriented architecture
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Proceedings of the 10th ACM symposium on Performance evaluation of wireless ad hoc, sensor, & ubiquitous networks
Efficient event prewarning for sensor networks with multi microenvironments
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Proceedings of the Fourth Symposium on Information and Communication Technology
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Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? We focus on three types of transient faults, caused by faulty sensor readings that appear abnormal. To understand the prevalence of such faults, we first explore and characterize four qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict “normal” sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Time-series-analysis-based methods start with an a priori model for sensor readings. A sensor measurement is compared against its predicted value computed using time series forecasting to determine if it is faulty. Learning-based methods infer a model for the “normal” sensor readings using training data, and then statistically detect and identify classes of faults. We find that these four classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome to train, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. Time-series-analysis-based methods are more effective for detecting short duration faults than long duration ones, and incur more false positives than the other methods. We apply these techniques to four real-world sensor datasets and find that the prevalence of faults as well as their type varies with datasets. All four methods are qualitatively consistent in identifying sensor faults, lending credence to our observations. Our work is a first step towards automated online fault detection and classification.