Tolerating failures of continuous-valued sensors
ACM Transactions on Computer Systems (TOCS)
Localized algorithms in wireless ad-hoc networks: location discovery and sensor exposure
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Habitat monitoring with sensor networks
Communications of the ACM - Wireless sensor networks
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Efficient gathering of correlated data in sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
An energy-efficient querying framework in sensor networks for detecting node similarities
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
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
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
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Journal of Systems and Software
Elliptical anomalies in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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We develop a practical, distributed algorithm to detect events, identify measurement errors, and infer missing readings in ecological applications of wireless sensor networks. To address issues of non-stationarity in environmental data streams, each sensor-processor learns statistical distributions of differences between its readings and those of its neighbors, as well as between its current and previous measurements. Scalar physical quantities such as air temperature, soil moisture, and light flux naturally display a large degree of spatiotemporal coherence, which gives a spectrum of fluctuations between adjacent or consecutive measurements with small variances. This feature permits stable estimation over a small state space. The resulting probability distributions of differences, estimated online in real time, are then used in statistical significance tests to identify rare events. Utilizing the spatio-temporal distributed nature of the measurements across the network, these events are classified as single mode failures - usually corresponding to measurement errors at a single sensor - or common mode events. The event structure also allows the network to automatically attribute potential measurement errors to specific sensors and to correct them in real time via a combination of current measurements at neighboring nodes and the statistics of differences between them. Compared to methods that use Bayesian classification of raw data streams at each sensor, this algorithm is more storage-efficient, learns faster, and is more robust in the face of non-stationary phenomena. Field results from a wireless sensor network (Sensor Web) deployed at Sevilleta National Wildlife Refuge are presented.