TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
On Distributed Fault-Tolerant Detection in Wireless Sensor Networks
IEEE Transactions on Computers
CollECT: Collaborative event detection and tracking in wireless heterogeneous sensor networks
Computer Communications
Sensor faults: Detection methods and prevalence in real-world datasets
ACM Transactions on Sensor Networks (TOSN)
Delay optimal event detection on ad hoc wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Outlier Detection Techniques for Wireless Sensor Networks: A Survey
IEEE Communications Surveys & Tutorials
DRAGON: Detection and Tracking of Dynamic Amorphous Events in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Distributed Fault-Tolerance for Event Detection Using Heterogeneous Wireless Sensor Networks
IEEE Transactions on Mobile Computing
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Early detecting the approaching events is the primary way of minimizing their damages in the sensor-based systems. The majority of existing approaches of event description and detection rely on using crisp raw sensory data, which requires large amount of data transmission as well as is memory-consuming, moreover, these approaches are only applicable to homogeneous sensor networks. This paper describes a novel efficient framework for event prewarning in sensor networks with multi microenvironments, which mainly includes a simple and practical data preprocessing method, Node-level Noteworthy Event (NNE) detection algorithm, event probability encodings of NNEs and two distributed Node-level Alert Event (NAE) detection algorithms. We demonstrate our algorithms by experimentally evaluating their performance in various scenarios using real and synthetic data. Our NAE detection algorithm by leveraging spatial correlation only requires a small amount of data transmission and can detect over 90% of NAEs with few false negatives.