A sliding window approach for dynamic event-region detection in sensor networks

Wang, Tsang-Yi; Yang, Ming-Hsun; Wu, Jwo-Yuh

doi:10.1109/infoseee.2014.6946278

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…These learningbased methods modeled history sensor data based on various classification models, e.g. Neural Network [18], Support Vector Machine [19], Markov Random Field [20] etc., and then classified real-time sensor data. Singh et al [19] proposed a distributed machine learning approach for event detection in two phases, base phase and meta phase.…”

Section: ) Learning-based Approachmentioning

confidence: 99%

“…It detects events in a distributed manner using Support Vector Machine (SVM) classification with polynomial kernel. Markov Random Field (MRF) [20] has been adopted to model the spatial relationship of neighboring nodes and improve the accuracy of event detection by considering the data of nearby sensors. The using of MRF and Markov Chain in [21] described the spatiotemporal context of events to further improve the accuracy of event detection.…”

Section: ) Learning-based Approachmentioning

confidence: 99%

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A Declarative Service-Based Method for Adaptive Aggregation of Sensor Streams

Zhang

Liu

et al. 2019

IEEE Access

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The dynamic interventions among sensor streams bring new challenges for the Internet of Things applications to derive meaningful information from a large amount of sensor streams. This paper proposes a declarative service-based method for capturing meaningful events from dynamic sensor streams based on our previous service abstraction, which can increase the value density of primitive sensor streams and raise the accessibility for higher level business applications. For aggregating related events from different sources, we set up declarative rules and compose cascaded events through service collaboration. In addition, for dealing with dynamic interventions among sensor streams, we propose a declarative rule evolution method to realize adaptive service collaboration at runtime. This paper also reports the tryout use of our method in the China Power Grid for detecting abnormal situations of power quality. Through a series of experiments based on real sensor data in the Power Grid, we verified that our method can capture events with high and steady precision.

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