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• W2133992042 abstract "Many mission-critical applications such as military surveillance, human health monitoring, and obstacle detection in autonomous vehicles impose stringent requirements for event detection accuracy and demand long system lifetimes. Through quantitative study, we show that traditional approaches to event detection have difficulty meeting such requirements. Specifically, they cannot explore the detection capability of a deployed system and choose the right sensors, homogeneous or heterogeneous, to meet user specified detection accuracy. They also cannot dynamically adapt the detection capability to runtime observations to save energy. Therefore, we are motivated to propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user specified detection accuracy during runtime while significantly reducing energy consumption. Through evaluation with vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user specified detection accuracy and energy savings." @default.
• W2133992042 created "2016-06-24" @default.
• W2133992042 creator A5054814122 @default.
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• W2133992042 date "2010-04-01" @default.
• W2133992042 modified "2023-09-27" @default.
• W2133992042 title "Watchdog: Confident Event Detection in Heterogeneous Sensor Networks" @default.
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• W2133992042 doi "https://doi.org/10.1109/rtas.2010.15" @default.
• W2133992042 hasPublicationYear "2010" @default.
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