Scheduling randomly-deployed heterogeneous video sensor nodes for reduced intrusion detection time
ICDCN'11 Proceedings of the 12th international conference on Distributed computing and networking
Coverage and activity management of wireless video sensor networks for surveillance applications
International Journal of Sensor Networks
ACM Transactions on Embedded Computing Systems (TECS)
Personal and Ubiquitous Computing
Efficient event detection by exploiting crowds
Proceedings of the 7th ACM international conference on Distributed event-based systems
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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.