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1a learning-based approach to confident event detection in heterogeneous sensor networks.
| Content Provider | CiteSeerX |
|---|---|
| Author | Keally, Matthew Nguyen, David T. |
| Abstract | Wireless sensor network applications, such as those for natural disaster warning, vehicular traffic monitor-ing, and surveillance, have stringent accuracy requirements for detecting or classifying events and demand long system lifetimes. Through quantitative study, we show that existing event detection approaches are challenged to explore the sensing capability of a deployed system and choose the right sensors to meet user-specified accuracy. Event detection systems are also challenged to provide a generic system that effi-ciently adapts to environmental dynamics and works easily with a range of applications, machine learning approaches, and sensor modalities. Consequently, we propose Watchdog, a modality-agnostic event detec-tion framework that clusters the right sensors to meet user-specified detection accuracy during runtime while significantly reducing energy consumption. Watchdog can use different machine learning techniques to learn the sensing capability of a heterogeneous sensor deployment and meet accuracy requirements. To address environmental dynamics and ensure energy savings, Watchdog wakes up and puts to sleep sensors as needed to meet user-specified accuracy. Through evaluation with real 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, energy savings, and environ-mental adaptability. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Confident Event Detection Learning-based Approach Heterogeneous Sensor Network Right Sensor Energy Saving Environmental Dynamic User-specified Accuracy Energy Consumption Iris Mote Wireless Sensor Network Application System Lifetime Event Detection System Quantitative Study Sensor Modality Building Traffic Sensing Capability Different Machine Real Vehicle Detection Trace Data Detection Accuracy Meet Accuracy Requirement Deployed System Superior Performance Modality-agnostic Event Detec-tion Framework Heterogeneous Sensor Deployment Natural Disaster Warning Machine Learning Approach Vehicular Traffic Monitor-ing Accuracy Requirement Generic System Event Detection Approach Environ-mental Adaptability User-specified Detection Accuracy |
| Content Type | Text |
