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2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)
Power-aware data analysis in sensor networks
Long Beach, CA, USA
March 01-March 06
ISBN: 978-1-4244-5445-7
Daniel Klan, Department of Computer Science&Automation, Ilmenau University of Technology, Germany
Katja Hose, Department of Computer Science&Automation, Ilmenau University of Technology, Germany
Marcel Karnstedt, Digital Enterprise Research Institute, National University of Ireland, Galway
Kai-Uwe Sattler, Department of Computer Science&Automation, Ilmenau University of Technology, Germany
Sensor networks have evolved to a powerful infrastructure component for event monitoring in many application scenarios. In addition to simple filter and aggregation operations, an important task in processing sensor data is data mining - the identification of relevant information and patterns. Limited capabilities of sensor nodes in terms of storage and processing capacity, battery lifetime, and communication demand a power-efficient, preferably sensor-local processing. In this paper, we present AnduIN, a system for developing, deploying, and running in-network data mining tasks. The system consists of a data stream processing engine, a library of operators for sensor-local processing, a box-and-arrow editor for specifying data mining tasks and deployment, a GUI providing the user with current information about the network and running queries, and an alerter notifying the user if a better query execution plan is available. At the demonstration site, we plan to show our system in action using burst detection as example application.
Citation:
Daniel Klan, Katja Hose, Marcel Karnstedt, Kai-Uwe Sattler, "Power-aware data analysis in sensor networks," icde, pp.1125-1128, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), 2010
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