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A framework for the automated generation of power-efficient classifiers for embedded sensor nodes
2007
Proceedings of the 5th international conference on Embedded networked sensor systems - SenSys '07
This paper presents a framework for power-efficient detection in embedded sensor systems. State detection is structured as a decision tree classifier that dynamically orders the activation and adjusts the sampling rate of the sensors (termed groggy wakeup), such that only the data necessary to determine the system state is collected at any given time. This classifier can be tuned to trade-off accuracy and power in a structured, parameterized fashion. An embedded instantiation of these
doi:10.1145/1322263.1322285
dblp:conf/sensys/BenbasatP07
fatcat:twvya3omwzamfhfqcgmjtnsqku