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Computing Perception from Sensor Data

Title Computing Perception from Sensor Data
Author , , ,
Venue IEEE Sensors 2012
Year 2012
Resource Type ConferencePaper
Keyword(s) Semantic Sensor Web,Sensor Data,abstraction,Semantic Perception,Symbolic Aggregate Approximation,thematic, spatial and temporal features,knowledge-based model
Full Citation Payam Barnaghi, Frieder Ganz, Cory Henson, and Amit Sheth. Computing Perception from Sensor Data. In proceedings of the 2012 IEEE Sensors Conference, Taipei, Taiwan, October 28-31, 2012.
Abstract This paper describes a framework for perception creation from sensor data. We propose using data abstraction techniques, in particular Symbolic Aggregate Approximation (SAX), to analyse and create patterns from sensor data. The created patterns are then linked to semantic descriptions that define thematic, spatial and temporal features, providing highly granular abstract representation of the raw sensor data. This helps to reduce the size of the data that needs to be communicated from the sensor nodes to the gateways or highlevel processing components. We then discuss a method that uses abstract patterns created by SAX method and occurrences of different observations in a knowledge-based model to create perceptions from sensor data.
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