Computing Perception from Sensor Data
Barnaghi, P, Ganz, F, Henson, C and Sheth, A (2012) Computing Perception from Sensor Data In: IEEE Sensors 2012, 2012-10-28 - 2012-10-31, Taipei, Taiwan.
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.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research|
|Date :||28 October 2012|
|Additional Information :||
Copyright 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
|Depositing User :||Symplectic Elements|
|Date Deposited :||30 Nov 2012 12:03|
|Last Modified :||09 Jun 2014 13:15|
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