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Qualitative change detection using sensor networks based on connectivity information

Qualitative change detection using sensor networks based on connectivity information

Jiang, Jixiang, Worboys, Michael and Nittel, Silvia (2009) Qualitative change detection using sensor networks based on connectivity information. GeoInformatica, 15 (2). pp. 305-328. ISSN 1384-6175 (Print), 1573-7624 (Online) (doi:https://doi.org/10.1007/s10707-009-0097-0)

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Abstract

The research reported in this paper uses wireless sensor networks to provide salient information about spatially distributed dynamic fields, such as regional variations in temperature or concentration of a toxic gas. The focus is on deriving qualitative descriptions of salient changes to areas of high-activity that occur during the temporal evolution of the field. The changes reported include region merging or splitting, and hole formation or elimination. Such changes are formally characterized, and a distributed qualitative change reporting (QCR) approach is developed that detects the qualitative changes simply based on the connectivity between the sensor nodes without location information. The efficiency of the QCR approach is investigated using simulation experiments. The results show that the communication cost of the QCR approach in monitoring large-scale phenomena is an order of magnitude lower than that using the standard boundary-based data collection approach, where each node is assumed to have its location information.

Item Type: Article
Additional Information: [1] First published online: 22 October 2009. [2] Published in print: 1 April 2011. [3] Published as: GeoInformatica, (2011) Vol. 15, (2), pp. 305-328.
Uncontrolled Keywords: sensor network, topology, qualitative changes, spatio-temporal data
Subjects: Q Science > QA Mathematics > QA76 Computer software
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:24
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/10090

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