DocumentCode :
3653849
Title :
CrowdSA ? towards adaptive and situation-driven crowd-sensing for disaster situation awareness
Author :
Andrea Salfinger;Werner Retschitzegger;Wieland Schwinger;Birgit Pr?ll
Author_Institution :
Dept. of Cooperative Information Systems, Johannes Kepler University Linz, Altenbergerstr. 69, 4040 Linz, Austria
fYear :
2015
fDate :
3/1/2015 12:00:00 AM
Firstpage :
14
Lastpage :
20
Abstract :
Disasters pose severe challenges on emergency responders, who need to appropriately interpret the situational picture and take adequate actions in order to save human lives. Whereas Information Fusion (IF) systems have proven their capability of supporting human operators in rapidly gaining Situation Awareness (SAW) in control center domains, disaster management presents novel challenges: Due to the unpredictability, uniqueness and large-scale dimensions of disasters, their situational pictures typically cannot be extensively captured by sensors - a substantial amount of situational information is delivered by human observers. The ubiquitous availability of social media on mobile devices enables humans to act as crowd sensors, as valuable crisis information can be broadcast over social media channels. Although various systems have been proposed which successfully demonstrate that such crowd-sensed information can be exploited for disaster management, current systems mostly lack means for automated reasoning on these information, as well as an integration with structured data obtained from other sensors. Therefore, in the present work we provide a first attempt towards comprehensively integrating social media-based crowd-sensing in SAWsystems: We contribute an architecture on an adaptive SAW framework exploiting both, traditionally sensed data as well as unstructured social media content, and present our initial solutions based on real-world case studies.
Keywords :
"Hurricanes","Sensors","Surface acoustic waves","Disaster management","Semantics","Monitoring","Ontologies"
Publisher :
ieee
Conference_Titel :
Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2015 IEEE International Inter-Disciplinary Conference on
ISSN :
2379-1667
Electronic_ISBN :
2379-1675
Type :
conf
DOI :
10.1109/COGSIMA.2015.7107969
Filename :
7107969
Link To Document :
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