Karl M. Goeschka
University of Applied Sciences Technikum Wien
Sensor nodeDistributed computingClassification schemeComputational intelligenceWireless sensor networkLimited resourcesNode levelComputer scienceCorrectnessAnomaly detection
1Publications
2H-index
0Citations
Publications 3
Newest
#1Dominik Widhalm (University of Applied Sciences Technikum Wien)H-Index: 4
#2Karl M. Goeschka (University of Applied Sciences Technikum Wien)H-Index: 2
Last. Wolfgang Kastner (TU Wien: Vienna University of Technology)H-Index: 24
view all 3 authors...
Source
#1Dominik WidhalmH-Index: 4
#2Karl M. GoeschkaH-Index: 2
Last. Wolfgang KastnerH-Index: 24
view all 3 authors...
In wireless sensor networks, the quality of the provided data is influenced by the properties of the sensor nodes. Often deployed in large numbers, they usually consist of low-cost components where failures are the norm, even more so in harsh outdoor environments. Current fault detection techniques, however, consider the sensor data alone and neglect vital information from the nodes’ hard- and software. As a consequence, they can not distinguish between rare data anomalies caused by proper event...
Source
#1Dominik Widhalm (University of Applied Sciences Technikum Wien)H-Index: 4
#2Karl M. Goeschka (University of Applied Sciences Technikum Wien)H-Index: 2
Last. Wolfgang Kastner (TU Wien: Vienna University of Technology)H-Index: 24
view all 3 authors...
Wireless sensor networks play an important role in today's world: When measuring physical conditions, the quality of the sensor readings ultimately impacts the quality of various data analytical services. To maintain data correctness and quality, run-time measures such as anomaly detection techniques are gaining significance. In particular, the detection of threatening node anomalies caused by sensor node faults has become a crucial task.The detection of faulty sensor nodes is a non-trivial task...
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