Due to the possibility of extensive and pervasive deployment of many tiny sensor devices in the area of interest, Wireless Sensor Networks (WSNs) result particularly suitable to detect significant events and to react accordingly in industrial and home scenarios. In this context, fuzzy inference systems for event detection in WSNs have proved to be accurate enough in treating imprecise sensory readings to decrease the number of false alarms. Besides reacting to event occurrences, the whole network may infer more information to enrich the event semantics resulting from reasoning processes carried out on the individual nodes. Contextual knowledge, including spatial and temporal relationships, as well as neighborhood confidence levels, can be used to improve the detection accuracy, but requires to extend the number of variables involved in the reasoning process.
|Titolo della pubblicazione ospite||IEEE International Conference on Emerging Technologies and Factory Automation, ETFA|
|Numero di pagine||4|
|Stato di pubblicazione||Published - 2016|
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