A symbolic distributed event detection scheme for Wireless Sensor Networks

Risultato della ricerca: Other

Abstract

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.
Lingua originaleEnglish
Pagine1-4
Numero di pagine4
Stato di pubblicazionePublished - 2016

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Wireless sensor networks
Fuzzy inference
Semantics
Sensors

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Computer Science Applications

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title = "A symbolic distributed event detection scheme for Wireless Sensor Networks",
abstract = "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.",
author = "Daniele Peri and {Lo Re}, Giuseppe and Salvatore Gaglio and Gloria Martorella and Salvatore Gaglio",
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T1 - A symbolic distributed event detection scheme for Wireless Sensor Networks

AU - Peri, Daniele

AU - Lo Re, Giuseppe

AU - Gaglio, Salvatore

AU - Martorella, Gloria

AU - Gaglio, Salvatore

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/220030

UR - http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000260

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EP - 4

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