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TARGET ASSOCIATION RULES FOR POINT OF COVERAGE WSN
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Recently, Knowledge Discovery Process has proven to be a promising tool
for extracting behavioral patterns, regarding sensor nodes, from
wireless sensor networks. In this paper, we propose a new type of
behavioral patterns,which we refer to as Target-based Association Rules,
to discover the correlation among a set of targets monitored by a
network at a border region. Target-base Rules is an extension to a
recently proposed behavioral patterns named Sensor Association Rules.
The major application of Targetbased Rules is to predict the source of
future events. To report about the performance of our proposed knowledge
technique, an extensive set of simulation experiments have been
conducted to measure the performance ofthe network during the process
ofpreparing the data needed for generating Target-based Rules.
Knowledge Discovery (KD) in wireless sensor networks is a new field that is concerned with generating behavioral patterns regarding the sensor nodes. KD uses a special kind of data, collected to describe the behavior of the sensor nodes during their operational time. A mining technique is then applied to generate what is called behavioral patterns that capture the temporal relationships between the sensor nodes. Behavioral patterns provide a valuable information that can be used to make critical decisions to improve the performance and the Quality of Services of WSNs . Knowledge discovery in wireless sensor network is not well defined yet. However, it consists of steps similar to those introduced in the traditional database systems, with slight modifications to reflect the inherent limitations of wireless sensor networks. These steps include: knowledge definition, data preparation, and pattern generation
The major impacts of Sensor Association Rules that could bring benefit to many applications are the ability to predict sources of future events, and the ability to identify
sets of temporally correlated sensors. Sensor Association Rules were first introduced with a network topology that consisted of a set of sensor nodes deployed randomly, and the association was captured between all the nodes in the network
Knowledge Discovery (KD) in wireless sensor networks is a new field that is concerned with generating behavioral patterns regarding the sensor nodes. KD uses a special kind of data, collected to describe the behavior of the sensor nodes during their operational time. A mining technique is then applied to generate what is called behavioral patterns that capture the temporal relationships between the sensor nodes. Behavioral patterns provide a valuable information that can be used to make critical decisions to improve the performance and the Quality of Services of WSNs . Knowledge discovery in wireless sensor network is not well defined yet. However, it consists of steps similar to those introduced in the traditional database systems, with slight modifications to reflect the inherent limitations of wireless sensor networks. These steps include: knowledge definition, data preparation, and pattern generation
The major impacts of Sensor Association Rules that could bring benefit to many applications are the ability to predict sources of future events, and the ability to identify
sets of temporally correlated sensors. Sensor Association Rules were first introduced with a network topology that consisted of a set of sensor nodes deployed randomly, and the association was captured between all the nodes in the network
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