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[摘要]：Recently, Knowledge Discovery Process has proven to be a promising tool for extracting the behavioral patterns of sensor nodes, from wireless sensor networks. In this paper, we propose a new kind of behavioral pattern, named Target-based Association Rules (TARs). TARs aim to discover the correlation among a set of targets monitored by a wireless sensor network at a border area. The major application of the Target-based Rules is to predict the location (target) of a missed reported event. Different data preparation mechanisms for accumulating the data needed for extracting TARs have been proposed. We refer to these mechanisms as Al-Node, Schedule-Buffer, and Fused-Schedule-Buffer. Several experiment studies have been conducted to evaluate the performance of the three proposed data preparation mechanisms. Results show that the Fused-Schedule-Buffer scheme outperforms the selected schemes in terms of energy consumption. |
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