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A multi task boosting technique over sector based structure in wireless sensor network

机译:无线传感器网络中基于扇区结构的多任务提升技术

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With enormous use of sensor network, the sensor applications was enfeeble by large data movements across the network owing to the intrinsic characteristics of resource-constrained sensors. This paper emphasizes on two issues such as accuracy and communication overhead, data aggregation is an effective way to reduce communication issues at lower level, to take important decisions and accuracy at final aggregation result. Next a model is predict from the extracted data using supervised learning at the upper level thus reduce the communication effort, the high dimensional reduction of the data mining task, then improving the accuracy of the sensing task.
机译:随着传感器网络的大量使用,由于资源受限的传感器的固有特性,传感器应用在整个网络中的大量数据移动中变得无能为力。本文着重于准确性和通信开销这两个问题,数据聚合是减少底层通信问题,在最终聚合结果上做出重要决策和准确性的有效方法。接下来,在高层使用监督学习从提取的数据中预测一个模型,从而减少通信工作量,减少数据挖掘任务的高维度,从而提高感测任务的准确性。

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