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首页> 外文期刊>International Journal of Distributed Sensor Networks >Irrelevant data elimination based on a k-means clustering algorithm for efficient data aggregation and human activity classification in smart home sensor networks
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Irrelevant data elimination based on a k-means clustering algorithm for efficient data aggregation and human activity classification in smart home sensor networks

机译：基于K-Means聚类算法的无关数据消除，以实现智能家居传感器网络中的高效数据聚集和人类活动分类

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For the successful operation of smart home environments, it is important to know the state or activity of an occupant. A large number of sensors can be deployed and embedded in places or things. All sensor nodes measure the physical world and send data to the base station for processing. However, the processing of all collected data from every sensor node can consume significant energy and time. In order to enhance the sensor network in smart home applications, we propose the irrelevant data elimination based on k-means clustering algorithm to enhance data aggregation. This approach embeds the cluster head–based algorithm into cluster heads to omit irrelevant data from the base station. The pattern of measured data in each room can be clustered as an active pattern when human activity happens in that room and a stable pattern when human activity does not happen in the room. The irrelevant data elimination based on k-means clustering algorithm approach can reduce 55.94% of the original data with similar results in human activity classification. This study proves that the proposed approach can eliminate meaningless data and intelligently aggregate data by delivering only data from rooms in which human activity likely occurs.
机译：为了成功运行智能家庭环境，重要的是要了解乘员的状态或活动。大量传感器可以部署和嵌入在地方或事物中。所有传感器节点都测量物理世界并将数据发送到基站以进行处理。然而，每个传感器节点的所有收集数据的处理都可以消耗显着的能量和时间。为了增强智能家居应用中的传感器网络，我们提出了基于K-Means聚类算法的无关数据消除，以增强数据聚合。该方法将基于集群头的算法嵌入到集群头中以省略来自基站的无关数据。当在该房间内发生人类活动时，每个房间中测量数据的模式可以作为活动模式聚集成活性图案，当时人类活动不会发生在房间里的人类活动。基于K-Means聚类算法方法的无关数据消除可以减少55.94％的原始数据，其具有类似的人类活动分类结果。本研究证明，该方法可以通过仅来自可能发生的人类活动的房间的数据来消除无意义的数据和智能地聚合数据。

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