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.

中文翻译：

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基于k-means聚类算法的无关数据消除，用于智能家居传感器网络中的高效数据聚合和人类活动分类

对于智能家居环境的成功运行，了解居住者的状态或活动非常重要。可以在地方或事物中部署和嵌入大量传感器。所有传感器节点都测量物理世界并将数据发送到基站进行处理。然而，处理从每个传感器节点收集到的所有数据会消耗大量的能量和时间。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。这种方法将基于簇头的算法嵌入到簇头中，以忽略来自基站的无关数据。每个房间中测量数据的模式可以聚类为当该房间发生人类活动时的活动模式和不发生人类活动时的稳定模式。基于k-means聚类算法方法的无关数据剔除可以减少55.94%与人类活动分类结果相似的原始数据。这项研究证明，所提出的方法可以通过仅提供来自可能发生人类活动的房间的数据来消除无意义的数据并智能地聚合数据。