Wireless sensor network is effective for data aggregation and transmission in IoT environment. Here, the sensor data often contain a significant amount of noises or redundancy exists, and thus, the data are aggregated to extract meaningful information and reduce the transmission cost. In this paper, a novel data aggregation scheme is proposed based on clustering of the nodes and extreme learning machine (ELM) which efficiently reduces redundant and erroneous data. Mahalanobis distance-based radial basis function is applied to the projection stage of the ELM to reduce the instability of the training process. Kalman filter is also used to filter the data at each sensor node before transmitted to the cluster head. Computer simulation with real datasets shows that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy of the data and energy efficiency of WSN.

中文翻译：

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WSN节点聚类和极限学习机的高效数据聚合

无线传感器网络对于物联网环境中的数据聚合和传输是有效的。在这里，传感器数据通常包含大量噪声或存在冗余，因此，数据被聚合以提取有意义的信息并降低传输成本。在本文中，提出了一种基于节点聚类和极限学习机（ELM）的新型数据聚合方案，可有效减少冗余和错误数据。将基于马氏距离的径向基函数应用于ELM的投影阶段，以减少训练过程的不稳定性。卡尔曼滤波器还用于在传输到簇头之前对每个传感器节点的数据进行过滤。