Big data streams are available across the growing heterogeneous wireless sensor networks, with characteristics of vast volume and dynamic transmission. Energy efficiency improvement in big data stream gathering is becoming a challenge. In this article, a self-adaptive gathering algorithm for multisource heterogeneous big data streams with sliding windows is proposed, which can improve the energy efficiency for data stream processing due to adaptively adjusting the window size based on the difference of data probability distribution between adjacent windows. In order to save the spatial correlation of a heterogeneous data stream, the Gaussian Bernoulli Matrix Variable Restricted Boltzmann Machine (GBMVRBM) is proposed to deal with the multi-source data separately, and then a joint layer is used to fuse the data features of different modalities. The probability distribution of sliding window data is obtained by the energy function of the GBMVRBM, and the Hoeffding boundary is adopted to ensure that the probability distribution variation between the windows can be detected in time. The algorithm is tested on the Clemson University Audio Visual Experiments database, and it can be concluded that the algorithm proposed in this article can not only detect the data change in time, but also expand the window size to process the data efficiently.

中文翻译：

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基于深度学习的异构无线传感器网络中节能数据流的自适应收集

大数据流可在不断增长的异构无线传感器网络中使用，具有大容量和动态传输的特点。在大数据流收集中提高能效正成为一项挑战。本文提出了一种带有滑动窗口的多源异构大数据流的自适应采集算法，该算法可以根据相邻窗口之间数据概率分布的差异自适应地调整窗口大小，从而提高数据流处理的能效。 。为了节省异构数据流的空间相关性，提出了高斯伯努利矩阵可变限制玻尔兹曼机（GBMVRBM）分别处理多源数据，然后使用联合层融合不同数据流的数据特征。方式。通过GBMVRBM的能量函数获得滑动窗口数据的概率分布，并采用霍夫丁边界来确保可以及时检测到窗口之间的概率分布变化。该算法在克莱姆森大学视听实验数据库上进行了测试，可以得出结论，本文提出的算法不仅可以及时检测数据变化，而且可以扩展窗口大小以有效地处理数据。