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首页> 外文期刊>International Journal of Distributed Sensor Networks >Mechanism of situation element acquisition based on deep auto-encoder network in wireless sensor networks
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Mechanism of situation element acquisition based on deep auto-encoder network in wireless sensor networks

机译：无线传感器网络中基于深度自动编码器网络的情境元素获取机制

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In order to reduce the time complexity of situation element acquisition and to cope with the low detection accuracy of small class samples caused by imbalanced class distribution of attack samples in wireless sensor networks, a situation element extraction mechanism based on deep auto-encoder network is proposed. In this mechanism, the deep auto-encoder network is introduced as basic classifier to identify data type. In hierarchical training of the auto-encoder, a training method based on cross-entropy loss function and back-propagation algorithm is proposed to overcome the problem of weights updating too slow by the traditional variance cost function, and the momentum factors are added to improve the convergence performance. Meanwhile, in the stage of fine-tuning and classification of the deep network, an active online sampling algorithm is proposed to select the sample online for updating the network weights, so as to eliminate redundancy of the total samples, balance the amounts of all sample types, and improve the classification accuracy of small sample. Through the simulation and analysis of the instance data, the scheme has a good accuracy of situation factors extraction.
机译：为了降低情境元素获取的时间复杂度，并解决无线传感器网络中攻击样本的类别分布不均衡引起的小类别样本检测精度低的问题，提出了一种基于深度自动编码器网络的情境元素提取机制。 。在这种机制中，引入了深度自动编码器网络作为识别数据类型的基本分类器。在自动编码器的分层训练中，提出了一种基于交叉熵损失函数和反向传播算法的训练方法，以克服传统方差代价函数权重更新太慢的问题，并增加了动量因子来改善收敛性能。同时，在深度网络的微调和分类阶段，提出了一种主动的在线采样算法，用于在线选择样本以更新网络权重，从而消除了总样本的冗余，平衡了所有样本的数量。类型，提高小样本的分类精度。通过实例数据的仿真分析，该方案具有较好的情境因素提取精度。

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