Distributed detection in Neural Network based multihop Wireless Sensor Network

Jabal Raval, B. Jagyasi
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引用次数: 8

Abstract

In this paper, a Neural Network based data aggregation approach to detect the binary events in a multi-hop Wireless Sensor Network has been proposed. We envision every node in a network as a unit of neuron which gets trained by using the neural network based back propagation algorithm. As compared to the LMS based Adaptive Weighted Aggregation scheme for tree network, the proposed Neural Network based wireless sensor network approach leads to a significant improvement in detection accuracy without much energy losses due to communication and computation overhead. We also compare the detection accuracy of the proposed Neural Network based scheme with that of the non-adaptive Bayesian approach which requires apriori knowledge of the sensor's performance indices.
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基于神经网络的多跳无线传感器网络分布式检测
提出了一种基于神经网络的数据聚合方法来检测多跳无线传感器网络中的二进制事件。我们将网络中的每个节点都看作是一个神经元单元,通过基于神经网络的反向传播算法对其进行训练。与基于LMS的树状网络自适应加权聚合方案相比,本文提出的基于神经网络的无线传感器网络方法在不造成通信和计算开销的能量损失的情况下,显著提高了检测精度。我们还比较了所提出的基于神经网络的方案与需要先验了解传感器性能指标的非自适应贝叶斯方法的检测精度。
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