基于自适应广义回归神经网络的链路质量评估

为选择合适的链路质量参数，进一步提高链路质量评估的性能和泛化能力、降低时间复杂度，确定链路质量参数的备选集M-{CS}={μ,r,σ+2}，其中μ={μ-{lqi}, μ-{rssi},μ-{snr}}，r={r-{lqi},r-{rssi},r-{snr}}，σ+2={σ+2-{lqi},σ+2-{rssi},σ+2-{snr}}；提出包裹式链路质量参数选取算法，采用自适应广义回归神经网络(adaptive general regression neural network， AGRNN)评价各备选子集的重要性，选择链路质量参数；借助广义回归神经网络(general regression neural network， GRNN)在分类以及时间上的优势，提出基于AGRNN的链路质量评估模型，该模型为每个链路质量参数分配不同的光滑因子，采用误差反向传播的思想对其进行自适应修正；采用准确率、召回率、泛化误差和计算时间评价链路质量评估模型.室内、公园和公路场景下的{实验}表明:与基于多项式法、随机森林、支持向量分类器的链路质量评估模型相比，基于AGRNN的链路质量评估模型具有更优的评估性能和更好的泛化能力以及更低的时间复杂度.

Link Quality Estimator Based on Adaptive General Regression Neural Network

How to appropriately select link quality metrics and build a link quality estimation model with better performance, generalization capability, and lower time complexity is one of the key challenges in wireless sensor networks. We select M-{CS}={μ,r,σ+2}, where μ={μ-{lqi}, μ-{rssi},μ-{snr}},r={r-{lqi},r-{rssi},r-{snr}},σ+2={σ+2-{lqi},σ+2-{rssi},σ+2-{snr}}, as link quality metric candidate set. A link quality metric selection algorithm based on wrapper is proposed, which employs adaptive general regression neural network (AGRNN) to evaluate the importance of link quality metric candidate subsets so as to select link quality metrics. Taking advantages of general regression neural network (GRNN) in classification and time complexity, this paper proposes a link quality estimation model based on AGRNN which assigns different smoothing factors to each link quality metric and adaptively modifies them by using back propagation. Indexes, such as accuracy, recall, generalization error and computing time, are used to evaluate the link quality estimation models. In scenarios of the indoor scenario, the park scenario and the motorway scenario, the experimental results show that the proposed link quality estimation model can achieve better performance, generalization capability, and lower time complexity, compared with link quality estimation models based on polynomial, support vector classifier and random forest respectively.