Dynamic SIoT Network Status Prediction

Dong Hu, Shuai Lyu, Shih Yu Chang, Limei Peng, P. Ho
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Abstract

Prediction of social IoT (SIoT) data traffic is helpful in characterizing the complicated relationships for such as device-to-device, user-to-user, and user-to-device. One of the most popular traffic prediction methods in noisy environments is the Kalman filter (KF), which is extremely simple and general. Nevertheless, KF requires a dynamic traffic and measurement model as a priori, which introduces extra overhead and is often difficult to obtain in reality. In comparison, deep learning models with a Recurrent Neural Network (RNN) structure have been used extensively in modeling dynamic models evolving over time. On the other hand, the Content Adaptive Recurrent Unit (CARU) is an improvement of RNN that uses fewer parameters than the LSTM and GRU and thus is more promising in predicting the SIoT data traffic. This paper proposes the CARU-based extended Kalman filter (CARU-EKF) model, which is a new deep learning cell that utilizes CARU to predict extended Kalman filter (EKF) system parameters. Note that EKF is proper to predict nonlinear SIoT traffic in noisy environments. The proposed CARU-EKF can improve the performance of time-series data forecasting for nonlinear SIoT data traffic. Numerical experiments are conducted to evaluate the SIoT traffic prediction performance of the proposed CARU-EKF approach over two real datasets, i.e., IoT device traffic and wikipedia webpage visiting traffic. The proposed method shows better performance than existing prediction methods in terms of metrics of Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2).
动态SIoT网络状态预测
社交物联网(SIoT)数据流量预测有助于描述设备对设备、用户对用户和用户对设备等复杂关系。噪声环境下最流行的交通预测方法之一是卡尔曼滤波(KF),它非常简单和通用。然而,KF需要一个动态的流量和度量模型作为先验,这引入了额外的开销,并且在现实中往往难以获得。相比之下,具有循环神经网络(RNN)结构的深度学习模型已广泛用于建模随时间变化的动态模型。另一方面,内容自适应循环单元(CARU)是RNN的改进,它比LSTM和GRU使用更少的参数,因此在预测SIoT数据流量方面更有希望。本文提出了基于CARU的扩展卡尔曼滤波(CARU-EKF)模型,该模型是一种利用CARU来预测扩展卡尔曼滤波(EKF)系统参数的新型深度学习单元。请注意,EKF适用于预测噪声环境中的非线性SIoT流量。所提出的CARU-EKF可以提高非线性SIoT数据流量的时间序列数据预测性能。在两个真实数据集(即物联网设备流量和维基百科网页访问流量)上进行了数值实验,评估了所提出的CARU-EKF方法的SIoT流量预测性能。该方法在平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、均方根误差(RMSE)和决定系数(R2)等指标上均优于现有预测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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