针对无线传感网络(Wireless Sensor Network,WSN)固定周期数据传输导致的数据冗余和节点能耗高等问题,提出了一种基于同步预测的WSN自适应采样节能策略。通过在终端节点和协调器之间建立指数平滑同步预测模型,根据实际值和预测值的误差...针对无线传感网络(Wireless Sensor Network,WSN)固定周期数据传输导致的数据冗余和节点能耗高等问题,提出了一种基于同步预测的WSN自适应采样节能策略。通过在终端节点和协调器之间建立指数平滑同步预测模型,根据实际值和预测值的误差实现自适应通信;在同步预测模型基础上引入了传输控制协议(Transmission Control Protocol,TCP)拥塞控制思想,自适应地调整节点的采样间隔和睡眠时间,通过动态调整采样间隔,避免频繁的数据采集及传输,减少数据冗余。为验证节能性,基于ZigBee的室内甲醛监测系统平台进行仿真与实验。实验结果表明,在均方根误差(Root Mean Square Error,RMSE)为4.2×10^(-4)的情况下,相较于固定周期采样策略,所提出的策略能够节省能耗89.7%。对于提高WSN的能源效率具有参考价值。展开更多
This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate m...This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate mathematical models are challenging to establish or where system equations remain unknown.The Long Short-Term Memory(LSTM)neural network is trained using time series acquired from the desynchronization system states,subsequently predicting the lag synchronization transition.In the experiments,we focus on the Lorenz system with time-varying delayed coupling,studying the effects of coupling coefficients and time delays on lag synchronization,respectively.The results indicate that with appropriate training,the machine learning model can adeptly predict the lag synchronization occurrence and transition.This study not only enhances our comprehension of complex network synchronization behaviors but also underscores the potential and practical applications of machine learning in exploring nonlinear dynamic systems.展开更多
文摘针对无线传感网络(Wireless Sensor Network,WSN)固定周期数据传输导致的数据冗余和节点能耗高等问题,提出了一种基于同步预测的WSN自适应采样节能策略。通过在终端节点和协调器之间建立指数平滑同步预测模型,根据实际值和预测值的误差实现自适应通信;在同步预测模型基础上引入了传输控制协议(Transmission Control Protocol,TCP)拥塞控制思想,自适应地调整节点的采样间隔和睡眠时间,通过动态调整采样间隔,避免频繁的数据采集及传输,减少数据冗余。为验证节能性,基于ZigBee的室内甲醛监测系统平台进行仿真与实验。实验结果表明,在均方根误差(Root Mean Square Error,RMSE)为4.2×10^(-4)的情况下,相较于固定周期采样策略,所提出的策略能够节省能耗89.7%。对于提高WSN的能源效率具有参考价值。
基金supported by the National Natural Science Foundation of China(No.52174184)。
文摘This paper propose a comprehensive data-driven prediction framework based on machine learning methods to investigate the lag synchronization phenomenon in coupled chaotic systems,particularly in cases where accurate mathematical models are challenging to establish or where system equations remain unknown.The Long Short-Term Memory(LSTM)neural network is trained using time series acquired from the desynchronization system states,subsequently predicting the lag synchronization transition.In the experiments,we focus on the Lorenz system with time-varying delayed coupling,studying the effects of coupling coefficients and time delays on lag synchronization,respectively.The results indicate that with appropriate training,the machine learning model can adeptly predict the lag synchronization occurrence and transition.This study not only enhances our comprehension of complex network synchronization behaviors but also underscores the potential and practical applications of machine learning in exploring nonlinear dynamic systems.
