The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental...The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.展开更多
臭氧浓度的预测对于大气环境治理、空气质量改善等起到了重要的作用。本文提出了一种交互差分时空LSTM网络预测模型(ST-IDN)来挖掘臭氧浓度历史数据的时间相关性和空间相关性,并成功将其应用到网格化臭氧浓度数据预测上。在该模型中,首...臭氧浓度的预测对于大气环境治理、空气质量改善等起到了重要的作用。本文提出了一种交互差分时空LSTM网络预测模型(ST-IDN)来挖掘臭氧浓度历史数据的时间相关性和空间相关性,并成功将其应用到网格化臭氧浓度数据预测上。在该模型中,首先交互模块(IC)可以通过一系列的卷积操作来捕捉短期上下文信息,其次层融合模块(LF)可以融合不同层的空间信息来获得上一时刻丰富的空间信息,最后差分时空LSTM模块(DSTM)将捕捉到的时间信息和空间信息进行统一建模实现臭氧浓度预测。所构建模型分别与卷积LSTM网络(ConvLSTM)、预测循环神经网络(PredRNN)以及Memory in Memory网络(MIM)模型在河北省气象局提供的臭氧浓度数据上进行了对比分析,ST-IDN模型的平均绝对误差分别降低了19.836%、12.924%、7.506%。实验结果表明,所提出的模型能够提高臭氧浓度的预测精度。展开更多
基金Funding for this research is provided by the Natural Sciences and Engineering Research Council of Canada
文摘The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.
文摘臭氧浓度的预测对于大气环境治理、空气质量改善等起到了重要的作用。本文提出了一种交互差分时空LSTM网络预测模型(ST-IDN)来挖掘臭氧浓度历史数据的时间相关性和空间相关性,并成功将其应用到网格化臭氧浓度数据预测上。在该模型中,首先交互模块(IC)可以通过一系列的卷积操作来捕捉短期上下文信息,其次层融合模块(LF)可以融合不同层的空间信息来获得上一时刻丰富的空间信息,最后差分时空LSTM模块(DSTM)将捕捉到的时间信息和空间信息进行统一建模实现臭氧浓度预测。所构建模型分别与卷积LSTM网络(ConvLSTM)、预测循环神经网络(PredRNN)以及Memory in Memory网络(MIM)模型在河北省气象局提供的臭氧浓度数据上进行了对比分析,ST-IDN模型的平均绝对误差分别降低了19.836%、12.924%、7.506%。实验结果表明,所提出的模型能够提高臭氧浓度的预测精度。
