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.
Research on Adaptive Sampling Energy-saving Strategy for Wireless Sensor Network Based on Synchronous Prediction
In view of the problems of data redundancy and high node energy consumption caused by fixed-period data transmission in Wireless Sensor Network(WSN),an adaptive sampling energy-saving strategy based on synchronous prediction for WSN is proposed.An exponential smoothing synchronous prediction model is established between the terminal node and the coordinator,and adaptive communication is realized according to the error between the actual value and the predicted value.Based on the synchronous prediction model,Transmission Control Protol(TCP)congestion control idea is introduced to adjust adaptively the sampling interval and sleep time of nodes.By dynamically adjusting the sampling interval,frequent data acquisition and transmission are avoided and data redundancy is reduced.In order to verify the energy-saving effect,simulations and experiments are made on the indoor formaldehyde monitoring system platform based on ZigBee.The experimental results show that the proposed strategy can save energy by 89.7％compared with the fixed period sampling strategy when the Root Mean Square Error(RMSE)is 4.2×10-4.Therefore,this study is of great significance to improve the energy efficiency of WSN.
万方数据
维普
