关键词: AR（p）模型, 无线传感网络, 模糊隶属度函数, 预测精度, 数据通信

Abstract: Autoregressive AR（p） model is an effective method to reduce the frequency of data communication and decrease the energy consumption in Wireless Sensor Network（WSN）. In view of the problem that AR（p） model ignores the different influence of historical data in different periods on the predictive value in the modeling process and affects the prediction accuracy of prediction model and the frequency of data communication in WSN, an improved AR（p） prediction model which is known as FAR（p） is proposed. By specifying a fuzzy membership value for each historical data with a new designed fuzzy membership function introduced in AR（p）, it can achieve the forecasting of historical data “farther is more weight”, in addition, with the data sequence processed by two weighted average algorithm a new prediction model is built. Simulation results show that the improved model can effectively improve the prediction accuracy, reduce data communication and decrease energy consumption in WSN.

Key words: AR（p） model, Wireless Sensor Network（WSN）, fuzzy membership function, prediction accuracy, data communication