Abstract: Data prediction model is a research hotspot in the field of data transmission in wireless sensor networks in recent years. As the monitoring environment becomes more complex and diversified, the data set collected by the sensor node is often accompanied by abnormal points. Most of the current prediction models do not filter the abnormal points. In order to effectively filter out abnormal points and improve the streamlining of data transmission and the accuracy of prediction, this paper proposes an exponentially weighted smoothing prediction model based on box-plot abnormal detection, and introduces a short-term chain ratio mechanism to determine emergencies. Experiments show that the model can effectively filter out abnormal points and determine emergencies under different data sets, smoothness coefficient changes and different dynamic thresholds. The streamlining of data transmission is increased by 5.8%, and the prediction accuracy is increased by 8.4%. Compared with the existing prediction models, it has better robustness and abnormal point processing ability.

Key words: box-plot, abnormal detection, exponentially weighted smoothing, short-term chain ratio