传感器网络变权组合预测流量整形算法

针对现有流量整形算法在传感器网络应用上的不足，提出了一种新的流量整形算法。分析了传感器网络流量具有突发随机性以及时变不均衡性的原因，根据传感器网络流量的模糊性、随机性以及时变性统一建模，提出了变权组合预测流量整形算法（TSAV，Traffic Shaping Algorithm with Variable weight combination forecast），该算法通过逼近最优组合理论分配模糊AR预测与Kalman预测的组合权重，得到更为精确的预估流量值，提前规划整形速率从而平滑的输出分组流。实验表明，TSAV算法应用到传感器网络时能够准确预测流量，减少分组丢弃率的同时增大网络吞吐量，改善了传感器网络信息传输的QOS性能。 

Sensor Network Traffic Shaping Algorithm by Variable Weight Combination Forecast

Aiming at problems of the existing traffic shaping algorithms in applications of sensor network, a novel traffic shaping algorithm by variable weight combination forecast based is proposed. The reasons of sensor network traffic’s sudden randomness and time-varying unequilibrium are analyzed. By modeling the network traffic of fuzziness, randomness and time-varying, based on variable weight combination forecast a Traffic Shaping Algorithm with Variable weight combination forecast is put forward. To get more accurate forecast of traffic value, by optimal combination of two prediction method, Fuzzy AR and Kalman, traffic shaping algorithm is plan ahead shaping rate in order to smooth the output packet flow. Experiments show that the traffic shaping algorithm we proposed can predict the sensor network flow precisely, reduce the packet dropped rate and increase network throughput at the same time, so it can improves the sensor networks’ information transmission performance of QOS.