基于时间序列的自适应采样机制策略研究

Adaptive Sampling Algorithm Based on TCP Congestion Strategy

在传感器网络中,节点对同一事件采集的数据间存在一定的时空相关性。若有效利用数据相关性,动态调整采样间隔,则能够减少不必要的采样,从而相应地减少采样、计算、传输所耗费的能源,延长网络寿命。采用二次指数平滑法进行预测,参考TCP拥塞控制思想,快速调整采样间隔。实验证明,与普通算法相比,该算法能同时降低错误丢失率和采样率。

We presented the design of a novel adaptive sampling technique based on TCP congestion strategy,in which the temporal data correlations provide an indication of the prevailing environmental conditions and are used to adapt to the sensing rate of a sensor node.It uses irregular data series prediction to reduce sampling rate in combination with change detection to maintain data fidelity.The prediction method employs Wright＇s extension to Holt＇s method of exponential double sampling（EDS）coupled with a change detection mechanism based on exponentially weighted moving averages（EWMA）.The main advantages are that it does not require heavy computation,incurs low memory and communication overhead and the prediction model can be implemented with ease on resource constrained sensor nodes.