基于聚类模型预测的无线传感网自适应采样技术研究

该文利用无线传感网(WSNs)的数据空间相关性,提出一种基于数据梯度的聚类机制,聚类内簇头节点维护簇成员节点的数据时间域自回归(AR)预测模型,在聚类内范围实施基于预测模型的采样频率自适应算法.通过自适应优化调整采样频率,在保证数据采样精度的前提下减少了冗余数据传输,提高无线传感网的能效水平.该文提出的时间域采样频率调整算法综合考虑了感知数据的时空联合相关性特点,仿真结果验证了该文算法的性能优势.

Clustered Predictive Model Based Adaptive Sampling Techniques in Wireless Sensor Networks

According to the data spatial correlation of Wireless Sensor Networks (WSNs), this study proposes a clustering mechanism based on the data gradient. In the proposed clustering mechanism, the cluster head nodes maintain Auto Regressive (AR) prediction model of the sensory data within each cluster in the time domain. Moreover, the cluster head nodes adjust the temporal sampling frequency based on the implementation of above predicted adaptive algorithm model. By adjusting the temporal sampling frequency, the redundant data transmission is reduced as well as ensuring desired sampling accuracy, so as energy efficiency is improved. The temporal sampling frequency adjustment algorithm takes into account spatial and temporal combined correlation characteristics of sensory data. As a result, the simulation results demonstrate the performance benefits of the proposed algorithm.