基于流量密度的无线传感器数据融合方案

Scheme of Data Aggregation Based on Data Flow Density for Wireless Sensor Networks

由于无线传感器网络中节点的通信带宽和计算资源受限，使得高效的数据融合节能策略成为无线传感器网络研究热点技术之一。通过分析在一定时间内无线传感器网络进行数据收集的流量密度。给出一种高效的无线传感器网络数据融合算法。有效且变化幅度比较大的数据主要集中在相对的时间内，所需采样点就比较密集：反之，随机的变化幅度比较小的数据则是比较分散，所需的采样点就比较稀疏。在一定的数据收集时间内，根据数据流量密度找到1个有效收集中心点，距离中心点越近融合率越低，距离中心点越远数据融合率越高。仿真和性能分析结果表明该算法具有较高数据融合效率，可以有效节省网络结点能量，从而延长网络生命周期。

Due to the limitation of the communication bandwidth and computing resource in the nodes of wireless sensor networks, the efficiency data aggregation strategy of energy saving becomes one of the hottest technologies in wireless sensor networks. An efficiency data aggregation algorithm for wireless sensor networks is proposed by analyzing the data flow density in a certain period in which the network is going to make the operation of data collection. The data which is effective or have a large amplitude change is mainly concentrated in the relative time, and the sampling point required is more intensive. On the contrary, the data which is random or have a small amplitude change is relatively dispersed, and the sampling point required is comparatively sparse. And then an effective center point of time is determined according to the data flow density in a certain time for data collection. The closer the distance between the center point and the transmission time is, and the higher the data aggregation rate is. Conversely, the farther the distance is, the lower the data aggregation rate is. Simulation and performance analysis results demonstrate that the novel algorithm has a high efficiency of data aggregation to save the node energy effectively and prolong the lifetime of the network significantly.