Abstract: Energy is limited in Wireless Sensor Network(WSN), and data aggregation is used in WSN to save energy by aggregating redundant data to reduce the data size being transmitted. But the aggregation cost itself is non-neglectable. Focusing on minimizing the total energy cost of the WSN, this paper improves the Adaptive Fusion Steiner Tree(AFST) by substituting the Dynamic Shortest Path Tree(DSPT) routing algorithm for the Shortest Path Tree(SPT) routing algorithm to adapt to the changes of network environment and data size. Experimental and analysis results show that using the DSPT routing algorithm for directly relayed data has a better performance in energy saving and efficiency than SPT routing algorithm when data size changes.

Key words: WSN, data aggregation, Dynamic Shortest Path Tree(DSPT), routing

摘要： 无线传感器网络能量有限，数据融合能通过合并冗余数据减少传输数据量，但其本身的代价不可忽略。针对该问题，研究数据融合代价和数据传输代价对数据融合路由的影响，在基于决策数据融合技术AFST中，对直传数据采用动态最短路径(DSPT)算法，动态识别网络环境和数据特征变化，以最小的代价调整路由。实验与分析结果表明，当网络结构发生变化时，DSPT算法比SPT算法效率更高、更节能。

关键词: 无线传感器网络, 数据融合, 动态最短路径树, 路由

CLC Number: 

• TP393