基于Markov模型低时延数据融合树算法

Low-delay data aggregation tree algorithm based on Markov model

数据融合技术可节省无线传感网络的资源,但也会增加额外的时延。为此,针对数据传输必须在指定时延约束下完成的特殊场景,提出基于Markov模型低时延数据融合树算法(MLDGT)。给出在时延约束下的构建数据融合树问题的形式化表述,此问题已证实为NP问题。引用Markov近似模型寻找次优解,进而获取低时延的数据融合树。通过仿真和比较分析MLDGT算法的有效性。实验结果表明,MLDGT算法能够降低数据融合时延。

The data aggregation technology can save the resources of wireless sensor networks(WSNs),and also increase the extra delay.Therefore,a Markov model-based low-delay data aggregation tree(MLDGT)algorithm is proposed under specified delay constraint scenario for data transmission.The formalized expression of the data aggregation tree problem is constructed under delay constraint.The data aggregation tree problem is proved to be NP.The Markov approximate model is introduced to find the close-to-optimal solution,so as to get the low-delay data integration tree.The effectiveness of the MLDGT algorithm is validated with simulation and comparison.The experimental results show that the MLDGT algorithm can reduce the data aggregation delay.