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基于稀疏密集阵传输机制的WSN数据传输汇聚算法
王先清1, 彭 成2
1.广东科学技术职业学院 大数据与人工智能学院,广东 广州 510640;2.新疆师范大学 物理与电子工程学院,新疆 乌鲁木齐 830054
摘要:
为了改善无线传感网(WSN)的数据传输汇聚能力,提出了基于稀疏密集阵传输机制的WSN数据传输汇聚算法。引入核生成函数,设计了一种新的传输矩阵,将簇头节点与sink节点之间连通程度及负载程度进行量化,以提升簇头节点传输效果的评估能力;采用特征向量按列排序并结合卷积算法降低簇头节点传输值,以有效减少簇头节点负载;采用树分解模式搜寻可用哈密尔顿回路,构建了基于路径分解优化机制的汇聚稳定方法;通过使用哈密尔顿寻址来优化叶子节点与根节点之间的数据链路,以增强簇头节点覆盖能力与提高数据传输过程的稳定性能。仿真实验表明,与当前常用的基于阈值筛选模糊分簇的WSN数据稳定汇聚算法和面向医疗应用的无线传感器网络多径数据传输方法相比,所提算法具有更为集中的传输报文集中度,以及更高的传输链路抖动控制能力和网络汇聚带宽。
关键词:  无线传感网  数据汇聚  稀疏密集阵传输  核生成函数  树分解  哈密尔顿回路
DOI:10.11805/TKYDA2019530
分类号:
基金项目:国家自然科学基金资助项目(61370229);广东省重大科技专项基金资助项目(2016B010109008);广东省自然科学基金资助项目(S2013010015178)
WSN data transmission aggregation algorithm based on sparse dense array transmission mechanism
WANG Xianqing1, PENG Cheng2
1.School of Big Data and Artificial Intelligence,Guangdong Polytechnic of Science and Technology College,Guangzhou Guangdong 510640,China;2.School of Physics and Electronic Engineering,Xinjiang Normal University,Urumqi Xinjiang 830054,China
Abstract:
In order to improve the data transmission convergence ability of Wireless Sensor Networks (WSNs), a data transmission and aggregation algorithm based on sparse dense array transmission mechanism is proposed. A new transmission matrix is designed by introducing the kernel generation function to quantify the connectivity and load between cluster head node and sink node for improving the evaluation ability of cluster head node transmission effect. And the eigenvectors are sorted by column and convolution algorithm to reduce the transmission value of cluster head nodes for effectively reducing the load of cluster head nodes. The tree decomposition mode is utilized to search available Hamiltonian circuits, and a convergence stability method based on path decomposition optimization mechanism is constructed. The Hamiltonian addressing mode is adopted to optimize the data link between leaf node and root node one by one so as to enhance the coverage ability of cluster head nodes and improve the stability performance of data transmission process. The simulation results show that, compared with the current common algorithm of a stable clustering for WSN data based on threshold filtering and fuzzy clustering, and the energy-saving and reliable multi-path data transmission in wireless sensor networks for medical applications, the proposed algorithm has higher concentration of message transmission, stronger control ability on transmission link jitter, and higher network bandwidth.
Key words:  Wireless Sensor Network  data aggregation  sparse dense array transmission  kernel generation function  tree decomposition  Hamilton loop

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