无线传感器网络数据汇聚关键技术的研究

发布时间：2018-06-20 17:03

本文选题：无线传感器网络 + 数据汇聚　；参考：《东北大学》2014年硕士论文

【摘要】：无线传感器网络(WSN)由部署在监测区域的大量微型传感器节点组成,并通过无线通信方式形成的一种多跳自组织的网络系统,它能够采集监测区域数据,并通过汇聚节点将数据传送至客户端,从而达到对目标区域监测的目的。由于无线传感器节点能量严重受限,因此如何降低网络能耗,提高网络的生存周期已成为无线传感器网络研究的热点。研究表明,无线传感器网络数据汇聚算法对网络性能有很大影响,设计有效的数据汇聚算法可以延长网络生存周期。本文针对无线传感器网络数据汇聚算法展开深入的研究,在分析了现有汇聚算法优缺点的基础上,针对网络能耗不均衡的问题,从静止无线传感器网络和基于移动sink的无线传感器网络两方面进行了研究：(1)针对静止的无线传感器网络,提出了一种能量有效的分簇数据汇聚路由算法LEACH-EM。该算法在簇首选举时,将能量因素、网络拓扑结构作为选择簇首的依据,并加入对闽值公式的约束,使选出的簇首更合理且在数轮之后阈值不会过低：在簇形成阶段,对节点加入簇的方式进行分析,节点选择使网络通信能耗最小的簇头加入；在簇间数据传输阶段,提出一种改进权值的数据汇聚树的形成方法,通过多跳数据传输来均衡网络能耗。(2)针对基于移动sink的无线传感器网络,提出了基于RP节点的数据汇聚算法DCRP。该算法可以分为RP (Rendezvous Point)节点选举、网格内数据汇聚和移动sink路径优化三个阶段。由于RP节点的位置直接影响移动sink的路径,所以在RP节点选举阶段,通过划分网格并根据节点能量和网络拓扑选择RP节点,使选举出的RP节点数量合理且尽量靠拢；在网格内数据汇聚阶段,针对RP节点的位置特点,设计二级簇结构,减少远距离节点在网格内的数据传输能耗；最后利用模拟退火算法计算出遍历RP节点的最优路径,并将TSP问题转化为TSPN问题进一步缩短移动sink的数据收集时间。仿真结果表明,本文提出的针对静止无线传感器网络的数据汇聚路由算法能够较好的延长网络生存周期,针对基于移动sink的无线传感器网络提出的算法能够在均衡网络能耗的前提下,缩短网络延时。
[Abstract]:WSNs are composed of a large number of micro-sensor nodes deployed in the monitoring area, and a multi-hop self-organizing network system is formed by wireless communication. WSNs can collect and monitor regional data. The data is transmitted to the client through the convergent node to achieve the purpose of monitoring the target area. As the energy of wireless sensor nodes is severely limited, how to reduce network energy consumption and improve the lifetime of wireless sensor networks has become a hot topic in wireless sensor networks. The research shows that the data aggregation algorithm of wireless sensor networks has a great impact on the performance of the network, and the design of an effective data aggregation algorithm can prolong the lifetime of the network. Based on the analysis of the advantages and disadvantages of the existing convergence algorithms, this paper aims at the problem of uneven energy consumption in wireless sensor networks. This paper studies static wireless sensor networks and wireless sensor networks based on mobile sink. For static wireless sensor networks, an energy-efficient clustering data aggregation routing algorithm, LEACH-EM, is proposed. When the cluster head is elected, the energy factor and the network topology are taken as the basis of the cluster head selection, and the constraint on the threshold formula is added to make the selected cluster head more reasonable and the threshold value is not too low after several rounds: in the cluster formation stage, After analyzing the way that nodes join the cluster, the node selects the cluster head which makes the network communication energy consumption minimum. In the phase of data transmission between clusters, a method of data aggregation tree with improved weights is proposed. For wireless sensor networks based on mobile sink, a data aggregation algorithm based on RP nodes is proposed. The algorithm can be divided into three stages: Rendezvous Point node election, data aggregation in grid and mobile sink path optimization. As the location of RP nodes directly affects the path of mobile sink, in the election phase of RP nodes, the number of selected RP nodes is reasonable and as close as possible by dividing grids and selecting RP nodes according to node energy and network topology. In the phase of data convergence in the grid, according to the location characteristics of RP nodes, the secondary cluster structure is designed to reduce the data transmission energy consumption of remote nodes in the grid. Finally, the optimal path of traversing RP nodes is calculated by simulated annealing algorithm. The tsp problem is transformed into the tsp problem and the data collection time of mobile sink is further shortened. Simulation results show that the proposed data aggregation routing algorithm for static wireless sensor networks can extend the lifetime of the network. The proposed algorithm for wireless sensor networks based on mobile sink can shorten the network delay on the premise of equalizing network energy consumption.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5;TP212.9

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