无线传感器网络的数据聚合研究

发布时间：2017-12-26 16:41

本文关键词：无线传感器网络的数据聚合研究　出处：《西安电子科技大学》2015年硕士论文　论文类型：学位论文

【摘要】：无线传感器网络是由随机分布在部署地区内的大量微型传感器节点组成,以无线通信方式构成的一个多跳的自组织网络系统,其目标是互相协作地感知、采集和处理部署地区内感知对象的信息,并且实时地发送给用户。由于无线传感器网络中的节点是密集分布在监测地区中,相邻节点收集到的数据具有较大的相关性,所以网络中存在冗余信息。另外,环境对传感器节点收集到的数据的准确性有较大影响,节点有可能收集到误差较大的数据。然而,传感器节点的能量是有限的,所以网络的生存周期有限,对冗余和误差数据进行适当的处理来延长网络寿命并提高数据的准确性是非常必要的。传感器节点可以直接从环境中收集数据,也可以从其他传感器节点接收数据。数据聚合指的是传感器节点对收集的数据进行适当的处理,去除冗余或者误差较大的信息,合理地把多个分组合并成为一组,并向下一跳节点进行转发。这样不仅可以减少网络中的数据传输量,减少Sink节点接收到的数据量,而且可以有效地提高监测结果的准确性,能更好地降低节点的能量消耗。数据聚合算法的基本思想是充分发挥传感器节点自身的数据处理能力,将其收集的大量原始数据筛选、聚合并且传输。采用高效的数据聚合算法来增加网络的寿命是当前的研究热点之一。基于非均匀簇的数据聚合是一种高效可靠的延长网络寿命的算法。本文提出了两种数据聚合算法:(1)针对同构的无线传感器网络,提出了基于非均匀网格成簇的数据聚合新算法。算法根据Sink节点的位置把网络划分为大小不等的网格,在每个网格中选取剩余能量最高的节点作为簇首节点,其余节点根据就近原则选择性的加入簇,进而形成基于Sink节点位置的非均匀簇。每个簇内的所有节点对收集到的数据进行格拉布斯预处理,采用自适应加权数据聚合算法将有效数据以单跳的形式传输到簇首节点。然后,簇首节点以多跳的方式给别的簇首节点或者Sink节点传输数据。(2)针对非同构的无线传感器网络,我们提出一种新的基于数据滤波的双层数据聚合算法。该算法与上一个算法使用相同的非均匀网格成簇方法,簇内的成员节点在传输数据之前根据统计模型进行滤波处理,删除误差较大的数据和冗余数据,簇内节点通过顺序加权数据聚合算法以单跳的方式给簇首节点发送数据,簇首节点以多跳的形式将聚合后的数据传输至Sink节点进行下一层次的数据聚合。我们将提出的算法与其他的数据聚合算法通过仿真进行了对比,结果显示所提出的数据聚合算法可以明显降低传感器节点的能耗,实现最大化的数据聚合,并且簇首节点的选取机制更有效地均衡了网络的能耗,减少了节点的死亡数目,进而有效地延长了传感器网络的寿命,同时也提高了数据的准确性和网络的鲁棒性。
[Abstract]:Wireless sensor network is composed by the random distribution in the area of deployment in a large number of micro sensor nodes, wireless communication mode to constitute a multi hop self-organizing network system, its goal is to collaborate perception, acquisition and processing of the deployment area perceived objects and real-time information sent to the user. Because the nodes in WSN are densely distributed in the monitoring area, the data collected by the neighboring nodes are highly correlated, so redundant information exists in the network. In addition, the environment has a great impact on the accuracy of the data collected by the sensor nodes, and the nodes may be able to collect data with larger error. However, the energy of the sensor nodes is limited, so the lifetime of the network is limited. It is very necessary to process the redundant and error data appropriately to prolong the network life and improve the accuracy of the data. Sensor nodes can collect data directly from the environment, or receive data from other sensor nodes. Data aggregation refers to the proper processing of data collected by sensor nodes, removing redundant or error information, reasonably merging multiple groups into one group and forwarding them to the next hop node. In this way, we can not only reduce data transmission in network, reduce the amount of data received by Sink nodes, but also effectively improve the accuracy of monitoring results, and better reduce the energy consumption of nodes. The basic idea of data aggregation algorithm is to give full play to the data processing ability of sensor nodes, and collect, collect and transmit a large amount of raw data collected by them. The use of efficient data aggregation algorithms to increase the lifetime of the network is one of the hotspots of current research. Data aggregation based on nonuniform clusters is an efficient and reliable algorithm for prolonging network life. In this paper, two kinds of data aggregation algorithms are proposed: (1) a new algorithm for data aggregation based on heterogeneous grid is proposed for isomorphic wireless sensor networks. According to the location of Sink nodes, the algorithm divides the network into grid of different sizes. In each grid, the nodes with the highest remaining energy are selected as cluster heads. The rest nodes join clusters selectively according to the proximity principle, and then form an uneven cluster based on Sink node location. All nodes in each cluster are preprocessed by grabbas, and the adaptive weighted data aggregation algorithm is applied to transmit effective data to cluster head nodes in a single hop form. Then, the cluster head node transmits data to other cluster head nodes or Sink nodes in a multi hop way. (2) for the non isomorphic wireless sensor networks, we propose a new double data aggregation algorithm based on data filtering. Non uniform grid clustering method and the algorithm on an algorithm using the same, member nodes within the cluster are filtered according to the statistical model in data transmission, delete the error data and redundant data, cluster nodes send the data to the cluster head nodes in single hop mode by sequentially weighted data aggregation algorithm, cluster head the nodes in a multi hop form to transfer data to the Sink node after polymerization to the next level of data aggregation. We will present the algorithm with other data aggregation algorithm by comparing the simulation results, the results show that the proposed data aggregation algorithm can significantly reduce the energy consumption of sensor nodes, realize the maximization of data aggregation, and the mechanism of selection of cluster head nodes more effectively balance the network energy consumption, reduce the number of dead nodes, and then prolong the lifetime of the sensor network, but also improve the robustness and accuracy of the data network.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP212.9;TN929.5

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