基于无线传感器网络的数据聚合算法研究

发布时间：2018-05-07 18:20

本文选题：无线传感器网络 + 数据聚合　；参考：《南京邮电大学》2017年硕士论文

【摘要】：无线传感器网络(Wireless Sensor Networks,WSNs)利用各种各样的传感器节点,实时采集和监测网络区域内的各种信息,并将这些信息通过无线网络发送到汇聚节点(sink)。因此,WSNs在环境监测、移动医疗、交通监测等诸多领域都具有非常广阔的应用前景。通常,无线传感器网络能量有限,节点的数据传输将消耗大量的能量,因此如何减少网络中的数据传输量,降低节点能量消耗,延长网络的寿命成为无线传感器网络中研究的一个重点。数据聚合是无线传感器网络数据处理的重要技术,通过对采集或者接收到的数据进行聚合处理,可以有效地去除冗余数据。本文重点研究基于时空相关性的WSNs数据聚合算法,出发点是为了减少网内数据量,节约节点的能量消耗,最后达到延长网络寿命的目的。首先针对节点数据的空间相关性,提出了基于空间自相关模型的数据聚合算法SMDA(Spatial auto-regression Model based Data Aggregation)。在SMDA中,簇头节点收集簇内未休眠节点的信息,接着采用节点调度算法调度节点休眠,并利用Delaunay三角剖分算法和空间自相关模型预测休眠节点的缺失数据,最后对所有数据进行聚合操作并发送给汇聚节点。在此基础之上,基于节点数据之间的时间相关性和空间相关性,提出了一种基于空间自相关模型和灰色模型的数据聚合算法SGDA(Spatial auto-regression model and Grey model based Data Aggregation)。SGDA考虑节点数据的时间和空间相关性,以误差绝对值之和最小为最优准则,建立组合预测模型,进一步减小了预测的误差。仿真实验表明,本文提出的算法能够很好的减少网内冗余数据、均衡节点能耗、延长网络寿命,并保证较高的数据精度。
[Abstract]:Wireless Sensor Networks (WSNs) uses a variety of sensor nodes to collect and monitor all kinds of information in the network area in real time, and send the information to the convergent node via wireless network. Therefore, WSNs have a broad application prospect in many fields, such as environmental monitoring, mobile medicine, traffic monitoring and so on. In general, wireless sensor networks have limited energy, and the data transmission of nodes will consume a lot of energy, so how to reduce the amount of data transmission in the network and reduce the energy consumption of nodes, Prolonging the lifetime of wireless sensor networks (WSN) has become an important issue in wireless sensor networks (WSN). Data aggregation is an important technology of data processing in wireless sensor networks. The redundant data can be removed effectively by aggregating the collected or received data. This paper focuses on the WSNs data aggregation algorithm based on spatio-temporal correlation, which aims at reducing the amount of data in the network, saving the energy consumption of nodes, and finally prolonging the network life. Firstly, aiming at the spatial correlation of node data, a spatial autocorrelation model based data aggregation algorithm SMDA(Spatial auto-regression Model based Data aggregation is proposed. In SMDA, the cluster head node collects the information of the non-dormant node in the cluster, then uses the node scheduling algorithm to schedule the node sleep, and uses the Delaunay triangulation algorithm and the spatial autocorrelation model to predict the missing data of the dormant node. Finally, all the data are aggregated and sent to the aggregation node. On this basis, based on the temporal and spatial correlation between node data, In this paper, a data aggregation algorithm based on spatial autocorrelation model and grey model is proposed. Considering the temporal and spatial correlation of node data, a combined prediction model is established using the minimum of absolute error as the optimal criterion. The error of prediction is further reduced. Simulation results show that the proposed algorithm can reduce redundant data, balance node energy consumption, prolong network life and ensure high data accuracy.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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