混合无线传感网检测有害气体移动节点目标点选择及路径规划

发布时间：2018-04-15 22:36

本文选题：混合无线传感器网络 + 稀疏传感器网络　；参考：《中国地质大学(北京)》2017年博士论文

【摘要】：无线传感器网络因其数据采集实时性以及部署方式的灵活性在各领域有着广阔应用前景,如农业、工业、军事以及环境监测领域。利用无线传感器网络检测有害气体泄漏是其中一个研究热点。目前,传统无线传感器网络通常将密集的静态传感器节点部署在工业厂区中以获取较为准确的结果。然而实际场景中感知资源有限,工业厂区中通常使用稀疏网络来检测有害气体是否泄漏;而单纯使用稀疏网络又会导致研究区域感知空洞增多;另一方面,传统网络无法满足对研究区域中因环境变化而分布不均的有害气体中高浓度变化区域进行检测的需求。针对上述问题,本文提出如下解决方案:首先,利用部署在工业厂区中稀疏静态节点,检测出有害气体的内外边界,同时最大化节省感知资源;然后,利用基于泰森多边形的感知空洞修复方法,为混合无线传感器网络中的移动传感器节点寻找目标点,从而将因利用稀疏网络而产生的感知空洞均利用移动节点进行修复。其次,本文还提出了基于感知数据的移动传感器综合重要性目标点选取及路径规划。首先,利用静态节点的感知数据生成数据异常点,根据数据异常点的异常值和位置调节目标点的优先级。其次,利用完全连通有权有向图,根据目标点的优先级为混合无线传感器网络规划移动路径。最后,在实验部分提出了模拟数据产生的依据并通过实验分析说明本文算法的可行性。算法性能分析结果表明,当混合无线传感器网络中的静态节点数量取值为不同的时候,算法所到得到目标点所带来的综合重要性有所不同;文章也通过多次重复实验改变阈值的取值以使得移动节点的路径能够在保证优先访问高优先级目标的同时,在访问路径上消耗的时间也相对较低。
[Abstract]:Wireless sensor networks (WSN) have broad application prospects in many fields, such as agriculture, industry, military and environmental monitoring, because of its real-time data acquisition and flexibility of deployment.Detection of harmful gas leakage using wireless sensor networks is one of the hot research topics.At present, traditional wireless sensor networks usually deploy dense static sensor nodes in the industrial plant to obtain more accurate results.However, the perception resource is limited in the actual scene, the sparse network is usually used to detect whether the harmful gas is leaking in the industrial plant, and the pure use of the sparse network will lead to the increase of the perception void in the research area; on the other hand,The traditional network can not meet the need to detect the high concentration of harmful gases in the study area due to environmental changes.In order to solve the above problems, this paper proposes the following solutions: firstly, using sparse static nodes deployed in industrial plants to detect the inner and outer boundaries of harmful gases, and at the same time maximize the perceived resource saving.Based on Tyson polygon perceptual cavity repair method is used to find target points for mobile sensor nodes in hybrid wireless sensor networks so that the perceptual holes generated by sparse networks are repaired by mobile nodes.Secondly, this paper also proposes the target selection and path planning of synthetic importance of mobile sensors based on perceptual data.Firstly, the data outliers are generated by the perceptual data of the static nodes, and the priority of the target points is adjusted according to the outliers and the positions of the outliers.Secondly, the mobile path of the hybrid wireless sensor network is planned according to the priority of the target point by using the fully connected weighted directed graph.Finally, in the experimental part, the basis of the simulation data generation is put forward and the feasibility of the algorithm is proved by the experimental analysis.The results of performance analysis show that when the number of static nodes in hybrid wireless sensor networks is different, the importance of getting target points is different.This paper also changes the threshold value through repeated experiments so that the path of mobile nodes can guarantee priority access to high-priority targets while consuming relatively low time on the access path.
【学位授予单位】：中国地质大学(北京)
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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