动态传感器网络节点定位技术的研究
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摘要
动态传感器网络作为无线传感器网络的一种,近年来被广泛的应用于许多领域中,如环境监测、动物跟踪、医疗健康、井下人员搜救等都依赖于传感器节点的移动性。移动的节点不仅提高了事件监测的灵活性,而且增强了无线传感器网络的自组织性和适应性。对于动态传感器网络而言,节点的位置信息是其各种应用的前提。节点的移动性使动态传感器网络的节点定位比静态传感器网络的节点定位更复杂。如何在动态条件下,对节点实现低功耗、低复杂度和高精度的定位是无线传感器网络定位面临的挑战之一。
本文以动态传感器网络节点定位技术为主要研究内容,分析并仿真研究了不同网络模型下的动态传感器网络节点定位问题,以及节点移动模型对定位算法的影响。本文的主要工作和创新点如下：
首先,本文针对未知节点静止、锚节点移动的动态传感器网络模型的定位问题展开了研究,提出了一种基于虚拟信标选择的移动锚节点定位算法。该算法从几何学的角度,对在测距误差存在的情况下,如何减少误差积累和确保定位误差最小的问题进行了研究,定量的分析了由节点间相对位置所引入的误差,并通过数学证明了当参与定位的三个虚拟信标的位置成等边三角形时定位误差最小的定理。依据这一定理,未知节点在定位过程中对虚拟信标进行合适的选取,获得了更准确的位置信息。
接着,本文研究并探讨了移动锚节点定位算法中的锚节点路径规划问题,提出了一种能自适应网络拓扑的动态路径规划方法。为使移动锚节点能根据网络中节点的分布情况进行移动,该算法对锚节点的移动方向进行了划分,并利用锚节点与未知节点之间的信息交互,获取锚节点当前通信范围内的节点的邻居节点数,使锚节点的移动路径尽量规划在邻居节点数最多的区域内,以此确定锚节点的移动方向。仿真结果显示,本文提出的动态路径规划方法能有效的避免无节点区域的遍历,特别适用于网络节点非均匀分布的应用场景。
其次,本文针对未知节点和锚节点均移动的动态传感器网络模型的定位问题进行了研究。蒙特卡洛定位算法是解决这类定位问题的重要方法,但蒙特卡洛定位算法为实现定位而进行的大量采样不仅降低了算法的执行效率,而且消耗了传感器节点的大量能量。针对这些缺点,本文提出了一种自适应采样的改进的蒙特卡洛定位算法。该算法首先使用未知节点接收到的一跳和二跳锚节点建立采样区,然后采用Kullback-Leibler距离作为衡量节点位置的后验概率密度分布的真值与估计值之间的误差限,依据采样区的大小计算出基于此误差限的最大采样次数,在采样区域内进行均匀的采样和过滤,最后利用未知节点的一跳邻居节点对样本的权值进行区分,计算出节点的估计位置。仿真结果显示,该算法能在保证节点定位精度的情况下,有效降低采样次数,减少计算开销。
由于受到网络规模和成本的限制,以及难以从实际场景中获得节点的移动轨迹,目前针对动态传感器网络节点定位算法的研究仍以仿真研究为主。节点移动模型作为动态传感器网络节点定位算法仿真研究的基础,对定位算法的性能分析具有重要的影响。通过研究发现,节点移动模型的参数设置及许多与时间相关的特性是造成定位算法性能评估不一致的主要原因。因此,本文最后研究并分析了节点移动模型对动态传感器网络节点定位算法性能的影响,提出了为了能准确衡量算法的性能,在定位算法的仿真研究中必须综合考虑定位算法的应用场景和节点移动模型的基本特性的观点,并举例加以了分析和说明。
Dynamic sensor networks as a kind of wireless sensor networks, in recent years have been widely used in many fields, such as environmental monitoring, animal tracking, health, mine rescue, all of these applications are dependent on the mobility of the nodes. The mobile node can not only improve the event monitoring, but also enhance the flexibility, self-organization and adaptability in wireless sensor networks. For the dynamic sensor networks, the node location information is the premise of its application. Compared with node localization in static sensor networks, the node localization in dynamic sensor networks is more complicated due to the mobility of nodes. How to achieve the localization with low power consumption, low complexity and high precision in dynamic conditions is one of the challenges facing the localization in wireless sensor networks.
The node localization technologies in dynamic sensor networks are the main topic in this dissertation. The dissertation explores the localization technologies in different dynamic sensor networks models and proposes some localization schemes. And then the dissertation presents a comparative simulation study of node mobility models in the performance evaluation of localization. The main work and innovation are as follows:
Firstly, aiming at the network model which consists of static unknown nodes and mobile anchors, the dissertation presents a localization scheme with mobile anchors based on virtual beacon selection. How to reduce the error accumulation and minimize localization error in the presence of ranging error is considered in the scheme. The scheme gives the quantitative analysis on the error introduced by the relative position between the nodes from the perspective of geometry. The theorem is proved which the localization error is minimal when the position of the three virtual beacons into an equilateral triangle. In order to obtain more accurate location information, the unknown node chooses the appropriate virtual beacons to compute its estimated position according to this theorem.
Secondly, the dissertation focuses on the path planning of mobile anchor and proposes a dynamic path planning method which is adaptive for network topologies. In order to make the mobile anchor moves according to the distribution of nodes in the networks, the moving direction of anchor is divided. The unknown nodes communicate with the anchor to obtain the number of the neighbor nodes. The anchor chooses the direction who owns the maximum number of neighbor nodes and moves. The simulation results show that the dynamic path planning method proposed in the dissertation can effectively avoid traversing the region without any node, particularly suitable for the networks with the non-uniformly distributed nodes.
Thirdly, the dissertation explores the localization technologies aiming at the network model which consists of mobile nodes and anchors. Monte Carlo localization algorithm is an important method to solve this kind of problem. However, a large number of samples are sampled to achieve the positioning. This not only reduced the efficiency of the algorithm, but also consumed a great deal of energy of nodes. Aiming at these shortcomings, the dissertation proposes an adaptive sampling improved Monte Carlo algorithm. In the algorithm, the sample area is built on the one-hop and two-hop anchors of the nodes. And then the Kullback-Leibler distance as error limits is adopted to measure the error between the true value and estimation of the posterior probability density distribution of the node position. According to the sampling area size, the maximum sampling attempts based on the error limit is calculated. After uniformly sampling and filtering in sample area, the weights are given to the samples according to the one-hop neighbors of the nodes, and the estimated position is the weighted means of the samples. The simulation results show that the algorithm can reduce the number of sampling and the computational expense in case of ensuring the accuracy.
Due to the network size and cost constraints, it is difficult to obtain the moving tracks of the nodes from the actual scene. Thus, at present the research on the node localization algorithm in dynamic sensor networks still adopts the simulation technologies. The node mobility model is the basis for simulation on node localization algorithm in dynamic sensor networks. Lastly, the dissertation presents a comparative simulation study of node mobility models in the performance evaluation of localization, and puts forward the viewpoint that the application scenarios of the localization and the basic characteristics of node mobility models should be taken into account in the simulation research of the node localization in order to accurately evaluate the performance of the algorithm. Moreover, the dissertation gives the analysis and explanation by examples.
本文以动态传感器网络节点定位技术为主要研究内容,分析并仿真研究了不同网络模型下的动态传感器网络节点定位问题,以及节点移动模型对定位算法的影响。本文的主要工作和创新点如下：
首先,本文针对未知节点静止、锚节点移动的动态传感器网络模型的定位问题展开了研究,提出了一种基于虚拟信标选择的移动锚节点定位算法。该算法从几何学的角度,对在测距误差存在的情况下,如何减少误差积累和确保定位误差最小的问题进行了研究,定量的分析了由节点间相对位置所引入的误差,并通过数学证明了当参与定位的三个虚拟信标的位置成等边三角形时定位误差最小的定理。依据这一定理,未知节点在定位过程中对虚拟信标进行合适的选取,获得了更准确的位置信息。
接着,本文研究并探讨了移动锚节点定位算法中的锚节点路径规划问题,提出了一种能自适应网络拓扑的动态路径规划方法。为使移动锚节点能根据网络中节点的分布情况进行移动,该算法对锚节点的移动方向进行了划分,并利用锚节点与未知节点之间的信息交互,获取锚节点当前通信范围内的节点的邻居节点数,使锚节点的移动路径尽量规划在邻居节点数最多的区域内,以此确定锚节点的移动方向。仿真结果显示,本文提出的动态路径规划方法能有效的避免无节点区域的遍历,特别适用于网络节点非均匀分布的应用场景。
其次,本文针对未知节点和锚节点均移动的动态传感器网络模型的定位问题进行了研究。蒙特卡洛定位算法是解决这类定位问题的重要方法,但蒙特卡洛定位算法为实现定位而进行的大量采样不仅降低了算法的执行效率,而且消耗了传感器节点的大量能量。针对这些缺点,本文提出了一种自适应采样的改进的蒙特卡洛定位算法。该算法首先使用未知节点接收到的一跳和二跳锚节点建立采样区,然后采用Kullback-Leibler距离作为衡量节点位置的后验概率密度分布的真值与估计值之间的误差限,依据采样区的大小计算出基于此误差限的最大采样次数,在采样区域内进行均匀的采样和过滤,最后利用未知节点的一跳邻居节点对样本的权值进行区分,计算出节点的估计位置。仿真结果显示,该算法能在保证节点定位精度的情况下,有效降低采样次数,减少计算开销。
由于受到网络规模和成本的限制,以及难以从实际场景中获得节点的移动轨迹,目前针对动态传感器网络节点定位算法的研究仍以仿真研究为主。节点移动模型作为动态传感器网络节点定位算法仿真研究的基础,对定位算法的性能分析具有重要的影响。通过研究发现,节点移动模型的参数设置及许多与时间相关的特性是造成定位算法性能评估不一致的主要原因。因此,本文最后研究并分析了节点移动模型对动态传感器网络节点定位算法性能的影响,提出了为了能准确衡量算法的性能,在定位算法的仿真研究中必须综合考虑定位算法的应用场景和节点移动模型的基本特性的观点,并举例加以了分析和说明。
Dynamic sensor networks as a kind of wireless sensor networks, in recent years have been widely used in many fields, such as environmental monitoring, animal tracking, health, mine rescue, all of these applications are dependent on the mobility of the nodes. The mobile node can not only improve the event monitoring, but also enhance the flexibility, self-organization and adaptability in wireless sensor networks. For the dynamic sensor networks, the node location information is the premise of its application. Compared with node localization in static sensor networks, the node localization in dynamic sensor networks is more complicated due to the mobility of nodes. How to achieve the localization with low power consumption, low complexity and high precision in dynamic conditions is one of the challenges facing the localization in wireless sensor networks.
The node localization technologies in dynamic sensor networks are the main topic in this dissertation. The dissertation explores the localization technologies in different dynamic sensor networks models and proposes some localization schemes. And then the dissertation presents a comparative simulation study of node mobility models in the performance evaluation of localization. The main work and innovation are as follows:
Firstly, aiming at the network model which consists of static unknown nodes and mobile anchors, the dissertation presents a localization scheme with mobile anchors based on virtual beacon selection. How to reduce the error accumulation and minimize localization error in the presence of ranging error is considered in the scheme. The scheme gives the quantitative analysis on the error introduced by the relative position between the nodes from the perspective of geometry. The theorem is proved which the localization error is minimal when the position of the three virtual beacons into an equilateral triangle. In order to obtain more accurate location information, the unknown node chooses the appropriate virtual beacons to compute its estimated position according to this theorem.
Secondly, the dissertation focuses on the path planning of mobile anchor and proposes a dynamic path planning method which is adaptive for network topologies. In order to make the mobile anchor moves according to the distribution of nodes in the networks, the moving direction of anchor is divided. The unknown nodes communicate with the anchor to obtain the number of the neighbor nodes. The anchor chooses the direction who owns the maximum number of neighbor nodes and moves. The simulation results show that the dynamic path planning method proposed in the dissertation can effectively avoid traversing the region without any node, particularly suitable for the networks with the non-uniformly distributed nodes.
Thirdly, the dissertation explores the localization technologies aiming at the network model which consists of mobile nodes and anchors. Monte Carlo localization algorithm is an important method to solve this kind of problem. However, a large number of samples are sampled to achieve the positioning. This not only reduced the efficiency of the algorithm, but also consumed a great deal of energy of nodes. Aiming at these shortcomings, the dissertation proposes an adaptive sampling improved Monte Carlo algorithm. In the algorithm, the sample area is built on the one-hop and two-hop anchors of the nodes. And then the Kullback-Leibler distance as error limits is adopted to measure the error between the true value and estimation of the posterior probability density distribution of the node position. According to the sampling area size, the maximum sampling attempts based on the error limit is calculated. After uniformly sampling and filtering in sample area, the weights are given to the samples according to the one-hop neighbors of the nodes, and the estimated position is the weighted means of the samples. The simulation results show that the algorithm can reduce the number of sampling and the computational expense in case of ensuring the accuracy.
Due to the network size and cost constraints, it is difficult to obtain the moving tracks of the nodes from the actual scene. Thus, at present the research on the node localization algorithm in dynamic sensor networks still adopts the simulation technologies. The node mobility model is the basis for simulation on node localization algorithm in dynamic sensor networks. Lastly, the dissertation presents a comparative simulation study of node mobility models in the performance evaluation of localization, and puts forward the viewpoint that the application scenarios of the localization and the basic characteristics of node mobility models should be taken into account in the simulation research of the node localization in order to accurately evaluate the performance of the algorithm. Moreover, the dissertation gives the analysis and explanation by examples.
引文
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