无线传感器网络节点自身定位算法

无线传感器网络（WSN）的许多应用都是基于节点的位置信息．本文从WSN的基于测距的定位算法和无需测距的定位算法对其定位算法进行详细的说明．并分析比较各定位算法的优缺点．最后还指出了WSN的自身定位问题的研究方向。     

无线传感器网络节点自身定位算法

无线传感器网络(WSN)的许多应用都是基于节点的位置信息.本文从WSN的基于测距的定位算法和无需测距的定位算法对其定位算法进行详细的说明.并分析比较各定位算法的优缺点.最后还指出了WSN的自身定位问题的研究方向.     

无线传感器网络的节点自定位算法

无线传感器网络（WSN）是由大量靠无线多跳方式通信的智能传感器节点构成的网络,围绕WSN出现了许多新的研究内容,节点定位是其中一个很重要的方面。本文综述了无线传感网络近年来的自定位算法,分别分析了各种算法的优缺点。最后还讨论了节点自定位算法所采取的一些安全措施。     

基于模糊逻辑的WSN节点自定位算法

对无线传感器定位方法进行了研究,为节约成本、提高算法实用性和准确性,提出一种基于RSS和模糊逻辑的WSN节点自定位方法。该方法中一个移动未知节点采集信标节点广播的自身定位信息及其对应的RSS信号,构成已知条件,结合对信标节点的六边形网格划分、RSS向量唯一性定理和模糊数学中的测度贴近度计算方法设计实现了基于模糊逻辑的WSN节点自定位算法。理论分析表明,该算法能够以较低的计算开销满足WSN节点常规精度的定位要求,具有较好的实用性和准确性。     

无线传感器网络中结合丢包率和RSSI的自适应区域定位算法

首先分析无线传感器网络(WSN)中基于RSSI(接收信号强度指示)的现有定位算法存在的不足,然后在不同时间和空间的大量实验数据基础上,结合丢包率和RSSI提出了针对实际环境的距离评估模型和建立在此模型基础上的无线传感器网络定位算法,以此作为对无线定位研究的一种新的探索.通过实验对算法性能进行评估,证明使用该算法的WSN能够正常定位并具有较高的定位精度和稳定性.     

无线传感器的安全定位多点验证协议的形式化方法研究

目前,无线传感器网络的定位的主要是目标是在敌对环境中不受干扰.由于无线传感器网络定位的主要应用都需要在安全的定位结果下才能正常工作,对无线传感器的定位主要研究是集中在能够正常定位的前提下,对安全定位研究较少.本文首先介绍多点验证协议,并用形式化方法对其距离验证协议展开研究,然后验证距离验证协议在WSN的SPINE安全定位算法中能够进行定位,并验证其安全性.     

中继神经元径向自动控制算法及在WSN节点定位中的应用

提出一种改进的中继神经元径向自动控制算法,提高对被控对象的不确定性和外部扰动的抑制能力,实现WSN节点的自组织定位.设计中继神经元径向控制律,把系统的初始化状态固定到中继神经元上,最后采用扩展卡尔曼滤波器监控WSN节点MIMO行为,实现对WSN节点的定位和数据监测.仿真实验表明,改进的自动控制算法能有效提高自动控制增益,保证在低信噪比条件下对WSN节点的准确定位,提高WSN网络稳定性.     

一种面向无线传感器网络的分布式定位算法

针对无线传感器网络(WSN)的水下应用,在原有同类算法研究的基础上,提出了一种分布式无信标节点的相对定位算法。该算法结合了最高节点度分簇算法和经典度量多维尺度定位的理论。理论分析和仿真实验表明,与SDGPSN定位算法相比,该定位算法提高了约10%的定位精度。     

基于RSSI的WSN定位算法的研究

为了加强对基于RSSI的WSN定位算法的研究,采用基本的RSSI算法和自由传播模型,建立RSSI分析系统,实现WSN节点的RSSI值的捕获、节点RSSI值的分类存储、RSSI的实时查看、对存储的节点RSSI元数据的处理和分析、绘制不同节点RSSI值和距离的统计分布图.系统综合运用RSSI定位算法、TOA定位算法和三边定位算法,将待测节点的理论坐标与实际坐标进行对比分析,改进待测参数,从而将定位精度提高12％.     

基于FOA改进MCB算法的无线传感网络节点定位精度分析

为了提高无线传感网络（WSN）定位精度，应用果蝇优化算法（fruit fly optimization algorithm,FOA）-蒙特卡洛锚盒（Monte Carlo anchor box,MCB）算法（简称“FOA改进MCB算法”）对WSN中移动节点定位，分析了移动速度和锚节点数量对定位精度的影响。研究结果表明：相较于MCB算法，FOA改进MCB算法具有更优的定位精度；定位误差随着节点移动速度的增加而增大；随着锚节点数量的增加，定位误差表现为降低的趋势。该研究对提高无线传感网络节点定位精度具有重要意义，易于实现推广。     

无线传感器网络的节点自定位技术

文章对无线传感器网络的节点定位机制与算法进行了介绍,并对基于测距的和不基于测距的两大类方法进行了分析对比.文章认为节点定位是无线传感器网络的一项关键技术,对于无线传感器网络的许多应用来说节点位置信息都是必须的基本信息,虽然目前已有不少节点定位技术,但仅仅是一些初步的研究成果,距离无线传感器网络的整体优化目标还很不够,需要继续深入研究开发,提出更多的高效算法,促进无线传感器网络进一步的普及应用.     

无线传感器网络节点定位技术

无线传感器网络（Wireless Sensor Network,WSN）在许多领域有广泛的应用,无线传感器网络中节点位置对无线传感器网络的应用有重要的影响,没有位置属性的信息是无价值的,定位技术是无线传感器网络的重要研究方向之一.依据测距和非测距的分类方法,介绍节点定位技术的基本原理和方法及当前的发展状况,最后对节点定位技术的发展方向作展望.     

Theoretic analysis of unique localization for wireless sensor networks

Node self-localization has become an essential requirement for realistic applications over wireless sensor networks (WSNs). Although many distributed localization algorithms have been proposed, fundamental theoretic analysis of unique localization is still in its early stage of development. This paper aims at a synthetic and homogeneous survey of the theoretical basis on WSN localization problem carried out thus far. Specifically, subsequent to establishing a technological context of relevant terms, we construct a graph and then a formation for each WSN to present current state-of-the-art by analyzing possible conditions for unique localization, as well as corresponding verification algorithms, by drawing on the powerful results from rigidity theory, distance geometry, geometric constraints in CAD, and combinatorial theory. We show that the unique localization problem is well understood in two-dimension, however, only partial analogous results are available in three-dimension.     

Performance of some metaheuristic algorithms for localization in wireless sensor networks

In this paper we propose two novel and computationally efficient metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) principles for locating the sensor nodes in a distributed wireless sensor network (WSN) environment. The WSN localization problem is formulated as a non‐linear optimization problem with mean squared range error resulting from noisy distance measurement as the objective function. Unlike gradient descent methods, both TS and PSO methods ensure minimization of the objective function without the solution being trapped into local optima. We further implement a refinement phase with error propagation control for improvement of the results. The performance of the proposed algorithms are compared with each other and also against simulated annealing based WSN localization. The effects of range measurement error, anchor node density and uncertainty in the anchor node position on localization performance are also studied through various simulations. The simulation results establish better accuracy, computational efficiency and convergence characteristics for TS and PSO methods. Further, the efficacy of the proposed methods is verified with data collected from an experimental sensor network reported in the literature. Copyright © 2008 John Wiley & Sons, Ltd.     

基于边框定界的WSN分布式全搜索定位算法

提出一种基于边框定界的WSN分布式全搜索定位算法。该算法通过节点测距得到邻居节点的坐标和距离信息,然后通过边框定界方法确定节点存在的位置区域,最后将位置区域网格化,并用全搜索方法在该区域搜索最佳估计点,最佳估计点的坐标即为节点的定位坐标。该算法应用到网络时需运行多轮,通过逐步求精得到节点的定位坐标。仿真实验表明该算法达到当前其他复杂定位算法的性能。     

Energy cost and error performance of range-aware, anchor-free localization algorithms

Many applications that use sensor data from a wireless sensor network (WSN) require corresponding node position information as well. Therefore, it is not surprising that a common figure of merit for localization algorithms is the accuracy of the position estimate produced. Similarly, the amount of communication required by a localization algorithm is often of paramount interest as well since it is common knowledge that communication expends the most energy in a WSN. However, localization algorithms seldom characterize their communication cost. Furthermore, when they do it is often merely qualitative and is typically described as “expensive”. For two types of range-aware, anchor-free localization algorithms we found the opposite to be true. Rather than being expensive, the communication costs were quite modest. So much so that we maintain range-aware, anchor-free localization algorithms should be chosen on the basis of the accuracy required by the intended application independent of the communication cost.In this paper, we examine the effect of node degree, node distribution, range error and network size on distance error and communication cost for both incremental and concurrent versions of range-aware, anchor-free algorithms. The concurrent algorithm is twice as accurate as the incremental, but less efficient. Furthermore, node degree influences the energy cost of the algorithms the most, but neither algorithm uses more than a surprisingly small 0.8% of a 560 mA h battery. This result indicates less energy efficient localization algorithms can be tolerated, especially if they provide better accuracy. Furthermore, if energy does need to be conserved, there is not much savings available within the localization algorithm and savings must be found in other areas such as the MAC protocol or routing algorithm.     

Wireless Sensor Network Localization Based on Cuckoo Search Algorithm

Many applications of wireless sensor networks (WSN) require information about the geographical location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field to perform sensing and acting task. The goal of localization is to assign geographical coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, the cuckoo search algorithm is implemented to estimate the sensor’s position. The proposed approach has been compared in terms of localization error with particle swarm optimization (PSO) and various variants of biogeography based optimization (BBO). The results show that our method outperforms the PSO and BBO variants which are recently used in the literature.     

无线传感器网络的一种低功耗拓扑控制算法DSPT

针对无线传感器网络环境下拓扑控制问题,提出一种基于最小成本路径的分布式拓扑控制算法,其基本思想是:针对无线传感器网络many-to-one的通讯模型,建立以Sink节点为根节点的拓扑控制树,使得整个网络的通讯成本最低,从而延长网络的生命周期,与传统Ad Hoc网络采用的最小生成树拓扑控制算法相比较,具有低功耗,算法时间复杂低,易于实现等特点。仿真结果表明,在节点稠密部署情况下,无线传感器网络的整体功耗比MST生成树降低25%,关键节点的功耗比MST生成树降低13%。     

WSN中基于稀疏观测和异步传输的分布式实时定位算法

针对无线传感器网络（WSN）中现有集中式多维标度（MDS-MAP）节点定位算法在定位精度和分布式方面的不足,提出一种基于稀疏观测和异步传输的分布式实时定位算法。首先在传统MDS-MAP算法中融入稀疏观测机制,使其能够更好地符合实际观测场景;然后提出一种异步传输序列,使节点能够分布式计算距离观测,并通过分布式计算结果给出位置估计;最后通过提出的位置估计精化操作减小估计误差,最终实现节点的精确定位。实验结果表明,该算法具有较高的定位精度。     

无线传感器网络定位算法研究进展

无线传感器网络作为一种全新的信息获取和处理技术,可以在广泛的领域内实现目标监测、信息采集和目标追踪等任务,节点定位问题则是许多应用的基础,是无线传感器网络的支撑技术之一。对基于测距定位算法和免于测距定位算法进行了分析对比,并对无锚节点这一新的节点定位技术做了介绍。最后对节点定位算法的优缺点作了总结,并对节点定位技术未来趋势进行展望。