Power efficient range-free localization algorithm for wireless sensor networks

Considering energy consumption, hardware requirements, and the need of high localization accuracy, we proposed a power efficient range-free localization algorithm for wireless sensor networks. In the proposed algorithm, anchor node communicates to unknown nodes only one time by which anchor nodes inform about their coordinates to unknown nodes. By calculating hop-size of anchor nodes at unknown nodes one complete communication between anchor node and unknown node is eliminated which drastically reduce the energy consumption of nodes. Further, unknown node refines estimated hop-size for better estimation of distance from the anchor nodes. Moreover, using average hop-size of anchor nodes, unknown node calculates distance from all anchor nodes. To reduce error propagation, involved in solving for location of unknown node, a new procedure is adopted. Further, unknown node upgrades its location by exploiting the obtained information in solving the system of equations. In mathematical analysis we prove that proposed algorithm has lesser propagation error than distance vector-hop (DV-Hop) and other considered improved DV-Hop algorithms. Simulation experiments show that our proposed algorithm has better localization performance, and is more computationally efficient than DV-Hop and other compared improved DV-Hop algorithms.     

An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks

In emerging sensor network applications, localization in wireless sensor network is a recent area of research. Requirement of its applications and availability of resources need feasible localization algorithm with lower cost and higher accuracy. In this paper, we propose an Advanced DV-Hop localization algorithm that reduces the localization error without requiring additional hardware and computational costs. The proposed algorithm uses the hop-size of the anchor (which knows its location) node, from which unknown node measures the distance. In the third step of Advanced DV-Hop algorithm, inherent error in the estimated distance between anchor and unknown node is reduced. To improve the localization accuracy, we use weighted least square algorithm. Furthermore, location of unknown nodes is refined by using extraneous information obtained by solving the equations. By mathematical analysis, we prove that Advanced DV-Hop algorithm has lesser correction factor in the distance between anchor and the unknown node compared with DV-Hop algorithm, improved DV-Hop algorithm (Chen et al. 2008) and improved DV-Hop algorithm (Chen et al. in IEICE Trans Fundam E91-A(8), 2008), which is cause of better location accuracy. Simulation results show that the performance of our proposed algorithm is superior to DV-Hop algorithm and improved DV-Hop algorithms in all considered scenarios.     

一种基于误差距离加权与跳段算法选择的遗传优化DV-Hop定位算法

针对Distance Vector-Hop (DV-Hop) 定位算法存在较大定位误差的问题,该文提出了一种基于误差距离加权与跳段算法选择的遗传优化DV-Hop定位算法,即WSGDV-Hop定位算法。改进算法用基于误差与距离的权值处理锚节点的平均每跳距离;根据判断的位置关系选择适合的跳段距离计算方法;用改进的遗传算法优化未知节点坐标。仿真结果表明,WSGDV-Hop定位算法的性能明显优于Distance Vector-Hop (DV-Hop) 定位算法,减小了节点定位误差、提高了算法定位精度。     

Novel DV-Hop localization algorithm for wireless sensor networks

Many improved DV-Hop localization algorithm have been proposed to enhance the localization accuracy of DV-Hop algorithm for wireless sensor networks. These proposed improvements of DV-Hop also have some drawbacks in terms of time and energy consumption. In this paper, we propose Novel DV-Hop localization algorithm that provides efficient localization with lesser communication cost without requiring additional hardware. The proposed algorithm completely eliminates communication from one of the steps by calculating hop-size at unknown nodes. It significantly reduces time and energy consumption, which is an important improvement over DV-Hop—based algorithms. The algorithm also uses improvement term to refine the hop-size of anchor nodes. Furthermore, unconstrained optimization is used to achieve better localization accuracy by minimizing the error terms (ranging error) in the estimated distance between anchor node and unknown node. Log-normal shadowing path loss model is used to simulate the algorithms in a more realistic environment. Simulation results show that the performance of our proposed algorithm is better when compared with DV-Hop algorithm and improved DV-Hop—based algorithms in all considered scenarios.     

无线传感器网络中DV-Hop定位算法的改进

无线传感器网络中基于无需测距的节点定位算法定位精度不高,一般应用在粗精度定位中。为了提高基于无需测距的DV-Hop算法定位精度,利用最小均方差准则改进算法,通过修改指数值精化平均每一跳距离,提出不同通信半径、不同锚节点覆盖率下的最佳指数值概念,并应用在一种锚节点均匀分布环境中,进一步提高定位精度。MTLAB仿真结果表明,在最佳指数值下,改进的算法在不同锚节点覆盖率、不同通信半径下能提高定位精度,同时不会增加节点能量消耗与硬件成本。     

WSN中基于最佳指数的加权DV-Hop算法

DV-Hop算法是一种低成本、低定位精度的无需测距定位算法,在粗精度定位中应用广泛。为提高DV-Hop算法定位精度,从减小锚节点的平均每一跳距离误差和减小未知节点平均每一跳校正值误差两方面考虑。首先,用最佳指数值下的公式计算锚节点平均每一跳距离。然后,将未知节点的校正值加权处理,使所有的锚节点根据与未知节点距离的远近影响校正值的大小。MATLAB实验证明,改进的基于最佳指数值下的加权DV-Hop算法比DV-Hop算法、加权DV-Hop、最佳指数值下DV-Hop算法定位精度分别提高2%左右、1.65%左右、1.15%左右,同时不会增加网络硬件成本。     

基于改进DV-Hop的无线传感器网络节点定位算法

针对DV-Hop距算法定位误差大的难题,提出一种改进离估计误差,并利用DV-Hop的传感器节点定位算法。首先修正知节点与信标节DV-Hop算法对节点进行定位;然后对进V-Hop算法定位误差行校正,最后在Matlab 2012平台上对算法性能进行仿真分析。仿真结果表明,本文算法可以较好地克服DV-Hop算法存在的不足,提高了传感器节点的定位精度。     

改进的DV-HOP无线传感器网络定位算法

为了提高无需测距的距离矢量（DV-Hop）定位算法对随机分布网络节点的定位精度。在分析了DV-Hop算法实现思想的基础上,提出了一种锚节点整体均匀分布优化方案,对该算法节点跳数进行优化。另一方面,在双通信半径算法基础上提出了一种三通信半径改进方法,使得两节点之间的跳数与距离的关系更接近线性规律,最后结合两种改进方法,进行整体仿真。仿真结果表明,锚节点均匀分布的三通信半径DV-Hop算法比双通信半径DV-Hop算法提高定位精度约为7%~8%。     

基于加权的DV-Hop算法在WSN中的应用与研究

定位在无线传感器网络中非常重要,在DV-Hop定位算法中,平均每跳距离的计算误差过大.提出了改进的DV-Hop定位算法,结合较少跳数范围内加权的思想求解平均每跳距离,再乘以跳数,使其结果更加接近真实值.MATLAB仿真结果显示,改进的DV-Hop算法定位在不需要增加硬件开销的基础上增加了定位精度,定位误差明显减少.     

一种具有连续跳数值的三维DV-Hop改进算法

设计精确的定位算法是无线传感器网络（Wireless Sensor Networks,WSNs）的研究热点.针对DV-Hop（Distance Vector-Hop）定位算法中节点间距离估计误差较大导致定位不精确的问题,提出了一种具有连续跳数值的三维DV-Hop改进算法.探究了邻居节点间的距离与相应节点位置和通信半径构成的相交球体体积之间的关系,提出了连续跳数值的定义,并通过参数修正给出了其计算方法.通过仿真实验探究了网络环境对参数的影响并确定了参数的取值,使用连续跳数值代替DV-Hop算法中的跳数来降低节点间距离估计误差.仿真实验表明,该算法在不增加算法复杂度以及额外硬件的情况下有效地降低了定位误差.     

A fast handover protocol for 6LoWPAN wireless mobile sensor networks

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.     

一种基于可移动锚节点的DV-HOP改进定位算法

当节点不均匀分布时,DV-Hop的定位精度较差。针对DV-Hop定位算法的缺陷,提出一种基于移动锚节点的改进DV-Hop定位算法。在网络中引入具有一定移动能力的锚节点,并构建锚节点之间的虚拟力模型,锚节点受到虚拟力作用发生移动,从而均匀的分布于整个网络,修正了DV-Hop对不均与分布网络适应性差的特点。仿真实验表明,与原始算法相比改进后的算法定位精度有较大提高。     

DV-Hop定位算法在随机传感器网络中的应用研究

DV-Hop节点定位算法是一种重要的与距离无关的定位算法。在各向同性的密集网络中,DV-Hop可以得到比较合理的定位精度,然而在随机分布的网络中,节点定位误差较大。该文根据DV-Hop算法定位过程,在平均每跳距离估计、未知节点到各参考节点之间距离的计算和节点位置估计方法等3个方面进行了改进,分析和仿真了不同改进措施和综合改进的定位性能。结果表明,与有关方法相比,该文提出的改进措施可极大地提高节点定位精度。此外,该文改进措施不改变DV-Hop算法的定位过程,因此不需要增加网络通信量和额外硬件支持,是理想的与距离无关算法。     

水下光无线传感器网络的连接性分析与定位技术

针对水下光无线传感器网络(UOWSN)节点的传输范围受限和间歇性连接的问题,利用多跳通信扩大传输范围来增强网络连接性,提出一种网络节点定位算法.首先,将UOWSN建模为三维(3D)随机缩放模型图,并根据网络节点数、通信范围以及光发散角推导了该模型下网络节点的连接性概率表达式;然后,利用接收信号强度(RSS)定位算法修正...     

一种无需测距的无线传感器网络定位算法研究

定位信息是在无线传感器网络许多应用中不可缺少的,并且越来越重要。DV-Hop是一种典型的无需测距的定位算法。通过对DV-Hop算法的理论分析,找出其产生误差的主要原因,提出了一种改进的DV-Hop定位算法。增加锚节点数量及减少每条平均距离误差,有效提高节点定位精度。不用额外的硬件支持能够得到更接近实际位置的估算位置。仿真结果表明,提出的改进算法性能比原来的算法显著提升。     

WSN节点定位算法改进研究

在无线传感网络(WSN)中,节点的定位是一个非常之关键的问题,而DV-Hop算法是节点定位诸多算法中最主要的一个算法,但它计算未知节点到锚节点的距离时存在些许问题,所以提出了一个新的计算节点间距离的方法,考虑到在未知节点到锚节点路径上三个连续的节点所形成的角度的影响,使得距离的计算更精确.通过仿真可以得知这个改进算法能有效地提高未知节点定位的覆盖率和精度.     

DV-Loc: a scalable localization protocol using Voronoi diagrams for wireless sensor networks [accepted from open call]

Localization systems have been identified as a key issue in the development and operation of wireless ssensor networks. DV-Hop, a wellknown localization algorithm, has recently been proposed for WSNs. Its basic idea relies on transforming the distance to all beacon nodes from hops to meters by using the computed average size of a hop. Despite its advantages, the DV-Hop algorithm has some limitations, mainly due to its high communication cost and energy consumption, which unfortunately limit its applicability to small or medium-sized sensor networks. The scalability issue of DV-Hop is a challenging problem that needs to be addressed. In this article we propose a novel localizationbased protocol and show how Voronoi diagrams can be used efficiently to scale a DV-Hop algorithm while maintaining and/or reducing further DV-Hop?s localization error. In our localization scheme, nodes can also be localized by their Voronoi cells. In order to evaluate the performance of our scheme, we present an extensive set of simulation experiments using ns-2. Our results clearly indicate that our proposed algorithm performs and scales better than DV-Hop.     

MANCL: a multi‐anchor nodes collaborative localization algorithm for underwater acoustic sensor networks

Localization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.     

Unconstrained Face Detection of Multiple Humans Present in the Video

Accurate and fast localization of randomly deployed sensor nodes is needed for many applications in wireless sensor networks. Localization also benefits in recognizing the geographically area where an event took place. There is no meaning of any event information without the knowledge of its location coordinates. DV-Hop is one of the main range free localization technique, which estimates the position of nodes using distance vector. Particle swarm optimization is suitable for the localization issues because of its fast computing speed and high precision. To further reduce the positioning error, the traditional DV-Hop localization algorithm based on single objective optimization algorithm is converted into a multi objective optimization algorithm. In our proposed scheme, we have considered six different single objective functions and three different multi objective functions. In this paper, a multi objective particle swarm optimization based DV-Hop localization is proposed in 3-dimensional wireless sensor networks. The proposed functions has been evaluated on the basis of computation time, average localization error and localization error variance. The simulation results show that our proposed multi objective function performs better as compared to traditional single objective function.

     

一种基于加权处理的无线传感器网络平均跳距离估计算法

定位技术是无线传感器网络的关键技术之一,传统DV-Hop定位算法只考虑了最近一个锚节点估计的平均跳距离值,而单个锚节点估计的平均跳距离值无法准确地反映网络的实际平均跳距离。本文提出了一种基于加权处理的平均跳距离估计算法,考虑多个锚节点估计的平均跳距离值,根据距离未知节点的跳数进行加权,使网络平均跳距离的估计更加准确,从而提高定位精度。仿真结果表明,与DV-Hop算法的平均跳距离估计算法相比,本文算法更准确地估计平均跳距离,降低了均方根误差,并提高了定位精度。