Path planning using a mobile anchor node based on trilateration in wireless sensor networks

In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system‐equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

Concentric Distributed Localization Based on the Tripodal Anchor Structure and Grid Scan for Wireless Sensor Networks

Achieving high accuracy with minimum reference nodes, anchor nodes, and computation and communication costs is a goal for the localization in wireless sensor networks. Targeting at this goal, a localization scheme called concentric distributed localization with the tripodal anchor structure and grid scan (CDL-TAGS) requiring two reference nodes and a few anchor nodes is proposed in this paper. Under the precondition that the system has randomly distributed normal sensor nodes, a tripodal anchor structure is first designed. With this structure, the localization process is started from the centroid node and then stretched outward to the farthest normal nodes. Based on the two best reference nodes, a virtual point is generated to serve as the third reference node. In the CDL-TAGS scheme, a grid scan algorithm is employed to estimate the position of a normal node. Finally, we show that the communication overhead and time and space complexities among sensor nodes for CDL-TAGS can be kept at a low level. In addition, CDL-TAGS can achieve better accuracy with minimum anchor nodes as compared to some closely related localization schemes in the literature through simulation results.     

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.     

Power‐aware range‐free wireless sensor network localization using neighbor distance distribution

In large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.     

一种改进的蒙特卡罗定位算法研究

针对移动无线传感器网络中节点随机运动的情况,蒙特卡罗定位(MCL)算法有较好的定位精度,但由于MCL方法严格过滤而进行的频繁重采样带来大量计算,加重了节点能量消耗,针对上述情况提出了基于接收信号强度(received signal strength,RSS)的蒙特卡罗定位算法,该算法利用锚节点之间的距离及其测得的移动节点的RSS值来校正移动节点与每个锚节点之间的权值,缩小了传统MCL算法的采样范围。仿真表明,该方法降低了蒙特卡罗方法的采样次数以及通信开销,同时提高了节点定位精度。     

基于TOA的传感器网络定位误差几何分布研究

无线传感器网络中,自身节点定位精度与确定该节点位置的锚节点的几何关系密切相关。分析了TOA算法的定位原理,引入了GDOP描述定位误差与锚节点群几何布局关系,并给出了基于测距的算法中GDOP的计算方法。通过场景分析,结合质心算法原理,提出锚节点群内点定位精度高,仿真验证了结论。     

传感器网络的粒子群优化定位算法

无线传感器网络定位问题是一个基于不同距离或路径测量值的优化问题。由于传统的节点定位算法采用最小二乘法求解非线性方程组时很容易受到测距误差的影响,为了提高节点的定位精度,将粒子群优化算法引入到传感器网络定位中,提出了一种传感器网络的粒子群优化定位算法。该算法利用未知节点接收到的锚节点的距离信息,通过迭代方法搜索未知节点位置。仿真结果表明,该算法有效地抑制了测距误差累积对定位精度的影响,提高了节点的定位精度。     

基于临近锚节点修正的DBSCAN聚类加权定位算法

为了提高无线传感器网络节点的定位精确度,给出一种基于临近锚节点修正(CAAN)的具有噪声的基于密度的聚类(DBSCAN)加权定位算法.首先,在未知节点通信范围内的锚节点中选择三个构成三角形,证明当未知节点处在此三角形外接圆圆心位置时定位误差最小,然后据此选择合适的锚节点,结合滤波后的接收信号强度指示(RSSI)值进行定...     

基于几何学的无线传感器网络定位算法

提出一种基于几何学的无线传感器网络(WSN)定位算法。把网络区域中的节点分为锚节点和未知节点,假设在定位空间中有n个锚节点,由于受到几何学的限制,实际可行的锚节点序列是有限的,因此利用一种几何方法判断锚节点间的位置关系,从而选取最优的锚节点序列,能够更精确地确定未知节点的位置,并且分析了待定位节点的邻居锚节点数量对定位精度的影响。仿真结果表明,与已有的APS(Ad-Hoc positioning system)定位算法相比,该算法可有效地降低平均定位误差和提高定位覆盖度。     

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.     

无线传感器网络中的鲁棒区域定位算法

大多数传统的方法并不能处理一些影响定位算法性能的因素,如各向相异的投放环境,不精确的锚节点位置以及带误差的距离测量。该文提出一种鲁棒的区域定位算法,通过建立一个全局约束集来处理如上所述的影响因素。使用可行解区域投影方法计算每个节点的可行地理区域,将传感器节点的真实位置限定于该区域中,同时利用非凸约束计算其存在的内部空洞。此外为了提高该方法的实用性,提出了一种基于分簇的分布式迭代算法。仿真结果表明算法受地理环境,测量误差等因素的影响较小,能适用于传感器网络应用。     

一种基于网络密度分簇的移动信标辅助定位方法

现有移动信标辅助定位算法未充分利用网络节点分布信息,存在移动路径过长及信标利用率较低等问题。该文把网络节点分簇、增量定位与移动信标辅助相结合,提出了一种基于网络密度分簇的移动信标辅助定位算法(MBL(ndc))。该算法选择核心密度较大的节点作簇头,采用基于密度可达性的分簇机制把整个网络划分为多个簇内密度相等的簇,并联合使用基于遗传算法的簇头全局路径规划和基于正六边形的簇内局部路径规划方法,得到信标的优化移动路径。当簇头及附近节点完成定位后,升级为信标,采用增量定位方式参与网络其它节点的定位。仿真结果表明,该算法定位精度与基于HILBERT路径的移动信标辅助定位算法相当,而路径长度不到后者的50％。     

一种新型无线传感器网络分布式定位算法

针对无线传感器网络(Wireless Sensor Networks,WSN)的低成本、低耗能以及准确定位的需求,提出了一种射频干涉与测量多普勒频偏相结合的节点定位方法。该方法中移动锚节点通过2次交叉运动,产生多普勒效应并与静止锚节点形成射频干涉场;未知节点通过测量自身低频干涉信号的瞬时频率的变化规律,获得定位相关信息进而实现节点定位。仿真实验结果表明该方法可以实现预期的定位,且定位精度较高;同时,定位算法简单,运算量小,能耗小,尤其适用于大范围分布的大量节点进行定位。     

Monte Carlo localization for mobile wireless sensor networks

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.     

一种基于迭代聚类的无线传感器网络定位算法

In wireless sensor networks, node localization is a fundamental middleware service. In this paper, a robust and accurate localization algorithm is proposed, which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes. Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max, etc.) in accuracy, scalability and gross error tolerance.     

Ranging error‐tolerable localization in wireless sensor networks with inaccurately positioned anchor nodes

Localization is essential for wireless sensor networks (WSNs). It is to determine the positions of sensor nodes based on incomplete mutual distance measurements. In this paper, to measure the accuracy of localization algorithms, a ranging error model for time of arrival (TOA) estimation is given, and the Cramer—Rao Bound (CRB) for the model is derived. Then an algorithm is proposed to deal with the case where (1) ranging error accumulation exists, and (2) some anchor nodes broadcast inaccurate/wrong location information. Specifically, we first present a ranging error‐tolerable topology reconstruction method without knowledge of anchor node locations. Then we propose a method to detect anchor nodes whose location information is inaccurate/wrong. Simulations demonstrate the effectiveness of our algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

信号强度和运动向量结合的无线传感器网络移动节点定位

无线传感器网络的定位是近年来无线传感器网络研究的重要课题.本文首先介绍了无线传感器网络的来源、重要性以及无线传感器网络定位的分类.然后提出了一种全新定位算法,信号强度和运动向量结合的无线传感器网络移动节点定位,简称SSMV算法,在外围布置四个锚节点,得用信号强度和未知节点在运动中向量的变化,对锚节点在内的未知节点进行定位,并对该算法进行了仿真和总结.通过与凸规划法进行比较,仿真结果表明,该算法有更高的定位精度.     

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。     

一种基于虚拟锚节点策略改进的Bounding Box定位算法

针对Bounding Box算法定位误差大、覆盖率低的缺点,提出了一种采用虚拟锚节点策略的改进定位算法。首先未知节点利用其通信范围内的锚节点进行定位;其次,已定位的节点根据升级策略有选择性的升级为虚拟锚节点;最后,无法定位的节点利用虚拟锚节点实现定位。另外,在离散网络模型的基础上,通过建立双半径网络节点模型从而进一步约束了未知节点的位置。理论分析及仿真结果均表明,该算法在显著提高定位覆盖率的同时,有效地提高了定位精度。     

Probabilistic Support Vector Machine Localization in Wireless Sensor Networks

Sensor networks play an important role in making the dream of ubiquitous computing a reality. With a variety of applications, sensor networks have the potential to influence everyone's life in the near future. However, there are a number of issues in deployment and exploitation of these networks that must be dealt with for sensor network applications to realize such potential. Localization of the sensor nodes, which is the subject of this paper, is one of the basic problems that must be solved for sensor networks to be effectively used. This paper proposes a probabilistic support vector machine (SVM)‐based method to gain a fairly accurate localization of sensor nodes. As opposed to many existing methods, our method assumes almost no extra equipment on the sensor nodes. Our experiments demonstrate that the probabilistic SVM method (PSVM) provides a significant improvement over existing localization methods, particularly in sparse networks and rough environments. In addition, a post processing step for PSVM, called attractive/repulsive potential field localization, is proposed, which provides even more improvement on the accuracy of the sensor node locations.