基于改进单锚节点的无线传感器网络节点定位算法

节点定位是无线传感器关键技术之一,针对固定多锚节点方法定位精度低的缺陷,为了提高无线传感器的定位精度,提出了一种基于改进单锚节点的无线传感器网络节点定位算法(SFOA-SVM)。首先采用单移动锚节点在无线传感器网络中移动,构建无线传感器定位模型的学习样本,然后采用SVM构建节点定位模型,并采用渔夫捕鱼算法模拟渔夫捕鱼行为找到最优SVM参数,最后采用仿真实验测试节点的定位性能。结果表明,相对于其它定位算法,SFOA-SVM提高了无线传感器节点的定位精度,具有一定的实际应用价值。     

一种利用移动无线传感器网络特性的定位算法

在研究移动无线传感器网络特性的基础上,借鉴蒙特卡洛算法思想,提出一种利用移动无线传感器网络特性进行定位的算法(EFL).EFL算法把移动无线传感器网络的移动特性和低秩特性作为节点定位的约束条件以提高节点定位的精度.仿真结果表明,采用EFI算法,节点的定位精度有较大提高.     

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.     

基于自适应采样的移动WSN定位算法

节点自定位是无线传感器网络的关键技术之一。当前对无线传感器网络定位的研究主要集中静态节点定位,移动无线传感器网络定位研究相对较少。研究了基于序列蒙特卡罗方法的移动无线传感器网络定位。针对蒙特卡罗定位采用固定样本数,计算量大的缺点,根据蒙特卡罗定位盒（MCB）算法的锚盒子大小动态设置样本数,提出一种自适应采样蒙特卡罗盒定位算法。仿真表明,该算法在保持定位精度的同时有效地减小了采样次数,节约了计算量。     

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.     

基于连通度和加权校正的移动节点定位算法

移动节点定位问题是无线传感器网络中的研究重点。针对移动节点定位误差大的问题,提出一种基于连通度和加权校正的移动节点定位算法。在未知节点移动过程中,根据节点间连通度大小选取参与定位的信标节点,利用加权校正方法修正RSSI测距信息,然后用最小二乘法对未知节点进行位置估计。仿真分析表明,节点通信半径和信标密度在一定范围内,该算法表现出良好的定位性能,定位精度明显提升。     

无线传感器网络定位技术及典型系统

无线传感器网络被评为是改变二十一世纪、改变未来世界的十大新兴技术之一。无线传感器网络节点能够实现自身定位是获知监测地点信息的前提,同时也是实现目标跟踪和移动目标定位的基础。本文阐述了当前的节点定位基本原理,并整理了几种典型的无线传感器网络定位系统和算法,分析了其优缺点,并指出了当前定位算法存在的共性问题。     

无线传感器网络节点A身定位算法综述

节点位置信息是无线传感器网络应用的基础。介绍了无线传感器网络节点定位的基本原理,论述了已提出的几种典型定位算法,并对其进行了分析与比较。在综合分析当前定位算法不足的基础上,指出了无线传感器网络节点定位算法的研究方向。     

无线传感器网络节点自身定位算法综述

节点位置信息是无线传感器网络应用的基础.介绍了无线传感器网络节点定位的基本原理,论述了已提出的几种典型定位算法,并对其进行了分析与比较.在综合分析当前定位算法不足的基础上,指出了无线传感器网络节点定位算法的研究方向.     

城市移动无线传感器网络协同定位算法

随着无线传感器网络研究和应用的发展,城市规模的无线传感器网络开始出现,然而,其大规模、低成本、移动性和节点稀疏性等特性都给定位带来了困难.基于城市移动无线传感器网络的一种典型应用,研究了不依赖全球定位系统的无线传感器网络的定位问题,在曼哈顿环概率移动模型的基础上设计定位算法,并从理论和仿真两方面分析了该算法的收敛性和稳定性.     

移动传感网中一种基于RSSI的机会主义路由设计

 本文针对移动无线传感网提出一种结合节点移动向量和接收信号强度指示值RSSI(Received Signal Strength Indicator)信息的机会主义路由OR-RSSI,利用Sink节点Beacon报文的RSSI信息建立并更新机会概率值,使用报文广播后所能到达的具有最大机会概率值的最佳节点进行存储转发,完成移动无线传感网信息收集.OR-RSSI是一种良好的后择路由,不以既存路径为基础,不需额外设备支持,具有报文成功传输率高、网络有效吞吐量大以及能耗低等优点.     

无线自组网节点定位算法综述

节点移动性使无线自组网络在军事、医学、环境保护等领域展现出广阔的应用前景。然而随着人们对移动节点位置信息的需求,节点定位问题也成为一个研究的热点问题,到目前为止,己有许多有关无线自组网的自定位系统和算法。在对这些定位算法进行分类的基础上,把节点定位过程分为4个基本步骤,着重分析阐述各类申具有代表性的定位算法的原理和各自特点,并提出一个对定位算-法I陛能评价的标准。     

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.     

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.     

Computational intelligence based localization of moving target nodes using single anchor node in wireless sensor networks

Wireless Sensor Networks (WSNs) have tremendous ability to interact and collect data from the physical world. The main challenges for WSNs regarding performance are data computation, prolong lifetime, routing, task scheduling, security, deployment and localization. In recent years, many Computational Intelligence (CI) based solutions for above mentioned challenges have been proposed to accomplish the desired level of performance in WSNs. Application of CI provides independent and robust solutions to ascertain accurate node position (2D/3D) with minimum hardware requirement (position finding device, i.e., GPS enabled device). The localization of static target nodes can be determined more accurately. However, in the case of moving target nodes, accurate position of each node in network is a challenging problem. In this paper, a novel concept of projecting virtual anchor nodes for localizing the moving target node is proposed using applications of Particle Swarm Intelligence, H-Best Particle Swarm Optimization, Biogeography Based Optimization and Firefly Algorithm separately. The proposed algorithms are implemented for range-based, distributed, non-collaborative and isotropic WSNs. Only single anchor node is used as a reference node to localize the moving target node in the network. Once a moving target node comes under the range of a anchor node, six virtual anchor nodes with same range are projected in a circle around the anchor node and two virtual anchor nodes (minimum three anchor nodes are required for 2D position) in surrounding (anchor and respective moving target node) are selected to find the 2D position. The performance based results on experimental mobile sensor network data demonstrate the effectiveness of the proposed algorithms by comparing the performance in terms of the number of nodes localized, localization accuracy and scalability. In proposed algorithms, problem of Line of Sight is minimized due to projection of virtual anchor nodes.     

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.     

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.     

Pascal’s triangle-based range-free localization for anisotropic wireless networks

This paper introduces a Pascal’s triangle model to draw the potential locations and their probabilities for a normal node given the hop counts to the anchors according to the extent of detour of the shortest paths. Based on our proposed model, a Pascal’s triangle-based localization (PTL) algorithm using local connectivity information is presented for anisotropic wireless networks with a small number of anchors. The superiority of the PTL algorithm has been validated over the state-of-the-art algorithms through MATLAB simulations. We have shown that compared to the other algorithms, the PTL algorithm achieves higher localization accuracy with even fewer anchors. We have also validated the performance of the PTL algorithm in a real environment.     

Analyzing localization errors in one-dimensional sensor networks

One-dimensional sensor networks can be found in many fields and demand node location information for various applications. Developing localization algorithms in one-dimensional sensor networks is trivial, due to the fact that existing localization algorithms developed for two- and three-dimensional sensor networks are applicable; nevertheless, analyzing the corresponding localization errors is non-trivial at all, because it is helpful to improving localization accuracy and designing sensor network applications. This paper deals with localization errors in distance-based multi-hop localization procedures of one-dimensional sensor networks through the Cramér-Rao lower bound (CRLB). We analyze the fundamental behaviors of localization errors and show that the localization error for a sensor is locally determined by network elements within a certain range of this sensor. Moreover, we break down the analysis of localization errors in a large-scale sensor network into the analysis in small-scale sensor networks, termed unit networks, in which tight upper and lower bounds on the CRLB can be established. Finally, we investigate two practical issues: the applicability of the analysis based on the CRLB and the optimal anchor placement.