基于RSSI的优化加权质心定位算法研究

节点定位技术是无线传感器网络的关键技术之一。质心定位算法是指节点依靠无线传感器网络的连通性进行定位,定位误差较大。为了提高定位精度,鉴于质心定位算法受环境影响较小,基于RSSI的定位技术使用方便的特点,文中提出了基于RSSI的一种优化加权质心定位算法。通过RSSI测距,结合优化后的加权质心定位算法,确定节点位置。仿真结果表明,该算法降低了定位的平均误差,可以提高定位精度。     

基于RSSI的无线传感器网络距离修正定位算法

节点自身定位是无线传感器网络目标定位的基础。无线传感器网络节点定位算法包括基于距离和距离无关两类。其中基于RSSI的定位算法由于实现简单而被广泛使用,但RSSI方法的测距误差较大,从而影响了节点定位精度。提出了一种基于RSSI的无线传感器网络距离修正定位算法。该算法通过RSSI测距,计算近似质心的位置,以此为参考点进行距离修正,然后确定节点的位置。仿真结果表明该算法可以提高节点定位精度。     

基于RSSI的精确室内定位算法

无线传感器网络的关键问题是实现节点的精确定位。为了解决基于RSSI的无线传感器网络三角形质心定位算法在有些情况不适用的问题,本文提出一种新型的基于RSSI的精确室内定位算法,此算法提出了虚拟信标节点的概念并用此来修正未知节点位置。实验表明,该算法具有较高的定位精度,能满足大多数的应用场合,具有一定的实用价值。     

基于RSSI的无线传感器网络三角形质心定位算法

节点定位是无线传感器网络中的关键技术之一.基于RSSI的定位技术是现阶段研究的热点,为解决RSSI测量方法定位误差较大的问题,提出一种将RSSI测量方法与三角形质心算法相结合的新型定位算法,该算法用三角形质心算法减小RSSI的测量误差.仿真表明该算法比基于RSSI的三边测量法定位算法的定位精度有较大提高.     

一种基于RSSI的区域重叠质心室内定位算法

为提高基于无线传感网络的室内定位精度,分析基于测距和非测距室内定位算法的优缺点,以常规RSSI算法和质心定位算法为基础,提出了一种基于RSSI的区域重叠质心定位算法.算法通过建立信号传输模型,在未知节点接收信标节点位置信息的重叠区域运用质心算法进行定位.仿真结果表明,与普通质心算法相比,该算法定位效果更加精确.     

基于RSSI多边定位误差的加权质心定位算法

为了优化无线传感器网络(Wireless Sensor Networks, WSN)中的定位算法, 提高节点定位精度, 提出一种基于多边定位误差的加权质心算法。分析了无线电的路径损耗模型, 建立基于信号接收强度(Received Signal Strength Indicator, RSSI)和距离关系的对数拟合测距公式, 给出了求解未知节点坐标的多边定位法和位置估算模型。多组数据定位后, 以定位误差值的倒数作为权值, 改进传统的质心算法, 并讨论了参考点个数的选取与误差的关系。实验表明: 改进后的加权质心比传统质心定位精度进一步提高, 选择4~5个参考节点具有良好的定位效果。     

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。     

一种新型三维传感器网络质心定位算法

现有的二维质心算法不能有效应用于三维无线传感器网络,针对这一问题,提出了基于虚拟节点的三维质心定位算法(Virtual centroid-3D).该算法将二维质心算法和三维网络模型相结合,在此基础上求出包含未知节点的四面体并引入虚拟节点的概念.运用这一方法,在设置相同的锚节点前提下,可以提供更多的已知节点,从而提高节点的定位精度.仿真结果表明该算法可显著减小定位误差,并能够高效地适应节点分布不均的三维网络环境.     

基于EKF的无线传感器网络移动信标定位算法

针对无线传感器网络节点定位精度较低的问题,提出一种基于扩展卡尔曼滤波的移动信标节点定位算法。该算法采用等距三重优化覆盖思想确定虚拟信标分布,利用蚁群算法获取最优遍历路径,同时引入扩展卡尔曼滤波算法以提高节点定位精度。通过对节点通信半径、虚拟信标数目、路径长度、迭代次数等参数分别进行仿真验证,结果表明本文算法定位精度明显优于普通质心定位算法,同时该算法在提高网络覆盖度、降低网络成本等方面也有较大优势。     

改进质心算法的节点自定位研究

质心定位算法是指无线传感器网络节点仅基于节点间连通性进行自定位,其定位结果误差较大,但该算法思想简单,能够节约有限的节点能量.为了提高节点自定位精度,且能够保留算法简单的优越性,在此采用极大似然估计法测距模型对节点定位的误差值进行修正;极大似然估计法能够对节点问的测量距离和估算距离之间的差值进行计算.结果表明,经过修正的节点坐标有较高的精确度.     

Localization for mobile target in wireless sensor networks

A precise localization for mobile target in wireless sensor networks is presented in this letter,where a geometrical relationship is explored to improve the location estimation for mobile target,instead of a simple centroid approach.The equations of location compensation algorithm for mobile target are derived based on linear trajectory prediction and sensor selective activation.The results based on extensive simulation experiments show that the compensation algorithm gets better performance in metrics of quality of tracking and energy efficiency with the change of sensor sensing range,the ratio of sensing range and sensor activation range,and the data sampling rate than traditional methods,which means our proposing can achieve better quality-energy tradeoff for mobile target in wireless sensor networks.     

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。     

Distributed localization using mobile beacons in wireless sensor networks

A new distributed node localization algorithm named mobile beacons-improved particle filter (MB-IPF) was proposed. In the algorithm, the mobile nodes equipped with globe position system (GPS) move around in the wireless sensor network (WSN) field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages. Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo (MCMC) method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.     

Reference node placement and selection algorithm based on trilateration for indoor sensor networks

The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as two‐phase positioning (TPP) algorithm, are too complicated to be implemented and they cannot provide the real‐time localization of the mobile node. We analyze the localization error, which is produced when one tries to estimate the mobile node using trilateration method in the localization process. We draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. Therefore, we improve the TPP algorithm and propose reference node selection algorithm based on trilateration (RNST), which can provide real‐time localization service for the mobile nodes. Our proposed algorithm is verified by the simulation experiment. Based on the analysis of the acquired data and comparison with that of the TPP algorithm, we conclude that our algorithm can meet real‐time localization requirement of the mobile nodes in an indoor environment, and make the localization error less than that of the traditional algorithm; therefore our proposed algorithm can effectively solve the real‐time localization problem of the mobile nodes in indoor sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.     

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

The localization of the sensor nodes is a fundamental problem in wireless sensor networks. There are a lot of different kinds of solutions in the literature. Some of them use external devices like GPS, while others use special hardware or implicit parameters in wireless communications.In applications like wildlife localization in a natural environment, where the power available and the weight are big restrictions, the use of hungry energy devices like GPS or hardware that add extra weight like mobile directional antenna is not a good solution.Due to these reasons it would be better to use the localization’s implicit characteristics in communications, such as connectivity, number of hops or RSSI. The measurement related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons’ transmissions between the devices.In the current study, a novel tracking distributed method, called LIS, for localization of the sensor nodes using moving devices in a network of static nodes, which have no additional hardware requirements is proposed.The position is obtained with the combination of two algorithms; one based on a local node using a fuzzy system to obtain a partial solution and the other based on a centralized method which merges all the partial solutions. The centralized algorithm is based on the calculation of the centroid of the partial solutions.Advantages of using fuzzy system versus the classical Centroid Localization (CL) algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for testing localization algorithms.With this simulator, it is demonstrated that the proposed method obtains less localization errors and better accuracy than the centroid algorithm.     

Node distribution-based localization for large-scale wireless sensor networks

Distributed localization algorithms are required for large-scale wireless sensor network applications. In this paper, we introduce an efficient algorithm, termed node distribution-based localization (NDBL), which emphasizes simple refinement and low system-load for low-cost and low-rate wireless sensors. Each node adaptively chooses neighboring nodes, updates its position estimate by minimizing a local cost-function, and then passes this updated position to neighboring nodes. This update process uses a node distribution that has the same density per unit area as large-scale networks. Neighbor nodes are selected from the range in which the strength of received signals is greater than an experimentally based threshold. Based on results of a MATLAB simulation, the proposed algorithm was more accurate than trilateration and less complex than multi-dimensional scaling. Numerically, the mean distance error of the NDBL algorithm is 1.08–5.51 less than that of distributed weighted multi-dimensional scaling (dwMDS). Implementation of the algorithm using MicaZ with TinyOS-2.x confirmed the practicality of the proposed algorithm.     

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.     

A comparative investigation of deterministic and metaheuristic algorithms for node localization in wireless sensor networks

Location-based services in wireless sensor networks demand precise information of locations of sensor nodes. Range-based localization, a problem formulated as a two-dimensional optimization problem, has been addressed in this paper as a multistage exercise using bio-inspired metaheuristics. A modified version of the shuffled frog leaping algorithm (MSFLA) has been developed for accurate sensor localization. The results of MSFLA have been compared with those of geometric trilateration, artificial bee colony and particle swarm optimization algorithms. Dependance of localization accuracies achieved by these algorithms on the environmental noise has been investigated. Simulation results show that MSFLA delivers the estimates of the locations over 30% more accurately than the geometric trilateration method does in noisy environments. However, they involve higher computational expenses. The MSFLA delivers the most accurate localization results; but, it requires the longest computational time.

     

基于RSSI自适应三维加权质心定位算法

针对无线传感网络RSSI加权质心定位算法精度较低的问题,提出了一种采用RSSI值作为加权因子的三维加权质心定位算法。依据RSSI值自适应缩小定位区域,并根据筛选出的最优参考节点构建三维球体定位模型。仿真结果表明,改进的定位算法在相同测试条件下,在精度与稳定性上相较传统加权质心算法有了大幅提高。     

无线传感器网络节点定位算法研究

研究无线传感器网络节点定位的方法。首先介绍了节点定位的基本原理,在总结节点定位原理的基础上,对节点定位方法的分类依据进行了归纳。在对无线传感器网络节点定位方法的研究中,主要对是否基于测距的节点定位方法进行具体分析,介绍了2种类型的定位方法的基本原理,并对2种类型的定位方法中的典型算法做了具体说明,最后介绍了定位算法的评价标准。