无线传感器网络中基于接近度的无需测距定位算法

针对当前无需测距定位算法存在定位误差大的问题,本文提出了一种基于接近度的无需测距定位算法,接近度是本文定义的一个用来表示邻居节点距离远近的值.首先根据邻居节点之间的几何特征和邻居关系推导出一个线性函数,函数输出是接近度.然后用锚节点之间的距离和接近度计算一个矫正值,矫正值和邻居节点之间接近度的乘积作为邻居节点之间的估计距离.最后根据估计距离计算未知节点的估计位置.仿真结果表明,本文算法的估计距离误差和定位误差都要低于当前同类型定位算法.     

Improved 3-dimensional DV-hop localization algorithm based on information of nearby nodes

DV-Hop, a range-free localization algorithm, has been one of the most popular localization algorithm. It is easy and inexpensive to implement. Therefore, in the literature, many improved variants of this algorithm exist. However, poor location accuracy and higher power consumption by DV-Hop algorithm always open new avenues for research on this algorithm and makes it a favorite among the researchers. In this paper, we have proposed an Improved 3-Dimensional DV-Hop algorithm based on the information of nearby nodes (I3D-DVLAIN). In the algorithm, by calculating hopsize at the unknown nodes, we eliminate one communication among the nodes, which reduces power consumption in the network. The hopsize calculation and location estimation is done by using only the nearby anchor nodes, which minimizes the network usage and decreases the computational effort. For the selection of nearby anchor nodes, we introduce a new method. Further, for localization, a novel method is used for solving the system of distance equations that restricts propagation of inherent error in the distance and increases localization accuracy. Furthermore, by mathematically analyzing the propagation of error in solving the system of equations, we prove the superiority of I3D-DVLAIN over other compared algorithms. The results obtained through simulation and complexity analysis of the computation and communication further strengthens our observations about the superiority of the proposed algorithm.

     

Sensor Position Determination with Flying Anchors in Three-Dimensional Wireless Sensor Networks

This paper presents a range-free position determination (localization) mechanism for sensors in a three-dimensional wireless sensor network based on the use of flying anchors. In the scheme, each anchor is equipped with a GPS receiver and broadcasts its location information as it flies through the sensing space. Each sensor node in the sensing area then estimates its own location by applying basic geometry principles to the location information it receives from the flying anchors. The scheme eliminates the requirement for specific positioning hardware, avoids the need for any interaction between the individual sensor nodes, and is independent of network densities and topologies. The performance of the localization scheme is evaluated in a series of simulations performed using ns-2 software and is compared to that of the Centroid and Constraint range-free mechanisms. The simulation results demonstrate that the localization scheme outperforms both Centroid and Constraint in terms of a higher location accuracy, a reduced localization time, and a lower beacon overhead. In addition, the localization scheme is implemented on the Tmote Sky for validating the feasibility of the localization scheme.     

Range-free localization algorithm based on connectivity and motion

Information about the position of entities is very valuable in many fields. People, animals, robots and sensors are some examples of entities that have been targeted as nodes of interest for localization purposes. Technical advances in ubiquitous computing and wireless communications properties are very valuable means to obtain localization information. This paper presents a novel range-free localization algorithm based on connectivity and motion (LACM). The core of the algorithm is an error function that measures the error of the obtained trajectories with respect to the localization solution space, a multi-dimensional space that encompasses all solutions that satisfy completely the constraints of a range-free localization problem. LACM is a centralized method that can be used standalone or as a refinement phase for other localization methods. Limited-memory Broyden–Fletcher–Goldfarb–Shanno, an unconstrained optimization algorithm, is the numerical method used to minimize the error function. The performance of LACM is validated both through extensive simulations with excellent results in scenarios with irregular communications and by transforming real Bluetooth connectivity traces into localization information.     

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.     

A Distributed Localization Scheme for Wireless Sensor Networks with Improved Grid-Scan and Vector-Based Refinement

Localization is a fundamental and essential issue for wireless sensor networks (WSNs). Existing localization algorithms can be categorized as either range-based or range-free schemes. Range-based schemes are not suitable for WSNs because of their irregularity of radio propagation and their cost of additional devices. In contrast, range-free schemes do not need to use received signal strength to estimate distances and only need simple and cheap hardware, and are thus more suitable for WSNs. However, existing range-free schemes are too costly and not accurate enough or are not scalable. To improve previous work, we present a fully distributed range-free localization scheme for WSNs. We assume that only a few sensor nodes, called anchors, know their locations, and the remaining (normal) nodes need to estimate their own locations by gathering nearby neighboring information. We propose an improved grid-scan algorithm to find the estimated locations of the normal nodes. Furthermore, we derive a vector-based refinement scheme to improve the accuracy of the estimated locations. Analysis, simulation, and experiment results show that our scheme outperforms the other range-free schemes even when the communication radius is irregular.     

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。     

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.     

Accurate and Efficient Node Localization for Mobile Sensor Networks

In this paper, we propose a range-free cooperative localization algorithm for mobile sensor networks by combining hop-distance measurements with particle filtering. In the hop-distance measurement step, we design a differential-error correction scheme to reduce the positioning error accumulated over multiple hops. We also introduce a backoff-based broadcast mechanism in our localization algorithm. It efficiently suppresses redundant broadcasts and reduces message overhead. The proposed localization method has fast convergence with small location estimation error. We verify our algorithm in various scenarios and compare it with conventional localization methods. Simulation results show that our proposed method has similar or superior performance when compared to other state-of-the-art localization algorithms.     

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。     

Weighted centroid localization based on compressive sensing

To solve the problem of estimating the locations of sensor nodes in wireless sensor networks where most nodes are without an effective positioning device, a novel range-free localization algorithm—weighted centroid localization based on compressive sensing (WCLCS) is proposed. WCLCS makes use of compressive sensing to get decomposition coefficients between each nonbeacon node and beacon nodes. According to these coefficients, WCLCS algorithm decides the weighted value of each beacon node for Centroid and estimates the locations of nonbeacon nodes. The simulation results show that WCLCS has better localization performance than LSVM.     

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。     

Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks

A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.     

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。     

High-accuracy and low-cost localisation scheme for wireless sensor networks

Recently, wireless sensor networks have been used in many promising applications including military surveillance, wildlife tracking, habitat monitoring and so on. They are an indispensable requirement for a sensor node to be able to find its own location. Many range-free estimate approaches eliminate the need of high-cost specialised hardware, at the cost of a less accurate localisation. In addition, the radio propagation characteristics vary over time and are environment dependent, thus imposing high calibration costs for the range-free localisation schemes. In order to reconcile the need for the high accuracy in location estimation, we describe, design, implement and evaluate a novel localisation scheme called laser beam scan localisation (BLS) by combining grid and light (laser) with mobile localisation policy for wireless sensor networks. The scheme utilises a moving location assistant (LA) with a laser beam, through which the deployed area is scanned and Zigbee platform is adopted for experiments in this article. The LA sends IDs to unknown nodes to obtain the locations of sensor nodes. High localisation accuracy can be achieved without the aid of expensive hardware on the sensor nodes, as required by other localisation systems. The scheme yields significant benefits compared with other localisation methods. First, BLS is a distributed and localised scheme, and the LA broadcasts IDs while unknown nodes listen passively. No interactive intersensor communications are involved in this process; thus, sensor energy is saved. Second, BLS reaches a sub-metre localisation error. Third, because the equation is simple, computational cost is low. Finally, BLS is a low-cost scheme because it does not require any infrastructure or additional hardware for sensor nodes.     

基于二次质心的无线传感器网络定位算法

节点的定位是无线传感器网络中的一种重要技术。提出了一种新的无线传感器网络定位算法——基于二次质心算法的定位算法,与以往的基于三边测量的加权质心方法不同,该算法改进了对未知节点位置的估算方法,一定程度上避免了因多次估算质心而产生的累积误差,提高了定位精度。仿真表明,该算法的定位精度较之前的三边测量方法提高了约19%。     

基于无线电相干的传感器网络节点定位

目前无线传感器网络节点定位算法中,能够兼顾高精度和远距离定位的算法只有RIPS方法,然而该方法利用汇聚节点进行集中定位。提出了一种基于无线电相干的角度估计算法,并分布式定位节点,在高精度、远距离定位节点的同时,可大规模应用该算法,且定位速度快。实验表明,该方法平均方位估计误差是3.20,90%的测量值误差在6.4度以内。     

Range-Free Mobile Sensor Localization and a Novel Obstacle Detection Technique

Mobile sensor localization is a challenging problem in wireless sensor networks. Due to mobility, it is difficult to find exact position of the sensors at any time instance. The aim of localization is to minimize positioning errors of the mobile sensors. In this paper we propose two range-free distributed localization algorithms for mobile sensors with static anchors. Both the algorithms depend on selection of beacon points. First we assume that mobile sensors move straight during localization which helps us to provide an upper bound on localization error. Certain applications may not allow sensors to move in a straight line. Obstacles may also obstruct path of sensors. Moreover beacon point selection becomes difficult in presence of obstacles. To address these issues, we propose another localization algorithm with an obstacle detection technique which selects correct beacon points for localization in presence of obstacles. Simulation results show improvements in performance over existing algorithms.     

基于限定域-蜂群的光纤物联网节点定位算法研究

为了实现物联网光纤感知层中待测节点的快速精确定位,提出了基于限定域-蜂群的节点定位算法,建立了针对光纤传感网络数据特点的节点定位模型,采用矩形限定域简化约束条件,从而提高定位精度。实验结果显示,当参考节点数大于3时,对定位平均误差的影响基本不变;当种群数取18时,定位平均误差趋于稳定,3种算法的平均定位精度分别是2.3 m、3.1 m和3.4 m;而达到定位精度需要的迭代次数分别是12次、15次和41次。由此可见,本算法在稳定性、定位精度及收敛速度方面均具有更好的定位性能,其在大范围物联网光纤感知层节点定位领域具有一定的实际应用价值。     

基于压缩感知的室内多目标无线定位算法

针对现存无线传感器网络定位算法中需要采集、存储和处理大量数据导致运算量较大与能耗过高的问题,提出了一种改进的基于贝叶斯压缩感知的多目标定位算法.该算法利用锚节点对监控区域的划分,结合贝叶斯压缩感知理论将多目标定位问题转换为稀疏信号重构的问题.针对传统观测矩阵难以实现的缺陷,该算法中改进观测矩阵的设计可实现且与稀疏变换基相关性较低,进而使得算法的重构性能较高,从而降低了定位的误差.仿真结果表明,与现有的一些方法相比,所提算法在保证较低的计算复杂度的情况下更加充分地利用了网络节点,有效提高了定位精度,同时具有较强的鲁棒性.