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JasminDesai

jdesai1@stevens.edu

Dept.Elec.andComp.Engineering

StevensInstituteofTechnology,

HobokenNJ07030

EVALUATING PERFORMANCE OF VARIOUS LOCALIZATION ALGORITHMS INWIRELESS AND

SENSOR NETWORKS

无线传感器网路各种定位算法的性能分析

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Abstract（摘要） In this paper, recent localization algorithms are analyzed under A common

one hop network scenario. The performance of localization is affected by physical parameters in a real wireless Environment such as anchor node location and quantity, and Error in the measured distance. The numerical analysis presented in this paper can be used to choose among localization Algorithms to satisfy practical constraints such as number of anchors, nodes, geometry of anchors and computational efficiency.

本文，我们在一个普通的一跳网路场景对最近的定位算法进行分析。定位的性能受到如锚节点位置和数量以及在测量距离的误差等真实环境中的真实参数的影响。本文提出的大量分析能够用在定位算法中来满足如锚节点数量，节点，锚节点的几何分布和计算效率等实际的约束。

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1.Introduction（引言） We have implemented various algorithms based on multilateration rather than

triangulation. These selected algorithms are maximum likelihood estimation (MLE), modified multidimensional Scaling (MMDS), Malguki spring model (MSM) and weighted multidimensional scaling (WMDS).

我们根据多边测量法来实施这几种算法而不采用三角测量法。这些选择的算法是最大似然估算（ MLE ） , 改进多维指标法（ MMDS ） , Malguki 弹性模型 (MSM) 以及加权多维指标法（ WMDS ）。

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2 SYSTEM MODEL

The mean received power by receiver at distance d is expressed in dB by ： 距离 d 接受到的信号强度可以以 db 为单位表达为： Based on this model, the Mean and variance Distance estimation is performed by ： 基于这个模型，距离估算为：

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3. ALGORITHMS

We have implemented five algorithms which represent the state Of the art in wireless and sensor network positioning. In the following, a brief description for each algorithm is presented. Consider the network model with n anchor nodes and one Unknown node for simplicity as shown in Figure2.

我们提出了代表无线传感器网络位置定位的 5 种算法。在下面，提出每一个算法简单的描述。考虑到图 2 所示的一个未知节点和 n 个锚节点的网络模型。

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最大似然估算法（ MLE ） 我们定义测量值和真实值之间的误差 ei

利用相似的第 n 个锚节点方程估算位置节点的平方项：

利用对于所有锚节点的方程将会得到已知方程的矩阵：

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改良的多维指标法（ MMDS ） 在典型的 MDS 算法中，分布矩形定义为：

D 矩阵中的双中心和分布中的特征值：

估算一个随机转角的位置以及需要传输矩阵：

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Malguki 弹性模型（ MSM ） Malguki 通过网络 spring 减少成本方程 ，成本方程表示为：

利用从锚节点 single force 的总和来减少成本方程：

迭代的实现和 m 矢量利用正指标参数转化成一个分布

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加权多维指标法 本算法的关键为得到更准确的距离测量需要更多的加权量

以及迭代的主要方法保证改善成本方程。成本方程 S 可以改善为：

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4.SIMULATION RESULTS

锚节点数量增加的影响 WMDS has best performance in terms of lowest position Error of below 1

meter which slightly deviates with increase In measurement error. The reason for this performance is Weighted distance, exploiting distance between unknown nodes And guaranteed cost function reduction method. These three features have made WMDS most robust among all 。

WMDS 的位置误差最小为 1 米而且不会随着测量误差的增加而大幅度增加。主要原因是加权距离，搜索未知节点的距离和保证成本方程的减少。这些特征使 WMDS 变成最稳健的算法。

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误差增加的影响 WMDS has best performance among all algorithms. The Measurement

error has less effect on performance curve. Position error remains below 3 meters up to 2dB measurement Error with 7 anchor nodes. Increased anchor nodes have performance improvement of 1 meter at 1.8dB measurement error. The large position error at the beginning is due to the poor initial position estimation in WMDS.

WMDS 在所有算法中是最好的。测量误差对定位性能曲线影响不大。测量误差为 2db 时，位置误差在七个锚节点下低于 3 米。增加锚节点使得 1.8 测量误差下的定位精度小于一米。开始误差较大的原因是 WMDS 下较差的初始位置估算。

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5.CONCLUSION

To achieve minimum error we need To use WMDS whereas MLE is preferred for less complexity. Both MLE and Malguki perform well when unknown nodes are Surrounded by anchor nodes. MDS has better performance with Sufficient anchor nodes and less error. This necessitates more Anchors for noisy measurements. Although WMDS gives best Performance its application is limited by complexity, convergence time and initial estimate requirements. This comparison Is very useful before selecting appropriate algorithm for any network.

为了获得最小的误差我们需要选择了 WMDS ， MLE 复杂性更小。 MLE 和 Malguki 在位置节点围绕着锚节点时性能很好。 MDS 在足够的锚节点下定位效果会更好。它需要更多的锚节点应付噪音环境。虽然 WMDS 性能最好，但是它的复杂性，集合时间和初始估算需求等因素限制它的应用。这些比较在选择合适定位算法是非常有用的。

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MarcelBaunach,ReinerKolla,ClemensMuhlberger

DepartmentofComputerEngineering,UniversityofWurzburg,AmHubland,97074Wurzbrug,Germany

Email: {baunach,kolla,muehlberger}@informatik.uni-wuerzburg.de

Telephone:+49(0)931-888-6716,Fax:+49(0)931-888-6702

Beyond Theory: Development of a Real WorldLocalization Application as Low Power WSN超越理论：现实世界低功耗WSN定位应用的开

发

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Abstract（摘要） One well-known and often required application with in WSN Is the

geographical localization of several sensor nodes. That Why this paper deals with some problems arising during the Development of a WSN using the time difference of arrival (TDoA) of ultrasound and radio signals for positioning. Its focus Is on handling of microcontroller difficulties like little memory, Low computational power or low energy consumption as well as Hardware driven failures like inaccurate measurements or node failures.

一个无线传感网络众所周知的应用是对节点的定位。这是很多文章用超声波和射频信号的到达时间差来定位的原因。它强调处理小存储量，低计算功率或者低能耗以及硬件造成的测量不准或者节点损坏等困难。

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1.Introduction（引言） This paper describes our own experiences gained during The development of

a WSN based localization system and Some of the lessons we learned. For system set up we used the sensor node platform SNOW , because wireless radio Communication is yet available and supported as well as its Great expand ability to stack further sensor modules.

本文在基于定位系统的开发给出了我们的经验以及我们学到的东西。对于建立的系统我们利用节点平台 SNOW ，因为无线传感器射频通信是可以得到的以及它可以扩展到更远的传感器模型。

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3.TDOA MEASUREMENT IMPROVED

The localization algorithm should calculate a concrete and Accurate position for the mobile device, using only information Like the coordinates and detected distances of sufficient static anchors. Because these coordinates are fixed and well-known Only the measured time difference of arrival of radio and Ultrasound need to be converted into a concrete distance. So, This section first presents one possible realization of a TDoA System and derives therefrom an improved distance Estimation algorithm. Next, the physical phenomena ramping will be examined as well as the possibility of multichirping.

定位算法必须利用坐标和充分静态锚节点的检测距离等信息来计算移动设备的一个具体和真实的位置。因为这些坐标是固定的而且射频和超声波到达时间的不同需要转化成一个具体的距离。所以，这部分首先提出 tdoa系统的一个可能的认识以及得到一个距离估计算法。然后，给出物理现象的斜率和多线性调频的可能性。

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SNOWBAT calls such a message chirp allocation vector(CAV) which contains al ldata necessary,e.g.

the ID of the mobile transmitter, its last known position posold , Its maximum speed vmax A duration.

SNOW BAT称这样的信息作为线性调频分布矢量（ CAV ）它包括所有必要的信息，

移动收发器的 id 它最近的已知位置 POSold 它的最大的速度 Vmax 一个持续时间

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If a static node receives a chirp after a foregoing CAV, it must also record the chirps time of arrival and can Shut down its ultrasonic receiver afterward for energy savings. The ultrasonic detection can be realized by an analog-to-digital Converter or by a capture/compare unit, which will be more Adequate and more energy-saving.

如果在一个前进的 CAV后一个静态节点接受了一个chirp ，它必须记录 chirp 的到达时间和减少他的超声波接收器来减少能耗。超声波的检测可以通过一个数模转换器或者一个捕获 /比较单元来实现，它更适合和更节能。

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距离估算 Assuming the things specified above, the distance d between The chirping

mobile node and the receiving anchor can be Computed then solely by the static node using an improved versionof(1)asfollows:

The more precise The time recording and the determination of v US are, the More accurate is the calculated distance. That means, the Interrupt latency at the nodes must be kept short and constant At most.

假设以上特定的事情，在 chirp 的移动节点和接受锚节点之间的距离 d 能够计算然后利用一个（ 1 ）式的改进：

它证明记录时间和 vus 的目的越精确，计算的距离越精确。也就是说，节点的中断必须保持短暂和常数

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Multichirping To increase accuracy, a mobile node can chirp more often For each

measurement. That means, after the first chirp the Mobile node sends a fixed number of further ultrasonic chirps At specific intervals, The receiving anchors thus have to determine at least one Such chirp and average over all received signals of that measurement. This result will be replied to the mobile one As the estimated distance. Thereby the average measurement Error will be kept small.

为了提高精度，一个移动节点可以在每一个测量内线性调整。它就是说，在一个 chirp 之后移动节点在特定间隔内发送一定数量的超声波 chirp 。接受锚节点必须检测一个或以上的 chirp 以及测量接收信号的平均值。所以平均测量误差会保持得很小。

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4.FROM DISTANCES TO POSITIONS

1、系统简化 根据毕达哥拉斯理论，测量距离半径 ri由测量距离 di 和

已知的高度 h得到： 2、方根近似 为了减少凑整误差，一个可以分成很多小的单元。

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3、数据选择 选择合理的存储空间 4、初始值选择 5、最优值的选择 6、位置估算

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5. CONCLUSION AND FUTURE WORK

大多数提到的优化方法已经在 SNOW 传感器节点中测试，对于这种情况，对于单独的距离它得到了 +-1mm 的精度以及在 3 维上得到 +-4mm 的精度。但是第一个实验表明通信协议成了无线传感器网络的瓶颈，因为很大部分的存在chirping 的移动节点以某一个频率运行。所以以后的实现需要采用不同的通信协议来在真实系统实验 。

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WernerKurschl,WolfgangGottesheim,StefanMitsch,

ReneProkop,andJohannesSchonbock

UpperAustriaUniversityofAppliedSciences

ResearchCenterHagenberg

Softwarepark11,A¨C4232Hagenberg,AUSTRIwkurschl,wgottesh,smitsch,rprokop,jschoenb@fh-hagenberg.at

A Two-Layered Deployment Scheme forWireless Sensor Network based Location Tracking

基于无线传感器网络的位置定位的一种两层分布策略

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Abstract（摘要） Nevertheless the communication infrastructure and thus The tracking

applications rely on radio transmission. Therefore problems like ambiguous locations and the neighboring-room problem known from other radio-based Tracking solutions may occur. We propose a two layered Deployment scheme for wireless sensor networks to overcome these limitations. It consists of a robust communication layer and a flexible location layer, which enable per-Room accuracy. Such accuracy is sufficient form any types Of tracking applications.

然而，通信的架构和跟踪的应用需要射频的传送，所以会出现如其他射频跟踪系统会处出现的不明确的定位和邻近 -房间等问题。我们提出了无线传感器网络的一个两层分布策略来克服这些困难。它包括一个稳健的通信层和一个灵活的定位曾，能够提高精度。这样的精度能够适合于跟踪系统的应用。

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1.Introduction（引言） Stojmenovicin[10] describes the localization problem In terms of two

hardware sub-problems: first, the problem Of defining a coordinate system, and second, the problem Of calculating the distance between nodes in the localization system. Both problems are not unique to sensor network localization and thus many proposals to solve them Already exist. But issues such as scalability, ease of deployment, maintenance, and fail-safety still need to be addressed To make sensor network location tracking applicable in industrial applications.

定位的问题根据两方面：首先定义一个坐标系统，然后，计算出节点的距离。两个问题不是唯一而且很多解决的方案也出现了。但是如扩展性，分布性，安全性仍然需要在传感器定位网络中考虑进去。

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2. Related Work To build a successful localization system in such application domains it is

necessary to solve the neighboring-Room problem. We developed and tested a two-layered deployment scheme that allows us to create exact boundaries Between rooms or with in rooms when needed. The deployment scheme is designed to be easily deployable, scalable To different area extents, and robust to failing nodes.

为了建立一个定位系统需要解决相邻房间的问题。我们开发和测试了一个 2层的分布策略允许我们建立一个精确的房子之间的边界。开发的策略容易分布，扩展到不同的领域，稳健等。

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3.A Redundant Two-Layered Deployment Scheme

3.1 通信层 A hub simply forwards location information to neigh-Boring hubs, bridges, and

gateways. A bridge consists of Two hubs linked by cable; it is used to connect two adjacent areas that can not be connected wirelessly (e.g., due To thick walls). A gateway connects the wireless network To another enterprise network, like WLAN or cabled Ethernet networks.

一个集线器发送定位信息到邻近集线器，网桥，网关。一个网桥包括两个连接到光纤上集线器；他用来连接两个不能通过无线连接的相邻区域（如有一个很厚的墙）。一个网关连接到无线传感器网络到另一个计划网络，如 wlan或者以太网。

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3.2 定位层 The mobile tag periodically sends location query messages that are replied

by location beacons. After receiving the reply, the tag computes and broadcasts its position And the communication mesh network forwards this position message to the central location tracking system. Sub-sequent location queries are then addressed to the beacons In the same room to lessen energy consumption in the network; only if these beacons do not answer, another location Query broadcast is issued.

移动标签周期发送定位查询信息，信息由信标节点回复，标签接收到回复后，标签计算和广播他的位置，通信 mesh 网络发送位置信息到中心定位跟踪系统。下一步的定位查询发动同一房间的信标节点来减少网络的能量消耗；如果这些信标节点没有答复，其他定位查询会广播询问。

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Mobile tags operate with high radio power to reach as Many hubs as possible when broadcasting location information through the communication network. Therefore, many Beacons may receive the tag’s location query message an Reply to it, but the tag only receives the reply messages from The beacons in the corresponding room as the communication range of beacons might be smaller than that of the tag.

移动标签根据很高的射频功率来使很多的集线器广播定位信息。所以，很多信标节点接受标签的位置查询信息然后答复，但是标签只能接收到相关房间信标节点的答复信息，信标节点的通信范围可以比标签的小

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4.Performance Evaluation and Discussion Battery Runtime The tags are required to issue frequent location queries, Which shortens

battery runtime depending on the update Interval needed, But mobile tags can Be easily carried to charging points and recharged periodically.

Temporal Resolution A tag that travels at a pace of 1m/s in attacking environment with a spatial

resolution of 10m requires A temporal resolution of 10s or better to enable continuous tracking.

标签需要查询位置信息，他会缩短电池的时间，移动标签可以周期性地在充电。

一个能够传输速度为 1 米每秒的标签需要在 10 米的区域定位需要 10秒或者更好的时间来保证连续的跟踪。

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Spatial Resolution Costs and Reliability

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5.Conclusion and Further Work Previous research laid the foundation for building large-Area location tracking

systems but ignored issues that arise In real-world (industrial) application. The described deployment scheme proved to be sufficiently accurate, easy to Install and maintain, and feasible in industrial settings for Improving occupational safety.

之前的研究建立在大范围的定位系统但是忽略了真实环境（工业）的应用。提出的部署策略证明了他在工业环境也能适用，容易安装和维护，并且可以灵活地设置来提高安全。