Energy and the Environment | US EPA赞助商链接
论文分类导航
经济管理论文：
教育教学论文：
医药卫生论文：
法学研究论文：
文哲艺术论文：
工程技术论文：
电子信息论文：
农业科学论文：
无线传感器网络定位技术分析
　　无线传感器网络WSN（Wireless Sensor Network）作为新兴物联网的重要技术之一，是当前信息领域中的研究热点。目前，WSN被广泛用于环境监测、目标识别与跟踪，如在大型结构状态监测、城市交通安全监测和智能家居等领域。然而，在监测区域很多时候不仅需要获取监测的事件信息，更需要知道事件发生的具体位置，这就使得WSN中节点自身位置信息变得非常重要，换句话说，节点的定位在很大程度上决定着WSN的应用前景。本文从以下三个方面对定位技术进行分析：WSN中网络节点是如何进行定位的，基于测距的定位算法和无需测距的定位算法分别通过什么方式进行定位以及各自的特点，目前的定位算法所面临的问题与挑战。&
　　2 无线传感器网络节点定位原理&
　　基于网络中传感器节点位置信息的获取状态，在WSN的节点定位技术中节点主要分为两类，一类是信标节点，即锚节点，另一类是未知节点。锚节点是已知自己的位置信息且位置固定，由人工部署或配有GPS等导航设备，成本比一般节点高，在定位过程中能量消耗也比较大，所以网络中锚节点的数量较少。未知节点通常是随机分布，不知道自身位置信息，需要进行定位。WSN的定位技术是进行其他众多应用的基本前提，目前的定位方法通常包含两个部分，一部分是测量节点之间的距离或者角度，另一部分是通过一定的计算方式，实现节点的定位。&
　　WSN中的未知节点进行第一部分操作其目的是为了获得与锚节点之间的直线距离。未知节点通过测量与邻近节点的距离或者角度来计算得到与邻近锚节点的距离，进而获取到锚节点的直线距离。在未知节点获得大于等于三个到达锚节点的直线距离后进行第二部分的具体定位计算。定位计算通常采用三边（角）测量法或极大似然估计法等方法进行，但由于网络中节点间的距离测量会存在一定误差，进而导致节点的计算坐标与实际坐标之间产生差异，因此在实际应用中通常采用最大似然估计法进行定位计算以此尽可能地减少差异。假设网络中有n个已知位置信息的节点，其坐标和到未知节点M（x，y）的距离分别为（x1，y1）、（x2，y2）、&（xn，yn）， d1、d2、d3、&dn，则存在下列公式（1）：&
　　3 典型的定位算法&
　　根据WSN中节点定位过程是否需要测量节点间的距离将其分为两类，一类是需要明确网络中节点之间的精确距离或角度，然后用三边测量法或三角形关系定位的高成本、高定位精度的基于测距（Range-Based）的定位算法。另一类是不需要额外的节点硬件支持，直接根据网络中各节点间的通信情况来记录锚节点和其他节点间的跳数值，然后估算节点间的距离的定位算法，即基于无需测距（Range-Free）的定位算法。两类定位算法相比较，前者定位精度较高，但在实际运用中受硬件条件、成本和功耗等因素的限制，难以应用于功耗和成本较低的领域。后者对硬件条件没有过高的要求，计算较简单，但由于定位精度不高，其适用范围有一定的局限性。&
　　3.1 基于测距的定位算法&
　　基于测距的定位算法主要是依据网络中节点间的距离或者角度的测量来确定未知节点的位置信息，而节点间的距离或者角度测量需要通过一定的方式进行，目前，在无线传感器网络中常用的测距方法有TOA，TDOA，AOA，RSSI等，各测距方法的特点如表1所示。&
　　3.2 基于无需测距的定位算法&
　　基于无需测距的定位算法主要是根据网络的连通性来计算网络中各节点与锚节点之间的距离，不需要测量节点之间的距离，典型的无需测距定位算法中有质心定位算法[1]、凸规划定位算法[2]、DV-Hop定位算法[3]等。&
　　Nirupama Bulusu 等提出的质心定位算法的基本原理是每间隔时间s，信标节点向网络中各节点发送一个信标信号（包含信标节点编号及其位置信息），当未知节点获得超过一定数量的不同信标信号时，信标节点所构成的多边形质心则为定位结果，该算法实现简单但对锚节点的依赖较大。针对质心定位算法存在的不足，文献[4]结合距离因素对算法进行优化，既不增加额外硬件设施又在一定程度上提升了算法的定位精度。文献[5]在考虑接收信号强度的基础上对算法的不足进行了改进，有效地避免了反演误差，在提高算法精度的同时还降低了算法的复杂度和节点功耗。&
　　Doherty等人提出的凸规划定位算法的基本思想是将WSN中节点间的网络通信连接作为一个凸集进行处理，通过半定规划或者线性规划等方式对凸约束进行优化，从而完成节点的定位。这种算法的覆盖面较低，为避免边缘节点的估计位置向网络中心偏移，锚节点需要在网络边缘进行部署。结合传统凸规划定位算法的不足，文献[6]在锚节点的通信范围内通过引入最大内接圆来减少无效区域，在减少未知节点定位误差的同时又降低了该算法的功耗与开销。&
　　DV-Hop定位算法是由Dragos Niculescu等人提出，该算法主要基于距离矢量路由和GPS定位原理。这种算法不需要进行实际距离的测量，也不需要其他的硬件条件支持，完全基于网络的连通性，在算法的运行过程中网络中所有节点在信息的同时计算自己的位置，节点之间没有地位之分。DV-Hop定位算法的执行过程简单，但其采用计算距离（节点间跳段数乘以平均每跳距离）代替实际距离，会导致计算坐标与实际坐标之间存在很大的误差，定位精度较差。针对DV-Hop定位算法存在的定位误差问题，大量学者围绕如何精确跳段数和网络平均跳距这两个值进行了深入研究，如文献[7-8]通过引入通信半径进一步精确记录节点间的跳段数以此优化节点间计算距离，从而缩小计算坐标与实际坐标的误差，提高算法的定位准确度。
　综上所述，基于无需测距的定位算法更多的偏向于理论研究，主要是通过网络连通度来进行定位，但是定位精度较低，缺乏实用性，其性能比较如表2所示：&
　　3.3 当前定位算法面临的主要挑战&
　　WSN中节点的定位问题是其运行的前提和基础，目前，WSN的定位研究已取得较多成果，但在应用中仍面临许多问题与挑战有待进一步深入分析解决。&
　　（1）定位精度：受硬件条件影响，不同的测距或测角技术具有不同的误差特征，由此带来的测距误差会影响定位精度。同时，在进行定位计算过程中造成的误差也会影响定位精度。&
　　（2）受能量限制：传感器节点依靠电池供电，但由于节点的电池能量有限，且网络要求自适应、自组织地运行，这使得节点的计算能力、内存、通信能力等都受到限制，要求节点间的通信和感知次数要尽可能的少，定位算法对节点的功耗要很小。因此，能量限制也是定位技术需要解决的问题。&
　　（3）锚节点数目：锚节点的位置通常是人工布置或由其他定位系统确定。但是对于大规模网络或某些人员不易接近的区域，人工布置不现实，所有节点通过定位系统确定也不实际，通常只有小部分节点为锚节点，稀疏的锚节点使得普通节点位置的确定面临困难。&
　　（4）实用性差：基于无需测距的定位算法大多数集中在理论研究，且基本都是在仿真环境中实现，会假设许多不确定因素，但无线传感器节点通常会部署在战场、无人区等复杂地理环境中，这些不确定因素在实际中难以满足，导致算法失去了实用性。&
　　4 结束语&
　　节点定位技术在无线传感器网络的应用中至关重要，本文介绍了无线传感器网络中节点如何进行定位，并在此基础上比较分析了几种典型的节点定位算法，同时指出现有定位算法存在的一些亟需解决的问题。节点定位涉及定位精度、网络规模、锚节点密度、网络的容错性和功耗以及成本等多个方面，如何平衡各个方面的关系对于无线传感器网络的定位问题是需要深入分析研究的。&
　　参考文献：&
　　[1] Bulusu N， Heidemann J， Estrin D. GPS-less Low-cost Outdoor Localization for Very Small Devices[J]. IEEE Personal Communications， 2000， 7（5）： 28-34.&
　　[2] Doherty L，Pister KSJ，Ghaoui LE. Convex position estimation in wireless sensor networks[C]//Proc. of the IEEE INFOCOM 2001.Anchorage：IEEE Computer and Communications Societies，-1663.&
　　[3] Niculescu D，Nath B. DV based positioning in ad hoc networks[J]. Journal of Telecommunication Systems，/4）：267-280.&
　　[4] 何艳丽.无线传感器网络质心定位算法研究[J].计算机仿真，2011，28（ 5） ： 163-166.&
　　[5] 李文辰，张雷.无线传感器网络加权质心定位算法研究[J].计算机仿真.）：191-194.&
　　[6] 向满天，罗嗣力，戴美思.无线传感器网络中一种改进的凸规划定位算法[J].传感技术学报，）：.&
　　[7] 李娟，刘禹，钱志鸿. 于双通信半径的传感器网DV-Hop定位算法[J].吉林学报（工学版），）：502-507.&
　　[8] 刘士兴，黄俊杰，刘宏银.基于多通信半径的加权DV-Hop定位算法[J].传感技术学报，）：883-887.
出处：电脑知识与技术&&作者：秦晓琴
上一篇： 　 下一篇：Android Fragmentation Visualized
11,868Distinct Android devices seen this year
3,997Distinct Android devices seen last year
682,000Devices surveyed for this report.
47.5%Samsung's share of those devices.
8Android versions still in use
37.9%Android users on Jelly Bean
Fragmentation is both a strength and weakness of the Android ecosystem. When comparisons are made between Android and iOS the issue of different API levels, and the vastly different devices running them, is often emphasised. In this report we examine the extent of Android fragmentation and analyse its impact on both users and developers.
The Problem:
Android devices come in all shapes and sizes, with vastly different performance levels and screen sizes.
Furthermore, there are many different versions of Android that are concurrently active at any one time, adding another level of fragmentation. What this means is that developing apps that work across the whole range of Android devices can be extremely challenging and time-consuming.
The Advantage:
Despite the problems, fragmentation also has a great number of benefits – for both developers and users. The availability of cheap Android phones (rarely running the most recent version) means that they have a much greater global reach than iOS, so app developers have a wider audience to build for. It may be tricky to do, but the potential reward definitely makes it worthwhile.
For consumers, extreme fragmentation means that they can get exactly the phone they want – big or small, cheap or expensive, with any number of different feature combinations.
Device Fragmentation
We have seen 11,868 distinct devices download our app in the past few months. In our report last year we saw 3,997.
This is the best way of visualizing the sheer number of different Android devices that have downloaded the OpenSignal app in the past few months. From a developer’s perspective, comparing fragmentation from this year to the previous year, we see that it has tripled, with even more
from around the world downloading the app. If you want to understand the challenge of building an app that will work on all devices that want to download it, this image is a good place to start!
Brand Fragmentation
Samsung have a 47.5% share of the Android market.
A similar look by brand, to see how much of the market each leading device manufacturer currently has, with Samsung clearly way out in front. Calculating the percentage share of the market held by the top few device manufacturers from our graphic really succeeds in emphasising quite how dominant Samsung are, with Sony-Ericsson in second with a 6.5% market share - less than 1/6th of Samsung's. Some of the brand names shown as different in the graphic are part of the same company, i.e. Moto and Motorola are the same and HTC is shown as split up into its different regional variants. But even when unified under one umbrella name Motorola only ends up with a 4.2% share and HTC even less at 3.9%.
Android Operating System Fragmentation
The Android operating system is the most fragmented it has ever been.
Device fragmentation is not the only challenge that developers face when building for A the operating system itself is extremely fragmented and has only become more so over time. The above graph shows the relative stages of Android fragmentation, and the steady decline of any one Android version having prominence can be seen by the progress of the white line.
Comparison with iOS
Android fragmentation of all kinds is usually illustrated in comparison with iOS. These two pie charts clearly show the difference in API fragmentation between the two competing operating systems.
Screen Sizes
Key to the success of any app is getting the UI right, and Android presents two particular challenges to developers in this regard. Firstly, brands have a tendency to produce their own variants on the system UI (Samsung’s Touchwhizz and the HTC Sense being two such examples - which can change the look of various default elements. Secondly, no other smartphone platform boasts such a proliferation of different screen sizes. For help in overcoming these difficulties see our
In the graphic below you can see the various physical screensizes we have observed on Android phones, with the darkness of the lines representing their frequency.
Designing and coding layouts that work well across all these screens is hugely challenging. Across the dozen or so iPod-touch, iPhones and iPad varieties there are just 4 different physical screen sizes - partly due to Apple's tendency to double pixel density while quadrupling resolution (e.g. iPad 2 -> iPad 3) maintaining the same physical screen size. The graphic below shows iOS screen size fragmentation, allowing for an easy comparison with Android.
So what does this mean?
Device fragmentation is increasing, and with it the reach and density of the Android operating system. While fragmentation certainly poses a headache to developers who have to test and optimize on an ever-increasing number of devices, the success of the of the Android ecosystem cannot be separated from its fragmented, free-for-all, nature. Similarly, it is easy for developers to bemoan API level fragmentation, but this is part-and-parcel of device fragmentation. Cheaper devices will struggle to run the most recent versions of Android and the fragmented operating system serves as an enabler of an ecosystem that is becoming more globally, and socio-economically, inclusive.
Fragmentation is responsible for a broader, more geographically dispersed, Android market - but fragmentation isn’t simply limited to devices and operating systems. It is also important for developers to think about the impact of contextual fragmentation, the variety of differing contexts in which devices are actually used. What is relevant for one region may not be to another, and developers need to take into account differences in network performance and reliability when designing their apps - as well as the level of Wi-Fi access for apps which are particularly data heavy. Another relevant fa while one day’s battery life may be acceptable in the developed world, it may well not cut it in developing markets. It is important to remember that the criteria against which app performance is judged can change by region, not simply by device.
What is clear from this report is that Android fragmentation, of all varieties, is increasing. Too often this is treated as a problem with Android, rather than a strength, but we feel that this misses the bigger picture. While there are certainly problems associated with fragmentation (and as developers we know them all too well), it is wrong to suggest that it is only a downside. Apple are currently working on a lower-end device, increasing the fragmentation of their ecosystem in the process, suggesting that the Android ecosystem is not only doing something right, but doing something to be imitated.
Notes on Methodology
The Device Fragmentation Graphic shows the 11,828 distinct device types that were present in the last 682,000 unique devices to download our app. The reason we chose 682,000 is because we wanted to make a fair comparison with last year's fragmentation report, which was based on a sample of 682,000 devices over a set period of time.
The Brand Fragmentation Graphic is based on the same 682,000 devices as the device fragmentation graphic.
The API Level Fragmentation Graphic is based on data made publicly available by Google (though it involved some digging). The stats up until Feb 2012 were collated by Chris Sauve
and he has our eternal thanks. Also a thank you to /u/ikjadoon on Reddit who pointed us in his direction!
The iOS Pie Chart is based on information made publicly available by Apple.
The Android Screen Size Graphic is based on information from 3.41 million OpenSignal users. In this graphic we are showing physical screen size, not size in pixels. Changes in resolution at the same screen size (for example as between the iPad 3 and iPad 2) present fewer difficulties for developers than changes in physical size. We derive the screen size by dividing the number of pixels of height and width by the pixel density (points per inch - ppi). Ideally we would use separate values for X-density and Y-density as screens are sometimes manufactured with different ppi in different directions, however Android only gives access to one variable. Also worth noting: with rooted devices users are able to “alter” the pixel density - in reality the physical characteristics of the screen stay the same, but the Android OS thinks it’s running on a larger device.
Sign up for our newsletter
No email address provided
Or follow us on