通信工程毕业论文开题报告-基于WIFI的无线传感器采集系统设计.doc

长春理工大学毕业设计任务书题目名称：基于WIFI的无线传感器采集系统设计 学生姓名：华丹阳 起止日期：2016.3.32015.6.22题目要求(包括主要技术参数):1 题目内容：设计基于WIFI技术的传感器信息采集系统，实现数据信息的网络发布2 具体要求及技术参数： 1.学习WIFI技术； 2.设计基于WIFI无线传感器信息采集系统硬件； 3.实现节点信息无线传输并上网发布。 指导教师签字 系主任签字 年 月 日 开题报告1、本课题研究的目的、意义；2、国内外研究现状；3、拟采取的研究路线；4、进度安排；1.课题研究的目的及意义 社会信息化日新月异,新技术层出不穷。而传感器技术在科技发展中却举足轻重。微型化、多功能化、网络化和智能化乃大势所趋,无线传感器网络则诠释了这些优势。将来人们将通过遍布周围的传感器网络直接感知客观世界，极大的改变人们认识世界、改造世界的能力。传感器网络覆盖范围大。能根据实际情况设计无线传感器网络的规模，有利于应用范围的扩展。传感器网络具有自组织功能。组网不需要任何固定的网络设备，传感器节点通过分布式网络协议形成自组织网络，能够自动调整来适应节点的变化，网络中的节点可以快速、自动的组成一个独立的网络。可以动态拓扑，无线传感器网络中可以随时添加或减少节点而并不影响网络其他节点数据的正常传输。功耗小，电池供电，网络节点一般都能工作3年左右，甚至更长。本文根据传感器网络发展状况，设计出基于WIFI的无线传感器网络，相比于传统的无线传感器网络，能够非常容易的与现有网络进行无缝的连接，相对降低组网成本和功耗。WIFI 无线传感器网络具有传输速率快、组网便捷等优点。2.国内外研究现状 1）国内的研究现状 山东省科学院与沈阳自动化研究所等研究单位及多所高校（如哈尔滨工业大学、北京邮电大学等）在无线传感器网络网络协议的研究与优化等方面也进行了大量的工作。除此之外，国家 863 计划、973 计划也对无线传感器网络的研究进行了相关规划。比如中科院宁波软件所和上海微系统所研究出自己的开发平台,中国科技大学,西北工业大学等院校都展 了路由层、数据链路层方面的研究。国家对传感器网络的研究也非常重视,国家自然科学基金委员会从2003年起开始设立了无线传感器网络相关研究课题,国家的“863”项目、国家自然科学基金项目、各省区的自然科学基金项目的课题中都有相当的比例是进行无线传感器网络研究的。 2）国外的研究现状 在传感器网络方面,加州大学伯克利分校提出了应用网络连通性重构传感器位置的方法,并研究出一个专门用于传感器网络节点的操作系统TinyOS。加州大学洛杉肌分校开发了一个无线传感器网络和一个无线传感器网络模拟环境,用于考察传感器网络各方面的问题。南加州大学提出了在生疏环境部署移动传感器的方法、传感器网络监视结构及其聚集函数计算方法、节省能源的计算、聚集的树构造算法等。麻省理工学院已经着手研究超低功耗无线传感器网络的问题,试图解决超低功耗无线传感器系统的方法和技术问题。在传感器网络通信协议方面,人们首先对已有的因特网和自组织无线网络的通信协议进行了研究,发现这些协议不适用于传感器网络应用场合。加州大学伯克利分校研究了传感器网络的数据查询技术,提出了实现可动态调整的连续查询处理方法和管理传感器网络上多查询方法,并研制了一个感知数据库系统TinyDB。南加州大学研究了传感器网络上的聚集函数的计算方法,提出了节省能源的计算聚集的树构造算法,并通过实验证明了无线通信机制对聚集计算的性能有很大的影响。 3）目前技术存在的问题 无线传感器网络即便节点灵活，可减硬件成本，但依然受有限能量的制约，优势未能充分发挥。无线传感器网络寿命长短与节点功耗大小息息相关，应致力于降低功耗。通信协议依然广泛，网络协议标准化较低。通信能力有限，传输距离不够长，受环境变化干扰。3. 采取的研究路线首先查阅大量关于无线传感器的相关文献，选用WIFI技术作为无线传感器网络的通信技术。充分研究，并通过 WIFI 组网技术设计 WIFI 无线传感器网络的结构。然后设计 WIFI 无线传感器节点的硬件结构。无线传感器网络节点分为核心控制模块、外围接口及电源管理模块、数据采集模块，针对各个模块的功能进行硬件设计。 选用ARM芯片AT91SAM9G45作为处理器， 选用AD7492作为A/D转换器，选用FIFO CY7C4261作为缓存器，FPGA芯片选用XC3S500E， WIFI 芯片选用 RT3070。然后进行软件设计，了解嵌入式Linus系统的开发环境，再进行基于FT245 USB芯片Linus驱动系统的设计，配置内核，使系统支持 USB 接口的 WIFI 无线网卡。进行基于RT3070芯片的网卡驱动移植，最后设计WIFI的驱动程序，进行WIFI联网。4.进度安排第1周第4周 资料收集，完成开题报告的撰写，英文资料的翻译。第5周第6周 拟定系统方案，进行系统总体设计。第7周第9周 电路设计 电路制作 程序编写。第10周第12周 调试测试 电路调试 软件调试。第13周第15周 数据整理，撰写论文。第16周 准备答辩。5. 文献综述（2000字以上，列出主要参考文献）5.1无线传感器网络概述 无线传感器网络的三个基本要素包括：无线传感器网络节点、覆盖环境感知对象和接收数据观察者。无线传感器网络节点是无线传感器网络最基本、最核心的组成部分，网络节点主要集成相应微型传感器、数字信号处理器、无线通信模块等功能单元。无线传感器网络节点按照执行功能的不同又可划分为传感器节点和汇聚节点，传感器节点完成数据的采集和通信链路的续传，而汇聚节点只完成收发无线网络数据和上传给接收数据观察者。覆盖环境感知对象是指节点判定为价值有效的监测目标，可以是监测区域的声音、光线、温度、震动等等，节点传感器通过数据采集、转化为系统可以识别的信息资源，并最终上传给接收数据观察者。接收数据观察者是无线传感器网络的终端用户，完成采集数据的应用。接收数据观察者一般为终端计算器或者其它监控设备，甚至是连接外部世界的万维网，数据采集观察者通过主动查询或者被动接收的方式分析无线传感器网络的数据信息，并最终完成数据的分析、应用。 图1-1 无线传感器网络节点结构示意图 无线传感器网络的传感器节点内部结构示意图如图 1-1 所示，内部分为四个模块：电源模块、传感器模块、信息处理模块和无线通信模块。传感器模块通过传感器触头感知外界信息，获取传感数据；无线通信模块通过天线与其他节点通信完成数据交换。 无线传感器网络组成形式如图 1-2 所示，其工作原理：首先分布于监控区域的众多传感器节点通过无线通信的方式自组织成一个有传播梯度的多跳网络，接着某个传感器节点采集接收到覆盖范围感知对象的有效数据，此节点将数据发送给周围选择的邻居节点，邻居节点再传递给自身周围的邻居节点，数据经过多跳传递给汇聚节点（sink），汇聚节点最后再传递给接收数据观察者，从而完成整个无线网络的通信功能。 5.2无线传感器网络WIFI技术介绍WIFI 全称 Wireless Fidelity14，又称 802.11 标准，是由一个名为“无线以太网相容联盟”（Wireless Ethernet Compatibility Alliance, WECA）的组织所发布的业界术语，中文译为“无线相容认证”。它是一种短程无线传输技术，能够在数百米范围内支持互联网接入的无线电信号。WIFI技术传输速率快，采用直接序列扩频技术，提供很高的传输速率，具有高移动性，在无线局域网覆盖范围内，地理位置的限制进行任意移动，各个节点可以不受覆盖范围广，WIFI 的覆盖范围半径在 150m，但通过中继能实现几千米的通信距离。辐射小，IEE802.11 规定的发送功率是 100mW，而一般的WIFI 设备只要6070mW。而且易扩展，传输可靠，组网便捷。5.3无线传感器网络的硬件结构在节点核心控制模块硬件结构中，ARM 作为一种嵌入式处理器，具有高性能、低功耗、低成本、体积小等优点。将 ARM 作为节点的主控制器可全面提高节点性能。本设计中，处理器选用 Ateml 公司的ARM芯片AT91SAM9G45，主频达400MHZ。动态存储器，选用 National Semiconductor 公司 64M DDR2 存储器，工作温度在系统中使用两片，总容量达 128M，大幅提高 ARM 处理器的运算效率。在节点外接口与电源管理模块中，电源管理芯片，选用 LM2596 和 LM1084，为系统提供 5V 和 3.3V 电压。两路 USB 接口控制芯片，选用双 USB 电源开关芯片 SP2526A-2USB 和两片 USB控制芯片 USBLC6-2P6 为系统提供两路 USB 接口，一路用于与无线模块进行通信，一路用于测试数据的有限读取。数据采集系统由传感器、AD 转换器、FPGA 组成，它的主要任务是把传感器采集到的模拟信号转换成数字信号。本文设计的系统所使用的 WIFI 无线网卡是 TOTOLINK 公司的 N200UA，这款 WIFI无线网卡的优点在于外置天线，我们可以根据需要选用特殊形状以及高增益的天线。无线 AP（AP，Access Point，无线接入节点）是一个包含很广的名称，它包含无线接入点（无线 AP）和无线路由器（含无线网关、无线网桥）等类设备的统称。5.4无线传感器采集系统的软件设计5.4.1嵌入式Linus系统的介绍 嵌入式 Linux 是在 Linux 的基础演变而成的，专门应用于嵌入式设备中。Linux 是开放源代码的，不存在黑箱技术，全球有众多 Linux 爱好者，对 Linux 发展提供强大技术支持。Linux 的内核小、执行效率高，非常容易裁剪定制，其系统内核最小只有约几百 KB。Linux 是完全免费，与其它昂贵操作系统如 Vxworks 相比，容易普及。Linux 是一个跨平台的操作，它适应于多种处理器，到目前为止，它可以支持几十种处理器，所以它的移植性非常好。Linux 内核的结构在网络功能完善，支持包括百兆、千兆以太网络以及无线网络。5.4.2嵌入式Linus系统的组成及其移植Linux 操作系统至少具有三部分：BootLoader（引导系统）、Kernel（内核）、Rootfs（根文件系统）。这三部分需要写到嵌入式系统的 NandFlash 中，不同的处理器，其烧写方式有所不同。本文选用 Ateml 公司的工业级 ARM 芯片 AT91SAM9G45，该处理器 BootLoader 和 Kernel 需要使用 Ateml 公司的 SAM-BA 软件通过 USB 口进行烧写，而 Rootfs 是通过网口进行烧写。 驱动程序设计好后，需要将其编译生成二进制文件，驱动程序不同于应用程序的编译，由于驱动程序是 Linux 内核的一部分，所以需要将驱动程序源码放到 Linux 内核源码中。Linux 内核是支持 WIFI 无线网络通信的，但是需要对其进行配置，才能使用。所以要配置内核，使内核支持 USB2.0，对内核进行相关的配置后，系统就完全支持 USB 接口的 WIFI 无线网卡了。参考文献：1王亚超，宁滨，基于无线传感器网络的城轨列车运行能耗数据采集系统设计D，控制工程2015.6.2林一多，高德云. 基于 ARM 的无线传感器网络 MAC 协议设计与实现 J.计算机应用，2010,30（5）:1145-1148.3林彬，基于 WIFI 的无线传感器网络检测系统的设计D.西南交通大学.2011.5.4黄茂芹，基于FPGA的实时无线传感器网络系统设计D，电子科技大学 电子与通信工程，2013.5.5曾强，张志杰，WIFI无线传感器网络的设计与实现D，中北大学，2012.6.6王赛博，刘素凯，毛先柏，无线传感器网络综述J，信息通信，2014.8.7秦邵华，无线传感器多信道通信技术的研究D，山东大学，2014.6.8孙宇,基于嵌入式 Linux 的无线传感器网络基站软件设计与实现 D,吉林大学,2009.4.9董云鹏.无线传感器网络节点的设计与实现D.北京:北京交通大学,200810潘洋.基于 ARM 的无线测控系统J.微计算机信息.2009.25(4):156-157.11阎连龙.基于 ARM 的无线数据采集系统 J.广东技术师范学院学报，2009.3:25-28.12 Camera calibration toolbox for matlab. / bouguetj/calib doc/index.html#links.13 Free space optics:technology insight. .14 Irda. http://index.cfm/.15Mipav./documentation/HTML%20Algorithms/FiltersSpatialGaussianBlur.html.16 Stan moore astronomy. /pixel size.html.17 A.Ashok, M.Gruteser, N. B. Mandayam, J. Silva, K. Dana, and M.Varga. Challenge: Mobile optical networks through visual mimo. In MobiCom 10: Proceedings of the sixteenth annual international conference on Mobile computing and networking, pages 105112, New York, NY, USA, 2010. ACM.外文文献： Characterizing Multiplexing and Diversity in Visual MIMOAbstract Mobile optical wireless has so far been limited to very short ranges for high data rate systems. It may be feasible to overcome the data rate limitations over large transmission range in optical wireless through camera receivers and light emitting transmitter arrays through a concept what we call ”visual MIMO”. In this concept multiple transmit elements of a light emitting array (LEA) are used as transmitters to communicate to the individual pixel elements of the camera which act as multiple receive elements to create the visual MIMO channel. Multiplexing information over parallel data channels albeit be very similar to RF MIMO in concept, the visual MIMO approach dramatically differs in its characterization. In visual MIMO since the received signal is essentially the image of the transmitting element, the perspective distortions in the visual channel dominate over some of the important properties of a RF wireless channel such as distance based attenuation and multipath fading. Some of the prominent perspective distortions include the reduction in the size of the image with distance and skew/rotation in the image due to angular view. Further lens blur (typically due to focus imperfection or jerks while capturing the image) can also significantly depreciate the image quality. In this paper we will detail how MIMO techniques such as multiplexing and diversity are characterized based on the effect of perspective distortions. Based our visual MIMO channel model we will derive the analytical channel capacity of the visual MIMO channel and using the same we illustrate the significance of parameters such as distance, viewing angle and blur in characterizing multiplexing and diversity in visual MIMO.I. INTRODUCTIONHigh data rate mobile optical wireless communications, has so far been limited to very short transmission ranges of less than 10m 3. To achieve transmission ranges greater than a few tens of meters in optical wireless requires highly directional light beams with very narrow angle-of-view 2. Optical wireless channels are characterized by large path loss and high background noise typically from sunlight or other ambient light sources in vicinity 16. Further the low transmit power levels in optical channels (due to output power regulations in optical sources such as LEDs and LASERs) limit the signal-to-noise ratios in these channels and thus the transmission range. In our recent work in 6, we have argued that it is now becoming feasible to achieve high data rates over large transmission ranges in mobile optical wireless communications using camera receivers through a concept what we call ”visual MIMO”. In this concept, optical transmissions by an array of light emitting devices are received by an array of photodetector (pixels) elements of a camera. The pixels in a camera can essentially be viewed as an array of highly directional receive elements. Such a structure allows allows reducing interference and noise from other light sources in the channel. Such a system offers a degree of freedom in selecting and combining a subset of the receiver elements that receive a strong signal from the transmitter and thus achieve large SNRs. This may be very similar to the antenna selection in RF-MIMO but will incur lesser overhead and non-complex processing at the camera receiver as the processing can be done in software using image processing and computer vision algorithms 6. However, the tradeoffs in the visual MIMO system, are a limited receiver sampling frequency and strong line-of-sight (LOS) requirements. We already showed in 6 that usingvisual MIMO it is possible to achieve considerable data rates over large transmission ranges with just a single transmitting element. Using MIMO techniques such as ”multiplexing” to send independent streams of bits using the multiple elements of the light transmitter array and recording over a group of camera pixels can further enhance the data rates. On the other hand the system could send the same information on all the transmit elements of the array and use diversity combining at the camera to achieve large transmission ranges due to the SNR gain. Though the multiplexing and diversity techniques are similar in concept to those in RF MIMO systems 11the visual MIMO channel with very different characteristics attributes certain unique behavior to the MIMO gains in these systems.In visual MIMO the perspective distortions in the visual channel dominate over some of the important properties of a RF wireless channel such as distance based attenuation and multipath fading. Though perspective distortions in visual channels are primarily distance dependent visual MIMO channels induce perspective distortions in the image even if the transmitter and receiver are aligned at an angle with respect to each other. Two images which are clearly separated in the image plane may look overlapped when viewed from an angle. Such distortions can depreciate the signal quality and the detection capability leading to errors and thus reduction in the data rates. Further lens blur (typically due to focus imperfection or jerks while capturing the image) also can significantly depreciate the image quality and thus reduce the information capacity. In this paper we will detail how MIMO techniques such as multiplexing and diversity are characterized based on the effect of perspective distortions in the visual MIMO channel. Based our channel model we will derive the analytical channel capacity of the visual MIMO channel and using the same we illustrate the significance of parameters such as distance, viewing angle and blur in characterizing multiplexing and diversity in visual MIMO.This paper is structured as follows; in section III we detail the visual MIMO channel model followed by the perspective dependent MIMO characterizations in section IV-C. In section V we plot the analytical channel capacity in visual MIMO and follow up with key inferences about the multiplexing and diversity characterization in visual MIMO based on the capacity plots.II. RELATED WORKPrior work in optical wireless using visible light that use photodiode receivers or imaging receivers are either limited to short ranges or require complex processing at the receiver 17, 21, 22. Though photo diodes can convert pulses at very high rates, they suffer from large interference and background light noise. This results in very low SNRs and thus short communication ranges. We showed analytically in 6, based on the visual MIMO concept, that a camera receiver outperforms photodiode receivers in terms of its channel capacity at medium to long ranges. Recently, a few sporadic projects have begun to investigate cameras as receivers, particularly for inter-vehicle communications 21 and traffic light to vehicle communications 8. Their analytical results show that communication distances of about 100m with a BER 106 are possible. Other work has investigated channel modeling 18 and multiplexing 7. While earlier work has also used cameras to assist in steering of FSO transceivers 25, the visual MIMO approach differs by directly using cameras as receiver to design an adaptive visual MIMO system that uses multiplexing at short distances but still can achieve ranges of hundreds of meters in a diversity mode.Only a few projects till now have investigated MIMO techniques for optical wireless. For shorter range systems 15, 26 show a MIMO approach for indoor optical wireless communication, 13 studied the capacity of a optical MIMO system and 19 details some work on space-time codes for optical MIMO. Earlier work by Kahn 23 investigates the use of multibeam transmitters and imaging receivers in Infra-Red systems very similar to MIMO in concept. Very recently the PixNet project 20 presents an implementation of an LCD - camera communication system that can deliver high data rates of the order of Mbps over distances of about 16m and wide view angles. PixNet uses OFDM to transmit between the LCD-camera pair similar to the pixelated - MIMO system proposed by Hranilovic and Kschischang 13. In this paper we will emphasize that regardless of any type of modulation and transmission scheme, visual MIMO can still achieve significantly high data rates by exploiting some of the uniquecharacteristics of the visual channel.III. VISUAL MIMO MODELIn the visual MIMO communications system, the optical transmit element generates a light beam (optical signal) whose output power is proportional to the electrical input power of the modulating signal, limited by the emitters peak transmission power 14, 18, 22. While RF channels are typically characterized by their impulse response that reflects the multipath environment, this aspect differs significantly for optical channels. Since the rate of change of the channel impulse response is very slow compared to the frequency of the optical signal, it is usually sufficient to use a static parameter (channel DC gain) 16 to represent the channel. For the same reason inter-symbol interference and multipath fading can be neglected in optical wireless channels. Similarly Doppler shift is negligible compared to the frequency as well. Consider the visual MIMO communication system model as shown in Fig. 1 where an optical transmitter consisting of an array of K transmitting elements communicates to a camera receiver with an array of I J pixels. The channel model for the visual MIMO system is given as, where Y 2 RIJ is the image current matrix with eachelement representing the received current y(i; j) in each pixel with image coordinates (i; j), xk 2 R represents the transmitted optical power from kth element of the LEA and Hk 2 RIJ is the channel matrix of the kth transmit element of the LEA, with elements hk(i; j) representing the channel between the kth transmit element and pixel (i; j), and N is the noise matrix. Noise in optical wireless is dominated by shot noise from background light sources and typically modeled as AWGN 16, 18. Each element n(i; j) of the noise matrix N representing the noise current at each pixel is given aswhere q is the electron charge, R is the responsitivity of the receiver characterized as the optical power to current conversion factor, Pn is the background shot noise power per unit area, s is the square pixel side length and W is the sampling rate of the receiver (equates to the frame rate of the camera).The optical signal from the kth transmit element (k =1; 2; 3 : : :K) emitting a light beam of power Pin;k will be transmitted into the channel. At the receiver, depending on the focusing of the camera and the distance between the transmitting element and the camera, the transmitting elements image may strike a pixel or a group of pixels of the detector array. The signal current in each pixel will depend on the concentration of the received signal component on that pixel which can be quantified as the ratio of the pixel area relative to the area spanned by the transmitting elements image on the detector. If ck(i; j) represents the concentration ratio of the kth transmit element of an LEA on pixel (i; j), the channel DC gain hk(i; j) from each transmit element k to the pixel(i; j) is given aswhere R is the responsitivity, Ro() is the Lambertian radiation pattern of the optical transmitting element 16 with half-power angle , Alens is the area of the camera lens, is the camera field-of-view (fov) and dk;i;j , k;i;j are the distance & viewing angle between each transmit element kand receiving pixel (i; j) respectively.Typically, since the pixel siz