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《基于无线传感器的信息采集系统的研究》一、引言随着科技的不断发展，无线传感器网络（WSN）在信息采集系统中扮演着越来越重要的角色。
无线传感器网络由大量部署在监测区域的传感器节点组成，通过无线通信技术实现信息采集、传输和处理等功能。
本文旨在研究基于无线传感器的信息采集系统，分析其技术原理、应用领域及发展趋势，为相关领域的研究和应用提供参考。
二、无线传感器信息采集系统的技术原理无线传感器信息采集系统主要由传感器节点、网关节点以及上位机等部分组成。
传感器节点负责监测环境参数，如温度、湿度、压力、光照等，并将数据传输至网关节点。
网关节点负责数据汇聚和初步处理，然后将处理后的数据通过无线网络传输至上位机。
上位机负责接收、存储和处理数据，为后续分析和决策提供支持。
（一）传感器节点传感器节点是无线传感器信息采集系统的核心部分，负责监测环境参数并转换为电信号。
传感器节点的类型多种多样，包括温度传感器、湿度传感器、压力传感器、光照传感器等。
每个传感器节点都具有微处理器、无线通信模块和电源等部分，能够实现数据的采集、处理和传输。
（二）网关节点网关节点是无线传感器信息采集系统中的中继节点，负责将传感器节点的数据汇聚并初步处理后传输至上位机。
网关节点通常具有更强的计算能力和更长的待机时间，以保证数据的可靠传输。
（三）上位机上位机是无线传感器信息采集系统的管理平台，负责接收、存储和处理来自网关节点的数据。
上位机通常具有强大的计算能力和丰富的软件资源，能够实现数据的可视化展示、存储和分析等功能。
三、无线传感器信息采集系统的应用领域无线传感器信息采集系统具有广泛的应用领域，包括智能农业、智能交通、智能医疗、环境监测等。
在智能农业中，无线传感器可以用于监测土壤湿度、温度和光照等参数，为农作物生长提供支持。
在智能交通中，无线传感器可以用于监测道路交通流量和车辆速度等信息，为交通管理和安全提供支持。
在智能医疗中，无线传感器可以用于监测患者的生命体征和健康状况，为医疗诊断和治疗提供支持。
基于无线传感器网络的温度采集系统的研制的开题报告一、选题背景现代工业生产和日常生活中，温度是一个非常重要的物理量，因此需要采集、记录和控制温度。
传统的温度采集方法通常使用有线传感器，需要铺设大量的电缆，造成了大量的物质和人力资源浪费。
而无线传感器网络（WSN）技术的出现，为温度采集提供了更加经济、高效、可靠的解决方案。
二、研究目的本项目旨在设计和研制一种基于无线传感器网络的温度采集系统，实现对室内和室外温度的实时监测和数据采集。
具体研究目的包括：1. 设计和研制一种小型化、低功耗、高灵敏度的温度传感器节点；2. 实现温度传感器节点和智能网关的数据通信和数据传输；3. 开发基于云平台的数据处理和存储系统，实现温度数据的实时监测和远程管理。
三、研究思路本项目采用以下研究思路：1. 确定温度采集的需求和技术指标，确定采集节点的技术规格，包括温度测量范围、精度、稳定性等指标；2. 设计温度传感器节点的硬件电路和软件程序，包括传感器、微处理器、通信接口和能源管理等部分；3. 编写无线传感器网络协议程序和网关程序，实现节点和网关之间的数据通信和数据传输；4. 开发基于云平台的温度数据处理和存储系统，包括数据采集、数据处理、数据分析和数据可视化等模块。
四、预期成果本项目预期实现以下成果：1. 设计和研制出高性能、低功耗的温度传感器节点，包括硬件电路和软件程序；2. 实现温度传感器节点和智能网关的数据通信和数据传输；3. 开发基于云平台的数据处理和存储系统，实现温度数据的实时监测和远程管理；4. 通过实验验证系统的性能和可靠性，向相关行业提供一种性价比高、功能齐全、易于使用的温度采集方案。
五、可行性分析无线传感器网络技术在物联网和智能家居等领域已经取得了广泛应用，相关技术已经趋于成熟。
本项目的选题符合时代要求和市场需求，具有较好的可行性。
本项目拟采用模块化设计思路，根据不同的功能模块，分别进行设计和研究，提高系统的可扩展性和可维护性。
基于无线传感器网络的数据采集系统的设计与实现的开题报告一、选题背景和意义随着科技的不断发展和应用，传感器技术已经成为了物联网的重要组成部分。
无线传感器网络作用于及其广泛，在监测环境变化、健康监测、车联网等多个领域都有着重要的应用。
数据采集系统则是无线传感器网络不可或缺的组成部分。
对数据采集系统的研究探讨能够提升无线传感器网络的性能和稳定性、提高信息收集和传输的效率，加快数据分析和共享的速度，对于促进现代化信息技术的发展和普及，具有非常重要的意义。
二、研究内容本次研究主要针对无线传感器网络中的数据采集系统进行设计和实现，具体涉及以下内容：1. 无线传感器网络数据采集系统的概述：包括无线传感器网络技术的基本概念、传感器节点的性能与功能等方面的介绍。
2. 无线传感器网络数据采集系统的需求分析：分析数据采集系统的需求，并对数据采集系统中可能存在的问题进行初步探讨。
3. 无线传感器网络数据采集系统的设计：设计无线传感器网络数据采集系统的架构，确定系统工作流程，设计传感器节点的独特标识和数据采集协议，实现数据采集、压缩、存储、传输等功能。
4. 无线传感器网络数据采集系统的实现：利用无线传感器网络开源硬件平台和软件平台实现数据采集系统的功能，同时通过实验测试数据采集系统的稳定性和可靠性。
5. 结果分析和总结：根据实验结果和采集数据对数据采集系统进行进一步的分析和总结，升华出本次研究的成果和贡献。
三、研究难点及解决方案在进行无线传感器网络数据采集系统的设计和实现时，存在着一些难点和问题：1. 网络拓扑：如何设计合理的网络拓扑，避免信号干扰和数据丢失。
2. 传输协议：如何设计高效和安全的数据传输协议，保证数据的可靠性和安全性。
3. 数据存储和处理：如何实现大量数据的存储和处理，以便于后续的分析和利用。
4. 系统的稳定性和可靠性：如何保证数据采集系统的稳定性和可靠性。
针对以上问题，我们可以采用以下的解决方案：1. 网络拓扑方案应根据实际需求和环境特点进行适当调整，例如采用分簇的方式进行网格布局，减少传输距离和信号干扰。
基于无线传感器网络的病人体温采集系统的研究与设计的开题报告一、选题的背景与意义随着医学技术的不断发展，病人的体温是一个非常重要的生理指标，能够反映出人体健康状态的变化。
在医疗机构和家庭养护中，对于病人体温的检测和监测是必不可少的，但传统的温度计需要人工不断地将其放入病人嘴里、腋下或肛门括约肌等部位进行测量，很不方便。
随着无线传感器网络技术的日益成熟，利用无线传感器网络实现对病人体温的远程采集和监测成为了一个研究的热点。
本项目旨在基于无线传感器网络技术，设计一种病人体温采集系统，使医生、护士或家庭养护者可以及时地获取病人的体温信息，以便及时采取相应的治疗措施，提高治疗效果，降低病患和养护者的负担。
二、研究内容和方法本项目主要包括以下内容：1.设计无线传感器网络体温采集系统：首先，设计一种基于无线传感器网络的体温传感器节点，可以实时监测病人体温，并将数据通过无线传输协议发送到网关节点。
2.实现病人体温数据的处理和管理：通过对采集到的数据进行处理和分析，生成相应的报表和统计信息，包括实时体温、平均体温、最高/最低体温等指标，并将其存储在云数据库中。
3.设计基于Web的用户界面：设计一个直观友好的Web界面，使医生、护士或家庭养护者可以方便地浏览和查询病人的体温数据，并及时发现和处理异常情况。
本项目采用的研究方法包括：1.文献调研：对于病人体温采集系统的相关技术和应用进行深入调研，了解目前的热点和趋势。
2.硬件设计：设计病人体温传感器节点硬件电路，包括温度传感器、微控制器、无线通信模块等组成部分。
3.软件设计：编写嵌入式系统的固件程序，实现传感器节点的数据采集、处理和无线传输等功能；同时，设计基于Web的用户界面，实现数据的显示和查询功能。
4.系统测试：对整个系统进行测试和优化，确保数据的准确性和可靠性。
三、预期成果和创新点本项目预期实现一种基于无线传感器网络的病人体温采集系统，并设计一个直观友好的用户界面，能够实现以下功能：1.实时监测病人的体温，提供数据可视化的实时显示。
基于无线传感器网络的数据采集技术研究的开题报告一、研究背景与意义随着现代科技的不断发展和人们对信息处理和管理的需求不断增加，无线传感器网络（Wireless Sensor Networks, WSN）已经成为目前互联网、物联网等领域研究的重要方向之一。
WSN是由许多传感器节点构成的分布式系统，能够对周围的环境进行数据采集、处理、传输和控制，因此在环境监测、农业、医疗、安防等领域都有广泛应用。
在WSN中，数据采集是一个非常重要的环节。
目前，传统的数据采集方式采用有线连接，成本较高，操作不灵活，容易受到环境影响。
而无线传感器网络正好能够解决这些问题，但是如何在无线传感器网络中实现高效、准确的数据采集，仍然是一个亟待解决的问题。
因此本文旨在研究基于无线传感器网络的数据采集技术，为WSN的应用提供技术支持，为推动物联网技术的发展做出一定的贡献。
二、研究内容1、WSN基础知识概述介绍WSN的基础知识，包括WSN的发展历程、WSN的结构、传感器节点的功能及通信方式等。
2、数据采集模型设计本研究将设计一种基于数据流模型的数据采集模型。
该模型是基于数据流处理的方式进行设计，通过对传感器数据进行处理和编码，达到降低传输数据的信息冗余，提高数据传输效率的目的。
3、数据采集协议设计针对现有的数据采集协议存在的问题，本研究将设计一种特定的数据采集协议，以提高数据采集效率和准确性。
该协议将重点考虑传输的效率和网络的负载均衡问题。
4、实验设计本研究将在模拟实验中对设计的数据采集模型及协议进行测试，并对实验结果进行分析与比较。
三、研究计划1、第一阶段：调研与文献综述时间：2022.01-2022.03任务：调研WSN的相关知识及数据采集技术，阅读相关文献，明确研究思路和设想。
2、第二阶段：数据采集模型设计时间：2022.04-2022.06任务：设计基于数据流模型的数据采集模型，包括数据处理和编码等。
3、第三阶段：数据采集协议设计时间：2022.07-2022.09任务：设计一种特定的数据采集协议，以提高数据采集效率和准确性。
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：2016.3.3~2015.6.22指导教师签字系主任签字年月日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 BER10 6 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 emitter’s 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 opticaltransmitter 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 RI J 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 RI J 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 element’s 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 element’s 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 size is very small (order ofmicrons), the difference in distance dk;i;j and the viewing angle -k;i;j between each element of the transmitter array and every pixel is negligible. Therefore we refer to the distance dk;i;j = d and the viewing angle -k;i;j = - as the perpendicular distance and the angle between the transmitter array and image detector planes respectively. Hence the channel between each transmit element k and each pixel (i; j), characterized by hk(i; j), is primarily dependent on the concentration ratio ck(i; j) which can expressed as,where, s, f, lk are the pixel edge length, camera focal length and diameter of kth transmit element (considering a circular transmitting element) respectively. The amount of concentration of the signal per pixel is also dependent on the amount of blur in the image due to the lens. Typically, lens blur is modeled as a Gaussian function [12] and the amount of blur in the image is quantified by its standard deviation ( blur). The lens essentially acts like a filter with the blur function as its impulse response. Thus the image of the transmit element can be viewed as a result of the projected image convolving with the blur function over the detector area.I(:) is an indicator function indicating whether a pixel (i; j) receives a signal from the transmit element k or not, and is referenced in terms of the distance from pixel at the center of the transmit element’s image (irefk ; jrefk ). Given the spatial coordinates of the transmitting elements of an LEA with respect to the camera reference we can determine the image center coordinates of those transmit element through optical ray-tracing techniques in conjunction with some basic computer vision theory [9].IV. PERSPECTIVE DEPENDENT MIMO GAINSWhile the channel model in (1) resembles that of the familiar RF MIMO channel model, in fact it is significantly different from that. In RF MIMO systems, the channel matrix is typically a rich scattering matrix (usually full rank) whose entries are modeled well as independent and identically distributed random variables [10]. Further, this property allows the RF MIMO system to exploit either diversity and or multiplexing gains in data transmission which primarily depend on the multipath fading in the RF channel. The fact that the communication system here uses light as the communication medium, requires line of sight at the receiver, and the nature of the concentration function of the camera, renders some unique multiplexing and diversity characterizations different from RF MIMO.A. Resolvability of imagesThe notion of ‘parallel’channels to obtain the multiplexing data rate gains can be achieved only if the circumference of two transmit elements as seen on the image plane are separated by atleast a threshold ( ) number of pixels in both dimensions (horizontal and vertical). As we see in Fig. 2 even if the circumference of the two transmit element images are separated by one pixel they may not be resolvable because of the blur in the image. Hence we set a threshold distance of separation between the image circumferences, = 2p 2ln2 blur, equal to the full-width-half maximum (FWHM) of the Gaussian lens-blur function typically used as a parameter for image resolution in analyzing fine detailed astronomical and medical images [4], [5]. The distance of separation between the images of the transmit elements can be determined by perspective projection analysis (as described in [6]) considering circular transmitting elements. Given a fixed-focal length f of the camera, pixel side length s and a spatial distance between the circumference of two adjacent LEDs, the circumference of two transmit element images will be separated by im = fds pixels in each dimension. Therefore the separation between thecircumference of two transmit element images will be equal to the threshold (=s) at a distance d = f between the LEA and camera. This implies that, multiplexing in visual MIMO ispossible only when d d and when d > d each transmit element has to transmit the same information whereby diversity combining at the receiver can ensure an SNR gain and hence an equivalent capacity gain.B. Dependence on viewing angleWe can observe in equation (3) that the channel quality depreciates with the viewing angle - (angle between the camera image plane and LEA surface plane). Two images which are clearly separated in the image plane may look overlapped when viewed from an angle. Such distortions can significantly depreciate the signal quality and the detection capability leading to errors and thus reduction in the data rates. Moreover such an angular view also reduces the achievable multiplexing transmission range. This is because when the camera image detector plane is at an angle - to the transmitter array the effective spatial separation between two neighboringtransmit elements becomes (as shown inFig. 3). From the earlier discussion on the resolvability of images, it implies that the distance upto which multiplexing can be achieved in visual MIMO then reduces toC. Distance dependent MIMO gainsIn the visual MIMO channel, for a static transmitter and receiver, the image of the LEA transmit elements captured by the camera spans one pixel or multiple pixels. Further, the image plane is spanned by images of each transmit element clearly delineated and the size of image span depending on the focus (concentration ratio) of the camera. As illustrated in Fig. 4, at short distances between the transmitter and receiver, each transmitting element of the LEA looks clearly focused on a unique set of pixels and the images of these elements can be detected from the complete image. In contrast, at a large distance between the transmitter and receiver, the image of each transmit element looks clearly unfocused and thus the signal from all the transmitting elements of the LEA is directed to typically one or few pixels. This suggests that at short distances, the system can offer large ”multiplexing” gains by using the transmitting elements to signal independent bitstreams or equivalently realiz ing ”parallel” channels. On the other hand, at large distances, there can only be a ”diversity” gain where by the same bits are signaled on each of the transmit elements. These distance dependent gains in visual MIMO is in contrast to the RF MIMO channel, where the rich scattering channel matrix typically allows a continuous trade-off between diversity and multiplexing gains [24], [27].Fig. 4. Distance dependent Multiplexing and Diversity modesV. VISUAL MIMO CHANNEL CAPACITYTo quantify the perspective dependent multiplexing and diversity gains in visual MIMO we use the channel capacity of the visual MIMO channel as a metric which is given as,where W is the receiver sampling rate (camera frame-rate), d is the threshold multiplexing distance from equation (6). SNRcam;k is the signal-to-noise ratio of the kth LED at the camera receiver [6] which is expressed in terms of the transmit power xk, the channel DC gain hk(i; j) from equation (3) and AWGN noise nk(i; j) from equation (2). I(:) is the indicator function, from equation (5). We plot the channel capacity from equation (7), for an exemplary visual MIMO system, where the transmit elements of the LEA are light emitting diodes (LEDs) and the receiver is a machine vision camera (Basler Pilot piA640), over a range of distances d (Fig. 5) and over different viewing angles -(Fig. 6). The underlying parameters used in our analysis are summarized in Table I. Inferences: From the analytical capacity plots we draw few notable inferences that relate to the multiplexing and diversity characterizations in visual MIMO.The visual MIMO system with no blur can achieve capacities of the order of Mbps even at long distances of about 90m. Blurring certainly reduces multiplexing range but still medium ranges of 30-40m are achievable at high data rates. The data rate gains at these distances are attributed to multiplexing where each LED sends an independent stream of bits over parallel channels. The transitions in the plot (for the multi LED cases) indicate the switch from multiplexing to diversity mode. The capacity gains due to diversity at the long distances, though may not be significant comparable to the multiplexing gains at shorter distances, are still close to an order of magnitude gain compared to the single LED system.Fig. 5. Visual MIMO channel Capacity versus distance (- = 0)Fig. 6. Visual MIMO channel Capacity versus angle (d constant)A visual MIMO system will have to switch between the multiplexing and diversity modes in discrete intervals based on distance and angle unlike RF MIMO where the gains in these modes could be achieved simultaneously but follow a continuous trade-off in performance. Moreover, a visual MIMO system will have to switch autonomously between these modes depending on the orientation of the receiver with respect to the transmitter in order to leverage the gains. This suggests that the throughput of visual MIMO links can be significantly improved through rate adaptation techniques, which adapt the transmission scheme to the receiver perspective. In RF MIMO, in order to select the mode of operation (multiplexing and/or diversity) the channel state information (CSI) has to be known or estimated which either incurs a data overhead and/or complex receiver processing. But in visual MIMO, since the optical channel is deterministic in nature the overhead in selecting either of the modes of operations will be very less because the need to send preamble bits to determine the channel information (distance,angle etc.) may be obviated by the use of efficient computer vision techniques [6]. Since fading is negligible the complexity in estimating the CSI to exploit MIMO techniques is lesser than in RF but still leads to interesting challenges in computer vision and image processing. The visual MIMO channel capacity is consistent over a wide range of viewing angles (small or large depends on distance). We see that the system can achieve large multiplexing gains at short distances and at almost all viewing angles which implies that the system would be robust to any misalignment between the transmitter and receiver. Its cleat that at large distances (of the order of 75m), due to the effect of lens blur, the LEDs may not be resolved easily even at - = 0 and hence at such distances where multiplexing will fail but using diversity over all angles can still offer an order of gain in data rates. Such consistency in data rates over angular misalignment is important especially in mobile settings as the choice of multiplexing and/or diversity depends largely on the orientation of the优于光电二极管接收器的信道容量在中长期范围。
开题报告基于无线传感网络的数据采集系统设计1 选题的背景、意义近年来，微机电系统（MEMS）、低功耗高集成度电子器件及无线通信技术的快速发展，导致低成本、微体积、多功能的无线传感器节点设备的出现。
在未来几年内，无线传感器网络（WSN）将对人们的日常生活和几乎所有军工业领域带来巨大影响。
尤其随着无线通信和射频技术的不断发展，具有布局灵活、成本低廉、组网便捷的特点的无线数据采集系统正在逐渐取代传统的有线数据采集系统。
无线传感器网络（简称 WSN，wi r e l es s s ens or net wor ks ）是综合了传感器技术、微电子技术、现代网络及无线通信技、，嵌入式计算机技术、分布式信息处理技术等各种先进现代技术，通过各类集成化微型传感器协作能够实时监测、感知和采集各种环境和监测对象的信息，并将采集到的这些信息以无线自组多跳网络方式传输到用户终端的网络技术。
WSN能以最低的成本、最大的灵活性连接任何有通讯需求的终端设备，进行数据采集和指令发送，具有一定的移动和动态调整的能力。
无线传感网络是目前信息科学领域中一个活跃的研究分支，其经历了三个阶段：智能传感器、无线智能传感器和无线传感器网络。
智能传感器是传感器中嵌入计算能力，使传感器节点不仅具有数据采集能力，且具有滤波和信息处理能力[ 1] ；无线智能传感器是在智能传感器的基础之上增加了无线通讯能力，大大延长了传感器的感知触角，降低了传感器的工程实施成本；无线传感器网络则是将网络技术引入到无线智能传感器中，使传感器不再是单个感知元件而是能够交换信息和协调控制的有机结合体，必将成为下一代互联网的重要组成部分。
无线传感器网络由大量工能相同或不同的无线传感器节点以自组织方式构成。
无线传感器节点是网络的基本单元，其体积小、成本低、具有通信能力和数据处理能力，节点的稳定运行是整个网络可靠性的基本保障【2】。
根据不同的应用目的，网络互联的传感器节点分布于监测区域中，以测量其周边环境中的光热、声音、电磁、化学成分等各种信号，从而检测到包括温度、光强度、噪声、压力、土壤成分、水质和各种移动物体的大小、速度以及移动方向等物质现象，进而在网络中完成数据处理和信息融合，最后将有用的信息数据传输给用户。
一种无线传感器网络系统的分析与设计的开题报告一、研究背景随着科技的不断发展，物联网技术越来越成熟，无线传感器网络已经成为了现代化的数据采集和监测方式之一。
无线传感器网络通过感知节点和基站之间的无线连接进行信息传输，能够实现远距离、多目标、实时监测。
因此，在环境监测、农业生产、交通管理、资源管理等方面有着广泛的应用。
二、研究目的本次研究的目的是设计一种基于无线传感器网络的环境监测系统，对该系统进行分析和评估，研究系统的优化方法，以提高其性能和节能程度，并为相关领域的技术人员提供相关的分析和设计方法。
三、研究内容（1）研究现有的无线传感器网络系统，分析其优缺点。
了解当前的研究热点和难点，针对问题提出解决方案。
（2）设计一种基于无线传感器网络的环境监测系统，包括感知节点、数据传输、数据接收和处理等方面。
优化系统的性能指标，例如数据传输速率、传输距离、系统延迟等。
（3）进行数学建模和仿真分析，分析系统的节点密度、通信协议、存储容量、能耗等因素对系统性能的影响。
（4）对设计的环境监测系统进行实验验证，对系统进行性能测试和数据分析。
（5）通过对实验结果的分析，提出系统的优化措施，并给出实现的方案。
四、研究意义本次研究设计的基于无线传感器网络的环境监测系统，将在环境监测、农业生产、交通管理、资源管理等领域应用，对于有效监测数据、提高产品质量、优化资源利用有着重要的意义。
同时，本研究对于无线传感器网络系统的研究有着重要的意义，可以为相关技术领域的工作者提供参考，并且可以推动无线传感器网络系统的技术发展，推动物联网技术的进一步发展。
无线传感器网络管理系统设计与实现的开题报告一、研究背景和意义无线传感器网络（Wireless Sensor Networks, WSN）是由大量的微型传感节点组成的自组织网络，这些节点分布在一个待监测的场景中，能够感知和采集环境中的各种信息，并将这些信息传输到中心节点进行处理。
因为无线传感器网络具有方便、易用、低成本、快速部署等特点，已经得到了广泛的应用，如环境监测、农业、智能交通、智能家居等领域。
但无线传感器网络管理系统的设计与实现仍然存在着一些问题，比如无线传感器网络节点数量较大，网络结构复杂且易受到外部干扰，导致网络的稳定性、可靠性和安全性受到威胁。
因此，设计一套高效、可靠、安全的无线传感器网络管理系统已经成为了无线传感器网络研究领域中的一个重要问题。
二、研究内容和目标本课题的研究内容主要是针对无线传感器网络管理系统的设计与实现，旨在探究一套高效、可靠、安全的无线传感器网络管理系统，包括以下内容：1. 照明无线传感器网络的基本框架和工作原理，探究无线传感器网络的节点组成及配置方法等。
2. 分析无线传感器网络所面临的安全问题，重点研究节点安全、数据安全和协议安全等方面。
3. 设计一种符合无线传感器网络架构的节点管理模型，包括节点的初始配置、网络动态维护和管理等，解决网络的稳定性，可靠性和安全性问题。
4. 开发一套无线传感器网络管理系统，该系统可以对网络中的节点进行初始化、部署、升级、监控、维护等操作，以实现对无线传感器网络的有效管理。
5. 对设计的无线传感器网络管理系统进行测试评估，并与现有的管理系统进行比较分析，验证系统的可行性和有效性。
本课题的最终目标是设计一套高效、可靠、安全的无线传感器网络管理系统，具备较高的实用性和推广价值。
三、研究方法和技术路线本研究采用以下方法和技术路线：1. 研究资料法：通过收集文献、调查问卷、网络查找等方法，了解无线传感器网络的发展现状和研究热点，寻找本研究的切入点和问题。
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：2016.3.3~2015.6.22指导教师签字系主任签字年月日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 BER10 6 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 emitter’s 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 opticaltransmitter 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 RI J 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 RI J 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 element’s 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 element’s 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 size is very small (order ofmicrons), the difference in distance dk;i;j and the viewing angle -k;i;j between each element of the transmitter array and every pixel is negligible. Therefore we refer to the distance dk;i;j = d and the viewing angle -k;i;j = - as the perpendicular distance and the angle between the transmitter array and image detector planes respectively. Hence the channel between each transmit element k and each pixel (i; j), characterized by hk(i; j), is primarily dependent on the concentration ratio ck(i; j) which can expressed as,where, s, f, lk are the pixel edge length, camera focal length and diameter of kth transmit element (considering a circular transmitting element) respectively. The amount of concentration of the signal per pixel is also dependent on the amount of blur in the image due to the lens. Typically, lens blur is modeled as a Gaussian function [12] and the amount of blur in the image is quantified by its standard deviation ( blur). The lens essentially acts like a filter with the blur function as its impulse response. Thus the image of the transmit element can be viewed as a result of the projected image convolving with the blur function over the detector area.I(:) is an indicator function indicating whether a pixel (i; j) receives a signal from the transmit element k or not, and is referenced in terms of the distance from pixel at the center of the transmit element’s image (irefk ; jrefk ). Given the spatial coordinates of the transmitting elements of an LEA with respect to the camera reference we can determine the image center coordinates of those transmit element through optical ray-tracing techniques in conjunction with some basic computer vision theory [9].IV. PERSPECTIVE DEPENDENT MIMO GAINSWhile the channel model in (1) resembles that of the familiar RF MIMO channel model, in fact it is significantly different from that. In RF MIMO systems, the channel matrix is typically a rich scattering matrix (usually full rank) whose entries are modeled well as independent and identically distributed random variables [10]. Further, this property allows the RF MIMO system to exploit either diversity and or multiplexing gains in data transmission which primarily depend on the multipath fading in the RF channel. The fact that the communication system here uses light as the communication medium, requires line of sight at the receiver, and the nature of the concentration function of the camera, renders some unique multiplexing and diversity characterizations different from RF MIMO.A. Resolvability of imagesThe notion of ‘parallel’channels to obtain the multiplexing data rate gains can be achieved only if the circumference of two transmit elements as seen on the image plane are separated by atleast a threshold ( ) number of pixels in both dimensions (horizontal and vertical). As we see in Fig. 2 even if the circumference of the two transmit element images are separated by one pixel they may not be resolvable because of the blur in the image. Hence we set a threshold distance of separation between the image circumferences, = 2p 2ln2 blur, equal to the full-width-half maximum (FWHM) of the Gaussian lens-blur function typically used as a parameter for image resolution in analyzing fine detailed astronomical and medical images [4], [5]. The distance of separation between the images of the transmit elements can be determined by perspective projection analysis (as described in [6]) considering circular transmitting elements. Given a fixed-focal length f of the camera, pixel side length s and a spatial distance between the circumference of two adjacent LEDs, the circumference of two transmit element images will be separated by im = fds pixels in each dimension. Therefore the separation between thecircumference of two transmit element images will be equal to the threshold (=s) at a distance d = f between the LEA and camera. This implies that, multiplexing in visual MIMO ispossible only when d d and when d > d each transmit element has to transmit the same information whereby diversity combining at the receiver can ensure an SNR gain and hence an equivalent capacity gain.B. Dependence on viewing angleWe can observe in equation (3) that the channel quality depreciates with the viewing angle - (angle between the camera image plane and LEA surface plane). Two images which are clearly separated in the image plane may look overlapped when viewed from an angle. Such distortions can significantly depreciate the signal quality and the detection capability leading to errors and thus reduction in the data rates. Moreover such an angular view also reduces the achievable multiplexing transmission range. This is because when the camera image detector plane is at an angle - to the transmitter array the effective spatial separation between two neighboringtransmit elements becomes (as shown inFig. 3). From the earlier discussion on the resolvability of images, it implies that the distance upto which multiplexing can be achieved in visual MIMO then reduces toC. Distance dependent MIMO gainsIn the visual MIMO channel, for a static transmitter and receiver, the image of the LEA transmit elements captured by the camera spans one pixel or multiple pixels. Further, the image plane is spanned by images of each transmit element clearly delineated and the size of image span depending on the focus (concentration ratio) of the camera. As illustrated in Fig. 4, at short distances between the transmitter and receiver, each transmitting element of the LEA looks clearly focused on a unique set of pixels and the images of these elements can be detected from the complete image. In contrast, at a large distance between the transmitter and receiver, the image of each transmit element looks clearly unfocused and thus the signal from all the transmitting elements of the LEA is directed to typically one or few pixels. This suggests that at short distances, the system can offer large ”multiplexing” gains by using the transmitting elements to signal independent bitstreams or equivalently realiz ing ”parallel” channels. On the other hand, at large distances, there can only be a ”diversity” gain where by the same bits are signaled on each of the transmit elements. These distance dependent gains in visual MIMO is in contrast to the RF MIMO channel, where the rich scattering channel matrix typically allows a continuous trade-off between diversity and multiplexing gains [24], [27].Fig. 4. Distance dependent Multiplexing and Diversity modesV. VISUAL MIMO CHANNEL CAPACITYTo quantify the perspective dependent multiplexing and diversity gains in visual MIMO we use the channel capacity of the visual MIMO channel as a metric which is given as,where W is the receiver sampling rate (camera frame-rate), d is the threshold multiplexing distance from equation (6). SNRcam;k is the signal-to-noise ratio of the kth LED at the camera receiver [6] which is expressed in terms of the transmit power xk, the channel DC gain hk(i; j) from equation (3) and AWGN noise nk(i; j) from equation (2). I(:) is the indicator function, from equation (5). We plot the channel capacity from equation (7), for an exemplary visual MIMO system, where the transmit elements of the LEA are light emitting diodes (LEDs) and the receiver is a machine vision camera (Basler Pilot piA640), over a range of distances d (Fig. 5) and over different viewing angles -(Fig. 6). The underlying parameters used in our analysis are summarized in Table I. Inferences: From the analytical capacity plots we draw few notable inferences that relate to the multiplexing and diversity characterizations in visual MIMO.The visual MIMO system with no blur can achieve capacities of the order of Mbps even at long distances of about 90m. Blurring certainly reduces multiplexing range but still medium ranges of 30-40m are achievable at high data rates. The data rate gains at these distances are attributed to multiplexing where each LED sends an independent stream of bits over parallel channels. The transitions in the plot (for the multi LED cases) indicate the switch from multiplexing to diversity mode. The capacity gains due to diversity at the long distances, though may not be significant comparable to the multiplexing gains at shorter distances, are still close to an order of magnitude gain compared to the single LED system.Fig. 5. Visual MIMO channel Capacity versus distance (- = 0)Fig. 6. Visual MIMO channel Capacity versus angle (d constant)A visual MIMO system will have to switch between the multiplexing and diversity modes in discrete intervals based on distance and angle unlike RF MIMO where the gains in these modes could be achieved simultaneously but follow a continuous trade-off in performance. Moreover, a visual MIMO system will have to switch autonomously between these modes depending on the orientation of the receiver with respect to the transmitter in order to leverage the gains. This suggests that the throughput of visual MIMO links can be significantly improved through rate adaptation techniques, which adapt the transmission scheme to the receiver perspective. In RF MIMO, in order to select the mode of operation (multiplexing and/or diversity) the channel state information (CSI) has to be known or estimated which either incurs a data overhead and/or complex receiver processing. But in visual MIMO, since the optical channel is deterministic in nature the overhead in selecting either of the modes of operations will be very less because the need to send preamble bits to determine the channel information (distance,angle etc.) may be obviated by the use of efficient computer vision techniques [6]. Since fading is negligible the complexity in estimating the CSI to exploit MIMO techniques is lesser than in RF but still leads to interesting challenges in computer vision and image processing. The visual MIMO channel capacity is consistent over a wide range of viewing angles (small or large depends on distance). We see that the system can achieve large multiplexing gains at short distances and at almost all viewing angles which implies that the system would be robust to any misalignment between the transmitter and receiver. Its cleat that at large distances (of the order of 75m), due to the effect of lens blur, the LEDs may not be resolved easily even at - = 0 and hence at such distances where multiplexing will fail but using diversity over all angles can still offer an order of gain in data rates. Such consistency in data rates over angular misalignment is important especially in mobile settings as the choice of multiplexing and/or diversity depends largely on the orientation of the优于光电二极管接收器的信道容量在中长期范围。
