红外传感器网络文献翻译

黄河科技学院毕业设计（文献翻译） 第9页 单位代码 01 学 号 分 类 号 TNO 密 级 文献翻译红外传感器网络 院（系）名称信息工程学院 专业名称电子信息工程 学生姓名 指导教师 2013年3月28日红外传感器网络摘要：无线传感器网络(WSN)已成为最近的一个研究热点。无线传感器网络在大范围的领域得到应用，主要应用于商业、军事以及住宅，创造了巨大的效益。在这个项目中，我们设计了一个计数系统，这个系统部署在交通控制、资源管理和人力的流量控制之中，用于追踪检测区以及相应的移动方向。我们的设计是基于自制的红外传感模块板，用于无线传感器网络之间的联系。我们的系统设计包括红外传感模块设计、传感器节点通讯、系统聚类、建筑和部署。我们进行了一系列的计数实验来评估系统的性能，论证了高效率的移动对象的有效性。关键词：红外辐射，无线传感器节点1 介绍红外 红外辐射是指波长在可见光与无线电波之间的部分电磁辐射。现在红外线已经被广泛应用，包括数据通讯、夜视装置,物件追踪等等。由于它容易产生并且受到电磁波的干扰很少，在红外数据通信中，人们通常使用红外线。把电视遥控器作为一个例子, 在每个人的家里可以被发现。当按钮被推后，红外遥控系统利用红外发光二极管(led)散发出红外(红外线)信号。按钮被推后，不同模式显示相应的按钮脉冲。在不受干扰的情况下，允许控制多种电器比如电视机、录像机、有线电视盒 ,系统通常有序言和一个地址进行同步识别来源的接收机的位置和红外信号。为了编码数据,系统通常用不同脉冲的宽度(脉宽调制)或宽度之间的间隔空间调制脉冲(脉冲)。另一种受欢迎的系统：双相编码,利用信号转换来传递信息。在载波频率中，实际上每个脉冲都是一阵红外。“高”的含义是一阵红外能量载波频率和 “低”体现了一种不在红外能量。这没有编码标准。然而,许多家庭使用他们自己的一些quasi-standards专有的编码系统的娱乐设备，确实存在。这些包括RC-5、RC -(六)、REC-80。此外,许多汽车制造商,如NEC、也成立他们自己的标准。2 无线传感器网络 无线传感器网络(WSN)是一种无线网络，这种无线网络是由大量不同传感器节点自主使用传感器监测不同地点的物理或环境条件,如温度、音响、振动、压力、运动或污染物。在传感器网络中每个节点通常配备了无线通信设备,一个小的单片机,一个或多个传感器,和一种能源,通常是一个电池。大小的单一传感器节点可以一样大,可作为鞋盒小面积的一粒尘埃,这取决于不同的应用程序。相同的传感器节点是同样的变量,从几美元到几百美分,根据无线传感器网络的大小和复杂性的要求确定单一传感器节点。在大小和成本约束条件下,传感器节点对相应的限制有可用的输入,例如精力,记忆,算法的计算速度和带宽。无线传感器网络的发展(WSN)起初的目的是用在军事方面,如战场上的监视。由于微-电子机械系统(MEMS)技术和无线网络技术的进步,嵌入式处理器、无线传感器网络的受益在许多平民的日常生活中得到广泛应用,包括生境监测、医疗应用,以及家庭自动化。3无线传感器网络的类型由于操作系统在传感器网络应用及在传感器网络资源约束条件下的硬件平台的特殊要求，相比通用传感器网络节点，无线传感器网络节点通常不那么复杂。这种操作系统不需要包括支持用户界面。此外,在资源约束下，内存方面和内存映射的硬件支持机制如虚拟内存或者不必要或无法实现。TinyOSTinyOS可能是第一个专门设计无线传感器网络的操作系统。在大多数其它操作系统基础上,TinyOS事件驱动编程模型代替多线编程模式。TinyOS节目都是合成事件处理器和任务以得到completion-semantics。当一个外部事件发生时,例如一个到来的数据分组或阅读,TinyOS传感器调用合适的事件处理程序来处理这个事件。这种TinyOS系统和节目都是写在一种特殊的编程语言上。nesC是一种延伸到C程序设计的语言。NesC是用来侦测的种族条件和处理任务间事件。也有操作系统允许这样的例子程序,使用c操作系统包括Contiki。在网络上支持加载模块和支持运行时，Contiki是用来设计标准精灵的文件。Contiki事件驱动,就像TinyOS内核,但制度的支持每个基础操作的多线程。不像其他无线传感是事件驱动的Contiki内核,其核心是基于先发制人的多线程。先发制人的多线程、应用程序不需要明确的微处理器就能有其他产量过程。4 介绍无线传感器节点感器节点，是能够进行处理，收集感觉信息，并与其他连接的网络中的节点进行通信的无线传感器网络中的一个节点。由于无线传感器节点的微型电子传感器设备，他们只能配备有限的电源传感器节点体积小，故传感器节点消耗能量极低，适用于无人操作，并能很好的适应环境。传感器节点的主要组成部分包括传感器，微控制器，收发器，和电源。传感器是硬件设备，通过物理条件的变化，能够产生可测量的电信号，如光密度和声音密度。由传感器收集的连续的模拟信号由模拟 - 数字转换器数字化。然后，数字化的信号被传递到控制器，用于进一步处理。无线传感器网络大多数是被动和全向传感器。它没有实际操作环境，并能主动探测，基于被动和全向传感器来感知数据，而在这些测量中没有涉及 “方向”的概念。人们通常部署用于检测热（如热传感器），光传感器（如红外传感器），超音（如超声波传感器），或电磁（如磁传感器）。在微控制器执行的任务中，可以配备一个以上的传感器的传感器节点，用于处理数据和控制传感器节点中的其他组件的操作。传感器节点可用于信号处理，根据需要或要求的物理量来检测传感器节点。它可以执行并处理中断，此外，它可用用于应用程序特定的计算。发射器和接收器被合并成一个单一的设备并作为收发器中所使用的传感器节点。收发器，可用于一个传感器节点的的相邻传感器和汇聚节点（一个中央接收器）之间的信息交换。收发器的运行状态有，发送、接收、空闲、睡眠。其电力被储存在电池或电容器内。电池的主要来源为传感器节点的电源。使用两种类型的电池，这两种电池分为充电和非充电。他们根据电化学材料，如镍镉电池（镍镉），镍锌（镍锌），镍金属氢化物（镍氢）和锂离子电池的电极分为不同的电池。电流传感器的开发能够更换他们的能源来源方式，这些能源主要来源于太阳能。两个主要的节能政策分为动态电源管理（DPM）和动态电压调节（DVS）。 DPM可根据不同的传感器节点分为不使用或主动关闭部分。 DVS方案的电压随着频率的不同而消耗不同的电压，能够降低电力的二次消耗，减小能量的消耗。5 挑战无线传感器网络的设计和实施的主要挑战是能量的限制，硬件的限制，以及覆盖的面积。无线传感器网络节点的能量是最稀缺的资源，它决定了无线传感器网络的生命周期。无线传感器网络被大量部署在各种环境下，包括远程和敌对地区，它是特设通信的关键。由于这个原因，算法和协议需要最大的寿命，其容错性需自己根据需要自行配置。在硬件上所面临的挑战是微型传感器节点的生产成本低。为了实现这些目标，电流传感器节点通常具有有限的计算能力和内存空间。因此，WSN的应用软件和算法，应具有最大限度的优化和浓缩。为了在每个信号节点的覆盖区域最大限度地提高稳定性和鲁棒性，具有低功耗的多跳通信的是优选的。此外，为了处理大型网络的规模，大量的WSN设计的协议必须被分发。6 研究问题在研究人员感兴趣的各个领域中，包括无线传感器网络的设计，实施和操作。其中包括硬件，软件和软件和硬件之间的基础设施。由于无线传感器网络通常部署在电池供电的节点资源受限的环境中，研究人员主要集中在能源优化，改善覆盖范围，误差减少，传感器的网络应用，数据的安全性，传感器节点的移动性和数据包的问题传感器之间的路由算法的研究。在文学作品中，一大群研究人员在WSN的研究中倾注了大量的精力。他们主要集中在各个领域，包括物理性能，传感器的测试，智能节点合作的安全性，媒体存取，随机和确定性放置的传感器覆盖范围，对象定位跟踪，传感器位置确定，处理，高效节能的广播和积极调度，能量守恒路由，连接，数据传播和收集，传感器中心的质量路由，拓扑控制和维护等。/view/2099de25a5e9856aad.html附：英文原文An Infrared Sensor Network Abstract：Wireless Sensor Network (WSN) has become a hot research topic recently. Great benefit can be gained through the deployment of the WSN over a wide range of applications, covering the domains of commercial, military as well as residential. In this project, we design a counting system which tracks people who pass through a detecting zone as well as the corresponding moving directions. Such a system can be deployed in traffic control, resource management, and human flow control. Our design is based on our self-made cost-effective Infrared Sensing Module board which co-operates with a WSN. The design of our system includes Infrared Sensing Module design, sensor clustering, node communication, system architecture and deployment. We conduct a series of experiments to evaluate the system performance which demonstrates the efficiency of our Moving Object Counting system. Keywords:Infrared radiation，Wireless Sensor Node1 Introduction to Infrared Infrared radiation is a part of the electromagnetic radiation with a wavelength lying between visible light and radio waves. Infrared have be widely used nowadays including data communications, night vision, object tracking and so on. People commonly use infrared in data communication, since it is easily generated and only suffers little from electromagnetic interference. Take the TV remote control as an example, which can be found in everyones home. The infrared remote control systems use infrared light-emitting diodes (LEDs) to send out an IR (infrared) signal when the button is pushed. A different pattern of pulses indicates the corresponding button being pushed. To allow the control of multiple appliances such as a TV, VCR, and cable box, without interference, systems generally have a preamble and an address to synchronize the receiver and identify the source and location of the infrared signal. To encode the data, systems generally vary the width of the pulses (pulse-width modulation) or the width of the spaces between the pulses (pulse space modulation). Another popular system, bi-phase encoding, uses signal transitions to convey information. Each pulse is actually a burst of IR at the carrier frequency. A high means a burst of IR energy at the carrier frequency and a low represents an absence of IR energy. There is no encoding standard. However, while a great many home entertainment devices use their own proprietary encoding schemes, some quasi-standards do exist. These include RC-5, RC-6, and REC-80. In addition, many manufacturers, such as NEC, have also established their own standards. 2 Wireless sensor network Wireless sensor network (WSN) is a wireless network which consists of a vast number of autonomous sensor nodes using sensors to monitor physical or environmental conditions, such as temperature, acoustics, vibration, pressure, motion or pollutants, at different locations. Each node in a sensor network is typically equipped with a wireless communications device, a small microcontroller, one or more sensors, and an energy source, usually a battery. The size of a single sensor node can be as large as a shoebox and can be as small as the size of a grain of dust, depending on different applications. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity requirement of the individual sensor nodes. The size and cost are constrained by sensor nodes, therefore, have result in corresponding limitations on available inputs such as energy, memory, computational speed and bandwidth. The development of wireless sensor networks (WSN) was originally motivated by military applications such as battlefield surveillance. Due to the advancement in micro-electronic mechanical system technology (MEMS), embedded microprocessors, and wireless networking, the WSN can be benefited in many civilian application areas, including habitat monitoring, healthcare applications, and home automation. 3 Types of Wireless Sensor Networks Wireless sensor network nodes are typically less complex than general-purpose operating systems both because of the special requirements of sensor network applications and the resource constraints in sensor network hardware platforms. The operating system does not need to include support for user interfaces. Furthermore, the resource constraints in terms of memory and memory mapping hardware support make mechanisms such as virtual memory either unnecessary or impossible to implement. TinyOS TinyOS is possibly the first operating system specifically designed for wireless sensor networks. Unlike most other operating systems, TinyOS is based on an event-driven programming model instead of multithreading. TinyOS programs are composed into event handlers and tasks with run to completion-semantics. When an external event occurs, such as an incoming data packet or a sensor reading, TinyOS calls the appropriate event handler to handle the event. The TinyOS system and programs are both written in a special programming language called nesC nesC which is an extension to the C programming language. NesC is designed to detect race conditions between tasks and event handlers. There are also operating systems that allow programming in C. Examples of such operating systems include Contiki Contiki, and MANTIS. Contiki is designed to support loading modules over the network and supports run-time loading of standard ELF files. The Contiki kernel is event-driven, like TinyOS, but the system supports multithreading on a per-application basis. Unlike the event-driven Contiki kernel, the MANTIS kernel is based on preemptive multithreading. With preemptive multithreading, applications do not need to explicitly yield the microprocessor to other processes. 4 Introduction to Wireless Sensor Node A sensor node, also known as a mote, is a node in a wireless sensor network that is capable of performing processing, gathering sensory information and communicating with other connected nodes in the network. Sensor node should be in small size, consuming extremely low energy, autonomous and operate unattended, and adaptive to the environment. As wireless sensor nodes are micro-electronic sensor device, they can only be equipped with a limited power source. The main components of a sensor node include sensors, microcontroller, transceiver, and power source. Sensors are hardware devices that can produce measurable response to a change in a physical condition such as light density and sound density. The continuous analog signal collected by the sensors is digitized by Analog-to-Digital converter. The digitized signal is then passed to controllers for further processing. Most of the theoretical work on WSNs considers Passive and Omni directional sensors. Passive and Omni directional sensors sense the data without actually manipulating the environment with active probing, while no notion of “direction” involved in these measurements. Commonly people deploy sensor for detecting heat (e.g. thermal sensor), light (e.g. infrared sensor), ultra sound (e.g. ultrasonic sensor), or electromagnetism (e.g. magnetic sensor). In practice, a sensor node can equip with more than one sensor. Microcontroller performs tasks, processes data and controls the operations of other components in the sensor node. The sensor node is responsible for the signal processing upon the detection of the physical events as needed or on demand. It handles the interruption from the transceiver. In addition, it deals with the internal behavior, such as application-specific computation. The function of both transmitter and receiver are combined into a single device know as transceivers that are used in sensor nodes. Transceivers allow a sensor node to exchange information between the neighboring sensors and the sink node (a central receiver). The operational states of a transceiver are Transmit, Receive, Idle and Sleep. Power is stored either in the batteries or the capacitors. Batteries are the main source of power supply for the sensor nodes. Two types of batteries used are chargeable and non-rechargeable. They are also classified according to electrochemical material used for electrode such as NiCd(nickel-cadmium), NiZn(nickel-zinc), Nimh(nickel metal hydride), and Lithium-Ion. Current sensors are developed which are able to renew their energy from solar to vibration energy. Two major power saving policies used are Dynamic Power Management (DPM) and Dynamic Voltage Scaling (DVS). DPM takes care of shutting down parts of sensor node which are not currently used or active. DVS scheme varies the power levels depending on the non-deterministic workload. By varying the voltage along with the frequency, it is possible to obtain quadratic reduction in power consumption. 5 Challenges The major challenges in the design and implementation of the wireless sensor network are mainly the energy limitation, hardware limitation and the area of coverage. Energy is the scarcest resource of WSN nodes, and it determines the lifetime of WSNs. WSNs are meant to be deployed in large numbers in various environments, including remote and hostile regions, with ad-hoc communications as key. For this reason, algorithms and protocols need to be lifetime maximization, robustness and fault tolerance and self-configuration. The challenge in hardware is to produce low cost and tiny sensor nodes. With respect to these objectives, current sensor nodes usually have limited computational capability and memory space. Consequently, the application software and algorithms in WSN should be well-optimized and condensed. In order to maximize the cover