无线传感器网络论文中英文资料对照外文翻译
红外传感器论文中英文资料对照外文翻译
中英文资料对照外文翻译外文资料Moving Object Counting with an Infrared Sensor NetworkBy KI, Chi KeungAbstractWireless 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 Node1.1 Introduction to InfraredInfrared 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 everyone's 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, somequasi-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.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.1.2 Wireless sensor networkWireless 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.1.3 Types of Wireless Sensor NetworksWireless 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 incomingdata 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.1.4 Introduction to Wireless Sensor NodeA 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 environmen t 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 renewtheir energy from solar to vibration energy. Two major power saving policies used are Dynamic Power Management (DPM) and Dynamic V oltage 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.1.5 ChallengesThe 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 coverage area with a high stability and robustness of each signal node, multi-hop communication with low power consumption is preferred. Furthermore, to deal with the large network size, the designed protocol for a large scale WSN must be distributed.1.6 Research IssuesResearchers are interested in various areas of wireless sensor network, which include the design, implementation, and operation. These include hardware, software and middleware, which means primitives between the software and the hardware. As the WSNs are generally deployed in the resources-constrained environments with battery operated node, the researchers are mainly focus on the issues of energy optimization, coverage areas improvement, errors reduction, sensor network application, data security, sensor node mobility, and data packet routing algorithm among the sensors. In literature, a large group of researchers devoted a great amount of effort in the WSN. They focused in various areas, including physical property, sensor training, security through intelligent node cooperation, medium access, sensor coverage with random and deterministic placement, object locating and tracking, sensor location determination, addressing, energy efficient broadcasting and active scheduling, energy conserved routing, connectivity, data dissemination and gathering, sensor centric quality of routing, topology control and maintenance, etc.中文译文移动目标点数与红外传感器网络作者KI, Chi Keung摘要无线传感器网络(WSN)已成为最近的一个研究热点。
无线传感器网络测距技术外文翻译文献
无线传感器网络测距技术外文翻译文献(文档含中英文对照即英文原文和中文翻译)原文：RANGING TECHNIQUES FOR WIRELESS SENSOR NETWORKSThe RF location sensors operating in different environments can measure the RSS, AOA, phase of arrival (POA), TOA, and signature of the delay - power profile as location metrics to estimate the ranging distance [4,7] . The deployment environment (i.e., wireless RF channel) will constrain the accuracy and the performance of each technique. In outdoor open areas, these ranging techniques perform very well. However, as the wireless medium becomes more complex, for example, dense urban or indoor environments, the channel suffers from severe multipath propagation and heavy shadow fading conditions. This finding in turn impacts the accuracy and performance in estimating the range between a pair of nodes. For this reason, this chapter will focus its ranging and localization discussion on indoor environments. This is important because many of the WSN applications are envisioned for deployment in rough terrain and cluttered environments and understanding of the impact of the channel on the performance of ranging and localization is important. In addition, range measurements using POA and AOA in indoor and urban areas are unreliable. Therefore, we will focus our discussion on two practical techniques,TOA and RSS.These two ranging techniques, which have been used traditionally in wirelessnetworks, have a great potential for use in WSN localization.The TOA based ranging is suitable for accurate indoor localization because it only needs a few references and no prior training. By using this technique, however, the hardware is complex and the accuracy is sensitive to the multipath condition and the system bandwidth. This technique has been implemented in GPS, PinPoint, WearNet, IEEE 802.15.3, and IEEE 802.15.4 systems. The RSS based ranging, on the other hand, is simple to implement and is insensitive to the multipath condition and the bandwidth of the system. In addition, it does not need any synchronization and can work with any existing wireless system that can measure the RSS. For accurate ranging, however, a high density of anchors or reference points is needed and extensive training and computationally expensive algorithms are required.The RSS ranging has been used for WiFi positioning in systems, for example, Ekahau, Newbury Networks, PanGo, and Skyhook.This section first introduces TOA based ranging and the limitations imposed by the wireless channel. Then it will be compared with the RSS counterpart focusing on the performance as a function of the channel behavior. What is introduced here is important to the understanding of the underlying issues in distance estimation, which is an important fundamental building block in WSN localization.TOA Based RangingIn TOA based ranging, a sensor node measures the distance to another node by estimating the signal propagation delay in free space, where radio signals travel at the constant speed of light. Figure 8.3 shows an example of TOA based ranging between two sensors. The performance of TOA based ranging depends on the availability of the direct path (DP) signal [4,14] . In its presence, for example, short distance line - of - sight (LOS) conditions, accurate estimates are feasible [14] . The challenge, however, is ranging in non - LOS (NLOS) conditions, which can be characterized as site - specific and dense multipath environments [14,22] . These environments introduce several challenges. The first corrupts the TOA estimatesdue to the multipath components (MPCs), which are delayed and attenuated replicas of the original signal, arriving and combining at the receiver shifting the estimate. The second is the propagation delay caused by the signal traveling through obstacles, which adds a positive bias to the TOA estimates. The third is the absence of the DP due to blockage, also known as undetected direct path (UDP) [14] . The bias imposed by this type of error is usually much larger than the first two and has a significant probability of occurrence due to cabinets, elevator shafts, or doors that are usually cluttering the indoor environment.In order to analyze the behavior of the TOA based ranging, it is best to resort to a popular model used to describe the wireless channel. In a typical indoor environment, the transmitted signal will be scattered and the receiver node will receive replicas of the original signal with different amplitudes, phases, and delays. At the receiver, the signals from all these paths combine and this phenomenon is known as multipath. In order to understand the impact of the channel on the TOA accuracy, we resort to a model typically used to characterize multipath arrivals. For multipath channels, the impulse respons 错误！未找到引用源。
无线红外传感器网络中英文对照外文翻译文献
中英文资料外文翻译文献外文资料AbstractWireless 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 ofapplications, 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 Node1.1 Introduction to InfraredInfrared radiation is a part of the electromagnetic radiation with a wavelength lying between visible light and radio waves. Infrared have be widely used nowadaysincluding 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 everyone's 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.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 ofapplications, 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.1.2 Wireless sensor networkWireless sensor network (WSN) is a wireless network which consists of a vast number of autonomous sensor nodes using sensors tomonitor 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.1.3 Types of Wireless Sensor NetworksWireless sensor network nodes are typically less complex than general-purpose operating systems both because of the specialrequirements 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 preemptivemultithreading. With preemptive multithreading, applications do not need to explicitly yield the microprocessor to other processes.1.4 Introduction to Wireless Sensor NodeA 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. magneticsensor). 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 areDynamic Power Management (DPM) and Dynamic V oltage Scaling (DVS). DPM takes care of shutting down parts of sensor node which arenot 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.1.5 ChallengesThe 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 coverage area with a high stability and robustness of each signal node, multi-hop communication with low power consumption is preferred. Furthermore, to deal with the large network size, the designed protocol for a large scale WSN must be distributed.1.6 Research IssuesResearchers are interested in various areas of wireless sensor network, which include the design, implementation, and operation. These include hardware, software and middleware, which means primitives between the software and the hardware. As the WSNs are generally deployed in the resources-constrained environments with battery operated node, the researchers are mainly focus on the issues of energy optimization, coverage areas improvement, errors reduction, sensor network application, data security, sensor node mobility, and data packet routing algorithm among the sensors. In literature, a large group of researchers devoted a great amount of effort in the WSN. They focused in various areas, including physical property, sensor training, security through intelligent node cooperation, medium access, sensor coverage with random and deterministic placement, object locating and tracking, sensor location determination, addressing, energy efficient broadcasting and active scheduling, energy conserved routing, connectivity, data dissemination and gathering, sensor centric quality of routing, topology control and maintenance, etc.中文译文移动目标点数与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。
传感器技术论文中英文对照资料外文翻译文献
传感器技术论文中英文对照资料外文翻译文献Development of New Sensor TechnologiesSensors are devices that can convert physical。
chemical。
logical quantities。
etc。
into electrical signals。
The output signals can take different forms。
such as voltage。
current。
frequency。
pulse。
etc。
and can meet the requirements of n n。
processing。
recording。
display。
and control。
They are indispensable components in automatic n systems and automatic control systems。
If computers are compared to brains。
then sensors are like the five senses。
Sensors can correctly sense the measured quantity and convert it into a corresponding output。
playing a decisive role in the quality of the system。
The higher the degree of n。
the higher the requirements for sensors。
In today's n age。
the n industry includes three parts: sensing technology。
n technology。
and computer technology。
车载无线传感器网络监测系统设计(外文原文+中文翻译)
Wireless sensor network monitoring system designKang yi-mei,Zhao lei,Hu jiang,Yang en-bo(Study on Beijing University of Aeronautics and Astronautics)Summary: A car wireless sensor network monitoring system based on IEEE 802.15.4 and ZigBee standards. With universal wireless sensor networks, expansion of the scope of monitoring and monitoring functions for in-car system, car data acquisition and condition monitoring of equipment status and the necessary equipment control, topology control, topology query functions. Keywords: wireless sensor networks; monitoring systemIntroductionIn order to satisfy the people to car safety, handling and comfort requirements, vehicle integrated with more and more electronic system .At present, car electronic equipment is widely used 16 or 32-bit microprocessor control. Creating in-vehicle monitoring system based on IEEE 802.15.4 and ZigBee standard for wireless sensor networks, designed to achieve a more optimized wireless sensor networks, the progressive realization of the network of automotive systems, intelligent and controllable to provide high-Car System security.System designIn this paper, the existing vehicle system, the data transmission mode is extended to the wireless transmission mode, the realization of a star network data acquisition system. And can place each data acquisition node of the acquired data is transmitted to the gateway, the gateway through the serial port to upload data to the host computer, in the host data real-time waveform display, and method of database to preserve, for the follow-up data processing. The application of system object is composed of a temperature sensor, pressure sensor, speed sensor, speed sensor, a current sensor, pressure sensor, sensor subsystem. The purpose of this design is to use a monitoring host machine end to the detection of multiple target environment, taking into account the access data throughput and software system complexity, using time-division multiplexing way, one by one on the net terminal collecting point of control and data acquisition.As shown in Figure 1, the system is divided into 3 parts: Vehicle Monitoring Center, gateway and mobile sensor node. Gateway is the whole vehicle system core, and all vehicular sensor node communication. Vehicle monitoring center to the gateway sends a control command by the gateway, the control command is converted to an RF signal and sent to the vehicle sensor node. When the vehicle sensor nodes to transmit data, gateway into the data reception state, and upload data to the monitoring center for further processing. In addition, car between sensor nodes cannot communicate with each other. The monitoring center of the monitoring software and gateway in RS232standard interface for communication.Vehicle sensor node life cycle is active and dormant periods. Nodes in the active phase of the completion of data acquisition, data sent to the gateway, receiving andexecuting gateway command; in the dormant period off the wireless RF module in order to save energy, until the next active period. System through this mechanism of dormancy to reduce energy consumption, extend the time span of the system as a whole.The system used PC as the control center, PC machine monitoring software in VB development environment, is a dialog based application software. In order to improve the communication module of the intelligent level, in the design, its function is not limited to the real-time data display, all of the data collection by the monitoring software by sending a request signal to the trigger. Considering the original data for subsequent processing and in-depth analysis of the vehicle system, can accurately judge, software has also added data preservation of the document and data file display function.Generally speaking, the whole network are controlled by the host monitoring software, the working process of every node of the network is the need of human participation.2 hardware system design2.1application chip introductionMC13192with IEEE802.15.4 standard, the operating frequency is2.405～2.480 GHz, data transmission rate of 250kbps, using 0-QPSK debugging mode. This feature-rich two-way 2.4GHz transceiver with a data modem which can be in the ZigBee technology application. It also has an optimized digital core, helps to reduce the MCU processing power, shorten the cycle of execution.The main control MCU choose HCS08series of low power, high performance microprocessor MC9S08GB60. The processor has a 60Application of KB programmable Flash、4 KB RAM,10 ADC,8 channel2 asynchronous serial communication interface ( SCI ),1 synchronous serial interface ( SPI ) and I2C bus module, can fully meet the requirement of vehicle gateway and node processor requirements.2.2 MCl3192and MC9S08GB60hardware connectionMC13192and MC9S08GB60 hardware connection diagram as shown in figure 2. The MC13192control and data transmission on 4 wire serial peripheral interface ( SPI ) is completed, the4interface signals were MOS-I, MISO,, SPICLK. The main control MCU through the control signal exiting sleep mode or hibernation mode, through to reset the transceiver, through the RXTXEN to control the data sending and receiving, or force the transceiver into idle mode. The sensor output analog signal through MCU 8 Channel10 bit ADC conversion input to MCU. MCU via SPI MC13192to read and write operation, and the sensor to collect the signal processed by MC13192launch out. The MC13192 interrupt IRQ interrupt register through the pins and to judge the type of interrupt. MC908GB60 pin to control the MC13192 into a different mode of operation .Control of the sensor signal from the MC13192receiving antenna in, transmitted via SPI to MCU, after MCU judgment after processingthrough the GPIO port is transmitted to the sensor, complete control of the sensor. At the same time, MCU MC13192transceiver control and the MAC layer operation. The 3system software design3.1of overall software designThe software design is the design of the core, the key lies in the overall framework of software and data structure design. An important factor to consider is a efficiency, another is to design the clarity.System software consists of the gateway node and the sensor node is composed of two parts, the two parts are needed to complete the SMAC protocol transplantation, and according to the different needs for the upper communication applications with API interface function. Because the SMAC protocol stack programming model using hierarchical design, only the underlying PHY and MAC program level and related hardware, and network layer and application layer procedures is not affected by hardware effects. SMAC in different hardware platform transplantation only need to modify the PHY and MAC layer, each layer can shield the hardware differences directly run.As shown in Figure 3, the design of the software for system platform layer, protocol layer and application layer 3layer. At the same time, defines 3API interface: system layer interface, protocol layer and application layer interface. System level interface defines a hardware register mapping, so C language to be able to directly access the hardware registers to control hardware. System platform based on real-time operating system μC/II protocol layer, to provide system services Hardware driving module provides the hardware driver, all of the hardware control through the module to provide services. Platform layer protocol layer interface protocol layer to provide services. Protocol layer is based on the IEEE 802.15.4 physical layer and link layer based on the ZigBee network layer protocol. Application layer through the application layer interface to invoke services provided by the protocol layer, network management and data transfer tasks. Application of configuration module can call protocol layer to provide network services, will direct the system configuration and query, it is mainly through the AT commands to achieve, so the module calls the application layer interface and protocol layer interface to provide services.3.2sensor node software designBased on the long-term use of the functional requirements, sensor nodes in the software design is the key to achieve the required functions, and can minimize the energy consumption of the sensor nodes.It was found, ZigBee module and the energy consumption is much larger than the central processor and the energy consumption of sensor module. Therefore, the sensor node design of application software to try to make each module in a dormant state, and minimizing wakes ZigBee module number. Therefore, the sensor nodes, power of each functional module initialization is completed, and joined the network, enter the Sleep state, the central processor cycles to be timed wake-up to send data tothe gateway, and receives the gateway command. Sensor nodes of the workflow are shown in Figure 4.The 3.3 gateway node software designGateway downward management sensor node, to complete and PC monitoring center of interaction, the need for a complicated task management and scheduling, therefore, based on the uC / OS kernel of embedded operating system to manage the gateway, the application task efficiently provide good software support. According to gateway function demand, the μC / OS-II, SMAC protocol organic union, form a network operating environment, the user can conveniently on the basis of its development and application. Based on μC / OS-II extended gateway software platform structure is shown in figure 5. Based on μC / OS-II operating system, were used to build the system task SYS_task ( ), START_task ( SMAC star network task ), gateway and a sensor node interaction task COMM_task ( ), PC monitoring center port monitoring mission ( SER_task ) applications such as a series of tasks, thus realizing the gateway software application function.The 3.4 host monitoring software designThis system is the ultimate goal of the collected vehicle sensor data is transmitted in real-time to the host, and the host of display and preservation. Display is designed to get on-board sensor node monitoring environment of the initial situation, preservation is designed as an in-depth analysis of the data samples. In addition, the system as a whole the main prosecution and the data acquisition request initiator, need to be able to send the data request signal in accordance with the requirements of. According to the above requirements, VB environment in the development of a dialog based application. This application includes a 4 module:①data waveform display module. The role of the module is a form of waveform data of the node to be displayed in real-time, it is the use of MS Chart and Timer control.②topology display module. When the user wants to know the wireless sensor network topology construction situation, you can view the topological information, understanding of network nodes join and loss.The historical data display module. In vehicle network system to a certain period of the past, may need a certain period of time the original data for subsequent processing and in-depth analysis, so that the vehicle system of accurate judgement. With the aid of historical data display module, the control center from the gateway of the data obtained, according to the different attributes of the nodes, address and time are saved to the database of the corresponding field, and may be will displayed by waveform of historical data, for the user analysis.The controlling module :In vehicle during system operation may be concerned about a vehicle sensor value node, or to a sensor threshold settings, for monitoring environmental exceptions can be promptly reported to the system. These are available through the control module of the system are corresponding to the set, the control module can also be on the system in which one does not need to delete the node.In short, through the host monitoring software users can visually and many aspects ofgeneral wireless sensor network systems to understand and use.4 test and verification4.1 testingTesting equipment:4 MCl3192ZigBee chip node,1as a gateway node, the remaining 3as sensor nodes.Test method: the gateway node power,4 LED and light, scanning channel if the search to the idle channel, the LED goes out and join the free channel for. The sensor node power,4 LED scanning in the channel at the same time, polling light. LED1 flashes once when the sensor nodes receive the allocation address of the gateway node, So far, networking process and address binding process is complete.4.2 Zigbee RF communication testTesting equipment: ZigBee node 4, a computer terminal stationTest method: according to the ZigBee transmission frame format, the actual transmission total bytes for ( n 6), namely ( n 6) bytes for a data packet. According to the set parameters of the software, such as packet loss is the loss number plus 1. If the received data packet, receives the data packet number plus 1, and then sends the data were compared with data, if the data is correct, the number of packets plus 1, and error packets number plus 1. The last statistic results, can know the data packet loss and packet error rate. The 4 node to form a ZigBee network,1 of them as the gateway, the remaining 3 nodes for sensor node. Write a program to set:3nodes and gateway communications, computer terminal and the gateway is connected through RS232, terminal equipment software records from the 3node to receive data, nodes work at 2.4 GHz frequencies, transmission of a byte of data, circular send 100 times. To obtain the final3 node test average as a result of the data analysis. Star network radio frequency communication BER test results as shown in table 1.Experimental analysis of: in a star network for data transmission, the test results significantly worse on a single point to single point transmission mode. This is mainly because, in the transmission process node must exist between the frequency interference and other interference.4.3power testSystem status and hibernation, respectively, using a multimeter to test the gateway node and the power consumption of sensor nodes, the test results listed in Table 2.ConclusionThis paper analyzes the IEEE 802.15.4 and ZigBee protocol, combined with the general development principles of communication systems and embedded systems, IEEE802.15.4 protocol on the μC / OS-II operating system, select the appropriate hardware and software platform, focusing on software support for the platform, the software design of the overall structure of the communication protocol stack, andultimately to achieve a compliant with the ZigBee specification car star wireless data acquisition network. The system has the following advantages:①system easy to install. Wireless interconnection makes the equipment installation location is flexible to meet the requirements of the automation system is installed. It is simply that the power can take equipment. The network system can automatically complete the network configuration.②scalability. Equipment within the coverage of the vehicle gateway, turn on the device, the node will automatically join the network.③network self-healing ability. If the network is a device fails, the vehicle gateway can automatically monitor, issue the command the device reset and re-network.车载无线传感器网络监测系统设计康一梅，赵磊，胡江，杨恩博（就读于北京航天航空大学）摘要：基于IEEE 802．15．4和ZigBee标准实现了一个车载无线传感器网络监测系统。
Zigbee无线传感器网络英文文献与翻译
Zigbee无线传感器网络英文文献与翻译DFig.3 Zigbee network modelII. THE GREENHOUSE ENVIRONMENTAL MONITORINGSYSTEM DESIGNTraditional agriculture only use machinery and equipment which isolating and no communicating ability. And farmers have to monitor crops’ growth by themselves. Even if some people use electrical devices, but most of them were restricted to simple communication between control computer and end devices like sensors instead of wire connection, which couldn’t be strictly defined as wireless se nsor network. Therefore, by through using sensor networks and, agriculture could become more automation, more networking and smarter.In this project, we should deploy five kinds of sensors in the greenhouse basement. By through these deployed sensors, the parameters such as temperature in the greenhouse, soil temperature, dew point, humidity and light intensity can be detected real time. It is key to collect different parameters from all kinds of sensors. And in the greenhouse, monitoring the vegetables growing conditions is the top issue. Therefore, longer battery life and lower data rate and less complexity are very important. From the introduction about above, we know that meet the requirements for reliability, security, low costs and low power.A. System OverviewThe overview of Greenhouse environmental monitoring system, which is made up by one sink node (coordinator), many sensor nodes, workstation and database. Mote node and sensor node together composed of each collecting node. When sensors collect parameters real time, such as temperature in the greenhouse, soil temperature, dew point, humidity and light intensity, these data will be offered to A/D converter, then by through quantizing and encoding become the digital signal that is able to transmit by wireless sensor communicating node. Each wireless sensor communicating node has ability of transmitting, receiving function.In this WSN, sensor nodes deployed in the greenhouse, which can collect real time data and transmit data to sink node (Coordinator) by the way of multi-hop. Sink node complete the task of data analysis and data storage. Meanwhile, sink node is connectedwith GPRS/CDMA can provide remote control and data download service. In the monitoring and controlling room, by running greenhouse management software, the sink node can periodically receives the data from the wireless sensor nodes and displays them on monitors.B. Node Hardware DesignSensor nodes are the basic units of WSN. The hardware platform is made up sensor nodes closely related to the specific application requirements. Therefore, the most important work is the nodes design which can perfect implement the function of detecting and transmission as a WSN node, and perform its technology characteristics. Fig.4 shows the universal structure of the WSN nodes. Power module provides the necessary energy for the sensor nodes. Data collection module is used to receive and convert signals of sensors. Data processing and control module’s functions are node device control, task scheduling, and energy computing and so on. Communication module is used to send data between nodes and frequency chosen and so on.Fig.4 Universal structure of the wsn nodesIn the data transfer unit, the module is embedded to match the MAC layer and the NET layer of the protocol. We choose CC2430 as the protocol chips, which integrated the CPU, RF transceiver, net protocol and the RAM together. CC2430 uses an 8bit MCU (8051), and has 128KB programmable flash memory and 8KB RAM. It also includes A/D converter, some Timers, AES128Coprocessor, Watchdog Timer, 32K crystal Sleep mode Timer, Power on Reset, Brown out Detection and 21 I/Os. Based on the chips, many modules for the protocol are provided. And the transfer unit could be easily designed based on the modules.As an example of a sensor end device integrated temperature, humidity and light,the design is shown in Fig. 5.Fig.5 The hardware design of a sensor node The SHT11 is a single chip relative humidity and temperature multi sensor module comprising a calibrated digital output. It can test the soil temperature and humidity. The DS18B20 is a digital temperature sensor, which has 3 pins and data pin can link MSP430 directly. It can detect temperature in greenhouse. The TCS320 is a digital light sensor. SHT11, DS18B20 and TCS320 are both digital sensors with small size and low power consumption. Other sensor nodes can be obtained by changing the sensors.The sensor nodes are powered from onboard batteries and the coordinator also allows to be powered from an external power supply determined by a jumper.C. Node Software DesignThe application system consists of a coordinator and several end devices. The general structure of the code in each is the same, with an initialization followed by a main loop.The software flow of coordinator, upon the coordinator being started, the first action of the application is the initialization of the hardware, liquid crystal, stack and application variables and opening the interrupt. Then a network will be formatted. If this net has been formatted successfully, some network information, such as physical address, net ID, channel number will be shown on the LCD. Then program will step into application layer and monitor signal. If there is end device or router want to join in this net, LCD will shown this information, and show the physical address of applying node, and the coordinator will allocate a net address to this node. If the node has beenjoined in this network, the data transmitted by this node will be received by coordinator and shown in the LCD.The software flow of a sensor node, as each sensor node is switched on, it scans all channels and, after seeing any beacons, checks that the coordinator is the one that it is looking for. It then performs a synchronization and association. Once association is complete, the sensor node enters a regular loop of reading its sensors and putting out a frame containing the sensor data. If sending successfully, end device will step into idle state; by contrast, it will collect data once again and send to coordinator until sending successfully.D. Greenhouse Monitoring Software DesignWe use VB language to build an interface for the test and this greenhouse sensor network software can be installed and launched on any Windows-based operating system. It has 4dialog box selections: setting controlling conditions, setting Timer, setting relevant parameters and showing current status. By setting some parameters, it can perform the functions of communicating with port, data collection and data viewing。
无线传感器网络英文摘要与翻译
AbstractA1（1）In the recent years, as the rapid development of MEMS, wireless communication network, embedded system, and the interaction of all kinds of new technologies, many new modes of information obtaining and process come into being. A2（2）Wireless sensor network (WSN) is one of them. A2（3）WSN can be used to monitor the environments, the machines and even the people; hence “ubiquitous computing” will come true. A2（4）WSN has wide application fields, so it has been paid high attention by the military, the academes, and the industrial from all over the world. A2（5）Meanwhile, this provides many challenges in the academe foundations and technologies.A3（6）This dissertation introduces the recent researches on WSN, and analyzes its key technologies：the setup of wireless communication network, the design and implementation of network nodes and the design steps of WSN, in an architecture view.A4（7）By analyzing and comparing, ZigBee technology is adopted to setup wireless communication network. A4（8）The topology of the network and hierarchical protocol stacks are designed. A4（9）The embedded network nodes are designed and developed, and the hardware and software are implemented. A4（10）An experimental WSN is deployed and the experimental data is collected and analyzed. A5（11）Finally, a typical example of wireless sensor network, personnelidentification and positioning system in mine, is presented. Keywords: Wireless sensor network, Embedded systems, IEEE802.15.4 protocols, ZigBee摘要近年来，随着微机电系统(MEMS)、无线通信网络和嵌入式系统等技术的飞速发展，各种新技术的融合，出现了许多信息获取和处理的新模式，无线传感器网络就是其中一例。
无线传感中英文对照外文翻译文献
(文档含英文原文和中文翻译)中英文对照翻译译文:无线传感器网络的实现及在农业上的应用1引言无线传感器网络（Wireless Sensor Network ,WSN）就是由部署在监测区域内大量的廉价微型传感器节点组成，通过无线通信方式形成的一个多跳的自组织的网络系统。
其目的是协作地感知、采集和处理网络覆盖区域中感知对象的信息，并发送给观察者。
“传感器、感知对象和观察者”构成了网络的三个要素。
这里说的传感器，并不是传统意义上的单纯的对物理信号进行感知并转化为数字信号的传感器，它是将传感器模块、数据处理模块和无线通信模块集成在一块很小的物理单元，即传感器节点上，功能比传统的传感器增强了许多，不仅能够对环境信息进行感知，而且具有数据处理及无线通信的功能。
借助传感器节点中内置的形式多样的传感器件，可以测量所在环境中的热、红外、声纳、雷达和地震波信号等信号，从而探测包括温度、湿度、噪声、光强度、压力、土壤成分、移动物体的大小、速度和方向等等众多我们感兴趣的物质现象。
无线传感器网络是一种全新的信息获取和信息处理模式。
由于我国水资源已处于相当紧缺的程度,加上全国90%的废、污水未经处理或处理未达标就直接排放的水污染,11%的河流水质低于农田供水标准。
水是农业的命脉,是生态环境的控制性要素,同时又是战略性的经济资源,因此采用水泵抽取地下水灌溉农田,实现水资源合理利用,发展节水供水,改善生态环境,是我国目前精确农业的关键，因此采用节水和节能的灌水方法是当今世界供水技术发展的总趋势。
2无线传感器网络概述2.1无线传感器网络的系统架构无线传感器网络的系统架构如图1所示，通常包括传感器节点、汇聚节点和管理节点。
传感器节点密布于观测区域，以自组织的方式构成网络。
传感器节点对所采集信息进行处理后，以多跳中继方式将信息传输到汇聚节点。
然后经由互联网或移动通信网络等途径到达管理节点。
终端用户可以通过管理节点对无线传感器网络进行管理和配置、发布监测任务或收集回传数据。
无线微传感器中英文对照外文翻译文献
无线微传感器中英文对照外文翻译文献(文档含英文原文和中文翻译)A Simple Energy Model for Wireless Microsensor TransceiversAbstract—This paper describes the modelling of shortrange transceivers for microsensor applications. A simple energy model is derived and used to analyze the transceiver battery life. This model takes into account energy dissipation during the start-up, receive, and transmit modes. It shows that there is a significant fixed cost in the transceiver energy consumption and this fixed cost can be driven down by increasing the data rate of the transceiver.I. IntroductionWireless microsensor networks can provide short-range connectivity with significant fault tolerances. These systems find usage in diverse areas such as environmental monitoring, industrial process automation, and field surveillance. As an example, Table I shows a detailed specification for a sensor system used in a factory machine monitoring environment.The major characteristics of a microsensor system are high sensor density, short range transmissions, and low data rate. Depending on the application, there can also be stringent BER and latency requirements. Due to the large density and the random distributed nature of these networks, battery replacement is a difficult task. In fact,a primary issue that prevents these networks to be used in many application areas is the short battery life. Therefore, maximizing the battery life time of the sensor nodes is important. Figure 1 shows the peak current consumption limit when a 950mAh battery is used as the energy source. As seen in the figure, battery life can vary by orders of magnitude depending on the duty cycle of each operation. To allow for higher maximum peak current, it is desirable to have the sensor remain in the off-state for as long as possible.However, the latency requirement of the system dictates how often the sensor needs to be active. For the industrial sensor application described above, the sensor needs to operate every 5ms to satisfy the latency requirement.Assuming that the sensor operates for 100µs every 5ms, the duty cycle is 2%. To achieve a one-year battery life, the peak current consumption must be kept under 5.4mA, which translates to approximately 10mW at 2V supply.This is a difficult target to achieve for sensors that communicate at giga-Hertz carrier frequencies.There has been active research in microsensor networks over the past years. Gupta [1] and Grossglauser [2] established information theoretic bounds on the capacity of ad-hoc networks. Chang [3] and Heinzelman [4] suggested algorithms to increase overall network life-time by spreading work loads evenly among all sensors. Much of the work in this area, especially those that deal with energy consumption of sensor networks, require an energy model [5]. This paper develops a realistic energy model based on the power consumption of a state of the art Bluetoothtransceiver [6]. This model provides insights into how to minimize the power consumption of sensor networks and can be easily incorporated into work that studies energy limited wireless sensor networks. The outline of this paper is as follows. Section II derives the transceiver model. Section III applies this model to analyzing the battery life time of the Bluetooth transceiver.Section IV investigates the dependencies in the model and shows how to modify the design of the Bluetooth transceiver to improve the battery life. Section V shows the battery life improvement realized by applying the results in Section IV. Section VI summarizes the paper.II. Microsensor Transceiver ModellingThis section derives a simple energy model for low power microsensors. Figure 2 shows the model of the sensor node.It includes a sensor/DSP unit for data processing, D/A and A/D for digital-to-analog and analog-to-digital conversion, and a wireless transceiver for data communication. The sensor/DSP, D/A, and A/D operate at low frequency and consume less than 1mW. This is over an order of magnitude less than the power consumption of the transceiver. Therefore, the energy model ignores the contributions from these components. The transceiver has three modes of operation: start-up, receive, and transmit. Each mode will be described and modelled.A. Start-up ModeWhen the transceiver is first turned on, it takes some time for the frequency synthesizer and the VCO to lock to the carrier frequency. The start-up energy can be modelled as follows:where P LO is the power consumption of the synthesizer and the VCO. The term t start is the required settling time. RF building blocks including PA, LNA, and mixer have negligible start-up time and therefore can remain in the off-state during the start-up mode.B. Receive ModeThe active components of the receiver includes the low noise amplifier (LNA), mixer, frequency synthesizer, VCO, intermediate-frequency (IF) amplifier (amp), and demodulator (Demod). The receiver energy consumption can be modelled as follows:where P RX includes the power consumption of the LNA,mixer, IF amplifier, and demodulator. The receiver power consumption is dictated by the carrier frequency and the noise and linearity requirements. Once these parameters are determined, to the first order the power consumption can be approximated as a constant, for data rates up to 10’s of Mb/s. In other words, the power consumption is dominated by the RF building blocks that operate at the carrier frequency. The IF demodulator power varies with data rate, but it can be made small by choosing a low IF.C. Transmit ModeThe transmitter includes the modulator (Mod), frequency synthesizer and VCO (shared with the receiver), and power amplifier (PA). The data modulates the VCO and produces a FSK signal at the desired data rate and carrier frequency. A simple transmitter energy model is shown in Equation (3). The modulator consumes very little energy and therefore can be neglected.P LO can be approximated as a constant. P PA depends on additional factors and needs to be modelled more carefully as follows:where η is the PA efficiency, r is the data rate, d is the transmission distance, and n is the path loss exponent. γPA is a factor that depends on E b /N O , noise factor F of the receiver, link margin L mar , wavelength of the carrier frequency λ, and th e transmit/receive antenna gains G T ,G R :From Equations (3) and (4), the transmitter power consumption can be written as a constant term plus a variable term. The energy model thus becomesIII. Bluetooth TransceiverHere we demonstrate how the above model can be used to calculate the battery life time of a Bluetooth transceiver [6]. This is one of the lowest power Bluetooth transceivers reported in literature. The energy consumption of the transceiver depends on how it operates. Assuming a 100-bit packet is received and a 100-bit packet is transmitted every 5ms, Figure 3 showsthe transceiver activity within one cycle of operation.The transceiver takes 120µs to start up. Operating at 1Mb/s, the receiver takes 100µs to receive the packet. The transceiver then switches to the transmit mode and transmits a same-length packet at the same rate. A 10µs interval, t switch , between the receive and the transmit mode is allowed to switch channel or to absorb any transient behavior. Therefore, the energy dissipated in one cycle of operation is simplyBoth the average power consumption and the duty cycle can be found From Figure 3. Knowing that the transceiver operates at 2V, the life time for a 950mAh battery is calculated to be approximately 2-months.IV. Energy OptimizationThe microsensor system described in Section I requires a battery life of one year or better. Although the Bluetooth transceiver described in the last section falls short of this requirement, it serves as a starting point for making improvements. This section examines E op in detail and suggests ways to increase the battery life by considering both circuit and system improvements. A.Start-up EnergyThe start-up energy can be a significant part of the total energy consumption, especially when the transceiver is used to send short packets in burst mode. For the Bluetooth transceiver, E start accounts for 20% of E op .The start-up energy becomes negligible if the following condition is held true:For the receive/transmit scheme shown in Figure 3, the right hand-side of Equation (8)is evaluated to be approximately 450µs. To keep E start an order of magnitude below E op , it is desirable to have a start-up time of less than 45µs. Cho has demonstrated a 5.8GHz frequency synthesizer im- plementation with a start-up time under 20µs [7].B. Power AmplifierThe PA power consumption is given bywhere η is the power efficiency and P out is the RF output power. P out can be determined by link-budget analysis. For a Bluetooth transceiver, the required P out is 1mW [8].This enables a maximum transmission distance of 10 meters, which is adequate for microsensor applications. Note that P out is small as compared to P LO . The Bluetooth transceiver discussed in Section II has a maximum RF output power of 1.6mW and a PA power consumption of 10mW, sothe efficiency is at 16%. At frequencies around 2GHz, the PA efficiency can vary from 10% [9] to 70% [10] depending on linearity, circuit topology, and technology. Since FSK signal has a constant envelope, nonlinea r PA’s can be used so that better efficiency can be achieved. As will be shown in the next section, PA efficiency has a significant impact on the battery life.C. Data RateAssuming a packet of length L pkt is transmitted at dat rate r, then the transmit time isThe transmitter energy consumption can be re-written asEquation (12) shows that the contribution of the fixed cost P LO can be reduced by increasing the data rate. The energy per bit, E bit , is defined as E op divided by the total number of bits received and sent during one cycle of operation. Assuming a packet of length L pkt is received and a packet of the same length is transmitted, E bit can be found by dividing Equation (7) by 2L pkt . Substituting the appropriate expressions for E start , E rx , and E tx and re-arranging the terms, we getThe first term in Equation (13) is the start-up energy cost. The second term is the PA energy cost. The third term is the cost of the rest of the transceiver electronics during the transmit and receive modes. Note that this term is divided by the data rate r. Figure 4 shows E bit as a function of data rate. The two solid curves have start-up time 120µs and PA efficiencies 10% and 70%, respectively. The two dotted curves have start-up time 20µs and efficiencies 10% and 70%, respectively. At low data rate, E bit is dominated by the fixed cost (the 3rd term in Equation (13)). At high data rate, the start-up energy and the PA energy dominates, so in order to increase battery life, good circuit design techniques need to be applied to minimize the start-up time and to maximize the PA efficiency.Figure 5 shows the impact of PA efficiency on the battery life at a data rate of 10Mb/s. At t start = 120µs, the startup energy is so large that the battery life is limited to 7month even if the PA reaches 100% efficiency. At t start =20µs, the battery life is much improved. The PA efficiency needs to be higher than about 30% to have a 1-year or better battery life. This is certainly achievable as discussed previously in the PA section.V. Performance ImprovementThere are three apparent results from the previous section. First, the data rate should be increased to reduce the fixed cost. Second, the start-up time should be minimized. Third, PA efficiency should be maximized. Figure 6 shows the transceiver activity for a transceiver that has 20µs start-up time and 10Mb/s data rate. The power consumption of the electronics are kept the same as in the Bluetooth transceiver except for the PA. The maximum RF output power is set at 10mW to accommodate the higher data rate, and the PA efficiency is assumed to be 50%. The switching time is kept at 10µs, although this is a conservative since the switching time is likely to be shorter for a faster frequency synthesizer. The E op of this transceiver is 8x lower than that of theBluetooth transceiver. The battery life-time extends from 2-months to approximately1.3 years.VI. ConclusionThis paper describes the modelling of short-range transceivers for wireless sensor applications. This model takes into account energy dissipation during the start-up, transmit, and receive modes. This model is first used to analyze the battery life of a state of the art Bluetooth transceiver, and then it is used to optimize E op . This paper shows that the battery life can be improved significantly by increasing the data rate, reducing the start-up time, and improving the PA efficiency. Increasing the data rate drives down the fixed energy cost of the transceiver. Reducing the start-up time decreases the start-up energy overhead. Improving the PA efficiency lowers the energy per bit cost of the PA.一个简单的能量无线微传感器的接收机模型摘要—本文描述了微传感器的近程的收发器的造型的应用程序。
传感器技术外文文献及中文翻译
传感器技术外文文献及中文翻译Sensor technologyA sensor is a device which produces a signal in response to its detecting or measuring a property ,such as position , force , torque , pressure , temperature , humidity , speed , acceleration , or vibration .Traditionally ,sensors (such as actuators and switches )have been used to set limits on the performance of machines .Common examples are (a) stops on machine tools to restrict work table movements ,(b) pressure and temperature gages with automatics shut-off features , and (c) governors on engines to prevent excessive speed of operation . Sensor technology has become an important aspect of manufacturing processes and systems .It is essential for proper data acquisition and for the monitoring , communication , and computer control of machines and systems .Because they convert one quantity to another , sensors often are referred to as transducers .Analog sensors produce a signal , such as voltage ,which is proportional to the measured quantity .Digital sensors have numeric or digital outputs that can be transferred to computers directly .Analog-to-coverter(ADC) is available for interfacing analog sensors with computers .Classifications of SensorsSensors that are of interest in manufacturing may be classified generally as follows:Machanical sensors measure such as quantities aspositions ,shape ,velocity ,force ,torque , pressure , vibration , strain , andmass .Electrical sensors measure voltage , current , charge , and conductivity .Magnetic sensors measure magnetic field ,flux , and permeablity .Thermal sensors measure temperature , flux ,conductivity , and special heat .Other types are acoustic , ultrasonic , chemical , optical , radiation ,laser ,and fiber-optic .Depending on its application , a sensor may consist of metallic , nonmetallic , organic , or inorganic materials , as well as fluids ,gases ,plasmas , or semiconductors .Using the special characteristics of these materials , sensors covert the quantity or property measured to analog or digital output. The operation of an ordinary mercury thermometer , for example , is based on the difference between the thermal expansion of mercury and that of glass.Similarly , a machine part , a physical obstruction , or barrier in a space can be detected by breaking the beam of light when sensed by a photoelectric cell . A proximity sensor ( which senses and measures the distance between it and an object or a moving member of a machine ) can be based on acoustics , magnetism , capacitance , or optics . Other actuators contact the object and take appropriate action ( usually by electromechanical means ) . Sensors are essential to the conduct of intelligent robots , and are being developed with capabilities that resemble those of humans ( smart sensors , see the following ).This is America, the development of such a surgery Lin Bai an example,through the screen, through a remote control operator to control another manipulator, through the realization of the right abdominal surgery A few years ago our country the exhibition, the United States has been successful in achieving the right to the heart valve surgery and bypass surgery. This robot has in the area, caused a great sensation, but also, AESOP's surgical robot, In fact, it through some equipment to some of the lesions inspections, through a manipulator can be achieved on some parts of the operation Also including remotely operated manipulator, and many doctors are able to participate in the robot under surgery Robot doctor to include doctors with pliers, tweezers or a knife to replace the nurses, while lighting automatically to the doctor's movements linked, the doctor hands off, lighting went off, This is very good, a doctor's assistant.Tactile sensing is the continuous of variable contact forces , commonly by an array of sensors . Such a system is capable of performing within an arbitrary three-dimensional space .has gradually shifted from manufacturing tonon-manufacturing and service industries, we are talking about the car manufacturer belonging to the manufacturing industry, However, the services sector including cleaning, refueling, rescue, rescue, relief, etc. These belong to the non-manufacturing industries and service industries, so here is compared with the industrial robot, it is a very important difference. It is primarily a mobile platform, it can move to sports, there are some arms operate, also installed some as a force sensor and visual sensors, ultrasonic ranging sensors, etc. It’s surrounding environment for the conduct of identification, to determine its campaign to complete some work, this is service robot’s one of the basic characteristicsIn visual sensing (machine vision , computer vision ) , cameral optically sense the presence and shape of the object . A microprocessor then processes the image ( usually in less than one second ) , the image is measured , and the measurements are digitized ( image recognition ) .Machine vision is suitable particularly for inaccessible parts , in hostile manufacturing environments , for measuring a large number of small features , and in situations where physics contact with the part may cause damage .Small sensors have the capability to perform a logic function , to conduct two-way communication , and to make a decisions and take appropriate actions . The necessary input and the knowledge required to make a decision can be built into a smart sensor . For example , a computer chip with sensors can be programmed to turn a machine tool off when a cutting tool fails . Likewise , a smart sensor can stop a mobile robot or a robot arm from accidentally coming in contact with an object or people by using quantities such as distance , heat , and noise .Sensor fusion . Sensor fusion basically involves the integration of multiple sensors in such a manner where the individual data from each of the sensors ( such as force , vibration , temperature , and dimensions ) are combined to provide a higher level of information and reliability . A common application ofsensor fusion occurs when someone drinks a cup of hot coffee . Although we take such a quotidian event for granted ,it readily can be seen that this process involves data input from the person's eyes , lips , tongue , and hands .Through our basic senses of sight , hearing , smell , taste , and touch , there is real-time monitoring of relative movements , positions , and temperatures . Thus if the coffee is too hot , the hand movement of the cup toward the lip is controlled and adjusted accordingly .The earliest applications of sensor fusion were in robot movement control , missile flight tracking , and similar military applications . Primarily because these activities involve movements that mimic human behavior . Another example of sensor fusion is a machine operation in which a set of different but integrated sensors monitors (a) the dimensions and surface finish of workpiece , (b) tool forces , vibrations ,and wear ,(c) the temperature in various regions of the tool-workpiece system , and (d) the spindle power .An important aspect in sensor fusion is sensor validation : the failure of one particular sensor is detected so that the control system maintains high reliability . For this application ,the receiving of redundant data from different sensors is essential . It can be seen that the receiving , integrating of all data from various sensors can be a complex problem .With advances in sensor size , quality , and technology and continued developments in computer-control systems , artificial neural networks , sensor fusion has become practical and available at low cost .Movement is relatively independent of the number of components, the equivalent of our body, waist is a rotary degree of freedom We have to be able to hold his arm, Arm can be bent, then this three degrees of freedom, Meanwhile there is a wrist posture adjustment to the use of the three autonomy, the general robot has six degrees of freedom. We will be able to space the three locations, three postures, the robot fully achieved, and of course we have less than six degrees of freedom Fiber-optic sensors are being developed for gas-turbine engines . These sensors will be installed in critical locations and will monitor the conditions inside the engine , such as temperature , pressure , and flow of gas . Continuous monitoring of the signals from thes sensors will help detect possible engine problems and also provide the necessary data for improving the efficiency of the engines .传感器技术传感器一种通过检测某一参数而产生信号的装置。
电子信息工程无线传感器中英文对照外文翻译文献
中英文对照外文翻译文献(文档含英文原文和中文翻译)基于最长寿命的无线传感器网络连续查询处理摘要监测应用成为无线传感器网络（WSNS）最重要的应用之一。
这类应用通常具有长期运行的复杂查询处理技术且通过传感器流对此处理技术进行评估。
基于无线传感器网络中传感器的能量有限，高效节能查询的评价对于延长系统使用寿命来说是至关重要的—使用期限指的是此网络查询从开始到停止所执行其预定任务的最早时间。
我们通过使用表达式树对复杂查询进行建模。
我们考虑使无线传感器网络的使用期限最大化以达成表达式树T的持续网络内评估，因此可在基站获得其根值。
网络内评估意味着对于算符T的评估可能会推至网络节点且同样意味着对T 进行重复评估（每轮一次）。
持续的网络内T评估需要解决以下问题的两个方面：（1）相对于网络节点的T的运算符，变量和变量的放置（2）以上量值对于适当网络节点的路径选择，网络节点需要使用以上量值评估运算符。
我们对其复杂性进行了分析，并且为T节点在WSN传感器节点上的放置提供了一种简单而有效的算法。
我们所提出的运算符放置算法试图使总传输数据量最小化。
T的放置可引起一定的最大使用期限并行流（MLCF）问题。
我们提供的算法可以找到解决MLCF问题的近优积分方案，其中一种便是收集路径，一定数量的积分流被路由。
我们对于T的持续网络内评估包括以上放置和路由算法。
实验证明，我们的做法能够一贯地、有效地找到对于无线传感网络表达式树的持续网络内评估的最大使用期限解决方案。
2010 Elsevier B.V. All rights reserved.1.介绍远程监控是无线传感器网络最具有吸引力的应用之一。
像环境监测和建筑监测，它们通常会在兴趣点处通过传感器不断的运行查询数据流。
例如有一种查询应用，可以在火山监测中每五分钟报告当前活动的情况，这是由于传感器的加工和相关表面振动，气压和温度，气体密度的变化，磁场变异等因素所产生的数据流测量，如何让这些因素运用在这些查询中并得到长时间高效地成功处理和操作的无线传感器网络运行是部署的一个重要的问题，有些问题不可行，是由于经常补充传感器电池的能量成本过高。
无线传感器网络模型设计-英文文献翻译.doc
Model Design of Wireless Sensor Network based on Scale-Free Network TheoryABSTRACTThe key issue of researches on wireless sensor networks is to balance the energy costs across the whole network and to enhance the robustness in order to extend the survival time of the whole sensor network. As a special complex network limited especially by the environment, sensor network is much different from the traditional complex networks, such as Internet network, ecological network, social network and etc. It is necessary to introduce a way of how to study wireless sensor network by complex network theory and analysis methods, the key of which lies in a successful modeling which is able to make complex network theory and analysis methods more suitable for the application of wireless sensor network in order to achieve the optimization of some certain network characteristics of wireless sensor network. Based on generation rules of traditional scale-free networks, this paper added several restrictions to the improved model. The simulation result shows that improvements made in this paper have made the entire network have a better robustness to the random failure and the energy costs are more balanced and reasonable. This improved model which is based on the complex network theory proves more applicable to the research of wireless sensor network.Key-words: Wireless sensor network; Complex network; Scale-free networkI. INTRODUCTIONIn recent years, wireless sensor networks have attracted more and more related researchers for its advantages. Sensor nodes are usually low-power and non-rechargeable. The integrity of the original networks will be destroyed and other nodes will have more business burden for data transmission if the energy of some certain nodes deplete. The key issue of sensor network research is to balance the energy consumption of all sensor nodes and to minimize the impact of random failure of sensor nodes or random attacks to sensor nodes on the entire network [1].Complex network theory has been for some time since first proposed by Barabasi and Albert in 1998, but complex network theory and analysis method applied to wireless sensor networks research is seriously rare and develops in slow progress. As a special complex network limited especially by the environment, sensor network is much different from the traditional complex network, and the existing complex network theory and analysis methods can not be directly applied to analyze sensor networks. Based on scale-free network theory (BA model) [2], (1) this paper added a random damage mechanism to each sensor node when deployed in the generation rule; (2) considering the real statement of wireless sensor networks, a minimum and maxinum restriction on sensor communication radius was added to each sensor node; (3) in order to maintain a balanced energy comsuption of the entire network, this paper added a limited degree of saturation value to each sensor node. This improved scale-free model not only has the mentioned improvements above, but also has lots of advantages of traditional scale-free networks, such as the good ability to resist random attacks, so that the existing theory and analysis methods of complex network will be more suitable for the researches of wireless sensor network.II. PROGRESS OF RELATED RESEARCHHailin Zhu and Hong Luo have proposed two complex networks-based models for wireless sensor networks [3], the first of which named Energy-aware evolution model (EAEM) can organize the networks in an energy-efficient way, and can produce scale-free networks which can improve the networks reliance against random failure of the sensor nodes. In the second model named Energy-balanced evolution model (EBEM), the maximum number of links for each node is introduced into the algorithm, which can make energy consumption more balanced than the previous model (EAEM).CHEN Lijun and MAO Yingchi have proposed a topology control of wireless sensor networks under an average degree constraint [4]. In the precondition of the topology connectivity of wireless sensor networks, how to solve the sparseness of the network topology is a very important problem in a large number of sensor nodes deployed randomly. They proved their proposed scheme can decrease working nodes, guarantee network topology sparseness, predigest routing complexity and prolong network survival period.LEI Ming and LI Deshi have proposed a research on self-organization reliability of wireless sensor network[5], which aiming on the two situations: deficiency of WSN nodes and under external attack, analyzes the error tolerance ability of different topologies of WSN, and eventually obtains optimized self—organized topological models of WSN and proposes a refined routing algorithm based on WSN.III. IMPROVED SCALE-FREE MODEL FOR WSNBecause of the limited energy and the evil application environment, wireless sensor networks may easily collapse when some certain sensor nodes are of energy depletion or destruction by the nature, and even some sensor nodes have been damaged when deployed. There is also a restriction on maxinum and mininum communication radius of sensor nodes rather than the other known scale-free networks such as Internet network, which has no restriction on communication radius. To have a balanced energy consumption, it is necessary to set up a saturation value limited degree of each sensor node [6].In response to these points, based on the traditional scale-free model, this paper has made the following improvements in the process of model establishment:(1) A large number of researches have shown that many complex networks in nature are not only the result from internal forces, but also the result from external forces which should not be ignored to form an entire complex network. Node failure may not only occour by node energy depletion or random attacks to them when sensor networks are in the working progress, but also occour by external forces, such as by the nature, when deployed. In this paper, a mechanism of small probability of random damage has been added to the formation of sensor networks.(2) Unlike Internet network where two nodes are able to connect directly to each other and their connection are never limited by their real location, sensor network, two nodes in which connect to each other by the way of multi-hop, so that each node has a maximum of length restriction on their communication radius. To ensure the sparse of the whole network, there must also be a minimum of length restriction on their communication radius. In this paper, a length restriction on communication radius of sensor nodes has been proposed in the improved model.(3) In sensor network, if there exists a sensor node with a seriously high degree, whose energy consumption is very quickly, it will be seriously bad. The whole sensor network would surely collapse if enough energy were not supported to the certain node. To avoid this situation, this paper has set up a saturation value limited degree of each sensor node. By adding the mentioned restrictions above to the formation of the scale-free model, the new improved model will be more in line with the real statement of sensor network. Complex network theory and analysis methods will be more appropriate when used to research and analyze the sensor network.IV . DESCRIPTION OF THE IMPROVED ALGORITHMThe specific algorithm of the improved model formation are described as follows : (1) A given region (assumed to be square) is divided into HS*HSbig squares (named as BS);(2) Each BS (assumed to be square) is divided into LS*LS small squares (named as SS), and each SS can have only one node in its coverage region;(3) m0 backbone nodes are initially generated as a random graph, and then a new node will be added to the network to connect the existing m nodes with m edges at each time interval. (m< m0, mis a quantity parameter);(4) The newly generated node v, has a certain probability of Peto be damaged directly so that it will never be connected with any existing nodes;(5) The newly generated node vconnects with the existing node i, which obeyes dependent-preference rule and is surely limited by the degree of the certain saturation value .(6) The distance div between the newly generated node v connects and the existing node i shall be shorter than the maximum dmax of the communication radius of sensor nodes.Above all, the probability that the existing node i will be connected with the newly generated node v can be shown as follows:In order to compute it conveniently, here assumed that few nodes had reached the degree of saturation value kimax . That is, N is very minimal in Eqs.(1) so that it can be ignored here. And in Eqs.iNj 1ak Kjπ=≈∑ 0N=m 1t +- (2)With The varying rate with time of ki, we get:m 112i i ii t jj k amk amk m t mt mk δπδ+-====-∑(3)When t→∞,condition: k i (t i )=m, we get the solution: i2,i t k t aββ=（t ）=m （）(4) The probability that the degree of node I is smaller than k is:11{k (t)k}P{t }i i m tP kββ<=>(5)The time interval when each newly generated node connected into the network is equal, so that probability density of t i is a constant parameter:01(t )i P m t=+1/β we replace it into Eqs. (5), then we get:11111{k (t)k}P{t }1(t )i m t k i i it m tP P k ββββ=<=>=-∑(6)1101(t m )m tk ββ-+ So we get: 110(k (t)k)21(k).i P m t P k m t k ββδδ<==+ (7)When t →∞, we get:2(k)2m r P k -=(8)In which 12=1+=1+aγβ, and the degree distribution we get and the degree distribution of traditional scale-free network are similar. Approximately, it has nothing to do with the timeparameter t and the quantity of edges m generated at each time interval.max P{d d }iv ≤could be calculated by the max in um restriction dmax on communicationradius of each sensor node and the area of the entire coverage region S, that ismax P{d d }iv ≤=2Sd π Then we replace max P{d d }iv ≤=2S d π and a=max P{d d }iv ≤(1-P )e intoEqs. and eventually we get: 22S21122(k)2m 2e aP km kπ----==（1-P ）d .V . SIMULATIONThis paper used Java GUI mode of BRITE topology generator to generate the topology, and parameter settings were as follows:1) N=5000N means the quantity of the sensor nodes at the end of thetopology generation.2) m=m0 =1M means the quantity of the new generated edges by the new generated node at each time interval.3) HS=500HS means the given region was divided into HS*HS big squares.4) .LS=50 LS means each big square was divided into LS*LS small squares.d=105) mind is the mininum restriction on communication radius of each sensor node.mind=1286) maxd is the maxinum restriction on communication radius of each sensor node.max7) PC=1PC means wether preferential connectivity or not.8) .IG=1IG means wether incremental grouth or not.9) e P=0.01, m=1This means that any newly generated node has 1% chance to be node failure and the newly generated node if normal only connect with one existing node .Then we got each degree of the sensor network nodes from BRITE topology generator. To analyze the degree distribution, we use Matlab to calculate datas and draw graph. As can easily be seen from Fig. 1, the distribution of degree k subjected approximately toPower-Law distribution. However, the value of γ is no longer between 2 and 3, but a very large value, which is caused by the random damage probability P e to new generated nodes when deployed and the max in um of communication radius d max of each sensor node. It can be easily seen that the slope of P(k) is very steep and P(k) rears up because sensor node has a limited degree of saturation value by 180. The existence of 0 degree nodes is result from the random damage to new generated nodes when deployed.Fig. 1 Degree distribution of Improved ModelCompared with the degree distribution produced by traditional scale-free network as is shown in Fig. 2, the generation rule proposed in this paper has produced a degree distribution in a relatively low value as is shown in Fig. 1; there are some nodes of 0 degree as is shown in Fig. 1 on the left for the random damage rule; as is shown on the right in Fig. 1, there are no nodes with higher degree than the quantity of 180 while there are some nodes whose degree are of higher degree than the quantity of 180.Fig. 2 Degree distribution of traditional Scale-free ModelVI. CONCLUSIONThis paper has added a random damage to new generated nodes when deployed; considering multi-hop transmission of sensor network, this paper has proposed a maximum restriction on the communication radius of each sensor node; in order to improve the efficiency of energy comsumption and maintain the sparsity of the entire network, this paper has also added a minimum restriction on the communication radius of each sensor node to the improved model; to balance the energy comsuption of the entire network, this paper has proposed a limited degree of saturation value on each sensor node.In this paper, an improved scale-free network model was proposed to introduce the theory of traditional scale-free network and analysis methods into the researches of wireless sensor networks more appropriately, which would be more approximate to the real statement of wireless sensor networks.REFERENCES[1] R. Albert, H. Jeong and A.-L. Barabasi. Error and attack tolerance of complex networks. Nature, 2000; 406: 378-382.[2] Albert R, Barabasi A. Statistical mechanics of complex networks. Rev Mod Phys 2002; 74: 47–97..[3] Zhu HL, Luo H. Complex networks-based energy-efficient evolution model for wireless sensor networks. Chaos, Solitons and Fractals; 2008: 1-4.[4] Chen LJ, Mao YC. Topology Control of Wireless Sensor Networks Under an Average Degree Constraint. Chinese Journal of computers 2007; 30: 1-4.[5] Lei M, Li DS. Research on Self-Organization Reliability of Wireless Sensor Network . Complex system and complexity science ; 2005, 2: 1-4.[6] Chen LJ, Chen DX. Evolution of wireless sensor network . WCNC 2007; 556: 3003–7.[7] Peng J, Li Z. An Improved Evolution Model of Scale-Free Network . Computer application. 2008 , 2; 1: 1-4.基于无范围网络理论的无线传感器网络模型设计张戌源通信工程部通信与信息工程学院上海，中国摘要无线传感器网络的研究的关键问题是是平衡整个网络中的能源成本并且为了延长整个传感器网络的生存时间要增强鲁棒性。
通信专业英语之无线传感器网络
Notes 4
4.The advantage of WSNs over conventional loggers is the "live" data feed that is possible.
译文：无线传感器网络的优点是使“活”的数据传送成 为可能。
Advantage:有利条件，优势，常与介词over连用，指“ 与……相比的长处”，
Translation of Texts
A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature sound, pressure, etc. ,and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor network was motivated by military applications such as battlefield surveillance; today as such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on.
最新传感器技术外文文献及中文翻译资料
Sensor technologyA sensor is a device which produces a signal in response to its detecting or measuring a property ,such as position , force , torque , pressure , temperature , humidity , speed , acceleration , or vibration .Traditionally ,sensors (such as actuators and switches )have been used to set limits on the performance of machines .Common examples are (a) stops on machine tools to restrict work table movements ,(b) pressure and temperature gages with automatics shut-off features , and (c) governors on engines to prevent excessive speed of operation . Sensor technology has become an important aspect of manufacturing processes and systems .It is essential for proper data acquisition and for the monitoring , communication , and computer control of machines and systems .Because they convert one quantity to another , sensors often are referred to as transducers .Analog sensors produce a signal , such as voltage ,which is proportional to the measured quantity .Digital sensors have numeric or digital outputs that can be transferred to computers directly .Analog-to-coverter(ADC) is available for interfacing analog sensors with computers .Classifications of SensorsSensors that are of interest in manufacturing may be classified generally as follows:Machanical sensors measure such as quantities aspositions ,shape ,velocity ,force ,torque , pressure , vibration , strain , and mass .Electrical sensors measure voltage , current , charge , and conductivity .Magnetic sensors measure magnetic field ,flux , and permeablity .Thermal sensors measure temperature , flux ,conductivity , and special heat .Other types are acoustic , ultrasonic , chemical , optical , radiation , laser ,and fiber-optic .Depending on its application , a sensor may consist of metallic , nonmetallic , organic , or inorganic materials , as well as fluids ,gases ,plasmas , or semiconductors .Using the special characteristics of these materials , sensors covert the quantity or property measured to analog or digital output. The operation of an ordinary mercury thermometer , for example , is based on the difference between the thermal expansion of mercury and that of glass.Similarly , a machine part , a physical obstruction , or barrier in a space can be detected by breaking the beam of light when sensed by a photoelectric cell . A proximity sensor ( which senses and measures the distance between it and an object or a moving member of a machine ) can be based on acoustics , magnetism , capacitance , or optics . Other actuators contact the object and take appropriate action ( usually by electromechanical means ) . Sensors are essential to the conduct of intelligent robots , and are being developed with capabilities that resemble those of humans ( smart sensors , see the following ).This is America, the development of such a surgery Lin Bai an example, through the screen, through a remote control operator to control another manipulator, through the realization of the right abdominal surgery A few years ago our country theexhibition, the United States has been successful in achieving the right to the heart valve surgery and bypass surgery. This robot has in the area, caused a great sensation, but also, AESOP's surgical robot, In fact, it through some equipment to some of the lesions inspections, through a manipulator can be achieved on some parts of the operation Also including remotely operated manipulator, and many doctors are able to participate in the robot under surgery Robot doctor to include doctors with pliers, tweezers or a knife to replace the nurses, while lighting automatically to the doctor's movements linked, the doctor hands off, lighting went off, This is very good, a doctor's assistant.Tactile sensing is the continuous of variable contact forces , commonly by an array of sensors . Such a system is capable of performing within an arbitrarythree-dimensional space .has gradually shifted from manufacturing tonon-manufacturing and service industries, we are talking about the car manufacturer belonging to the manufacturing industry, However, the services sector including cleaning, refueling, rescue, rescue, relief, etc. These belong to the non-manufacturing industries and service industries, so here is compared with the industrial robot, it is a very important difference. It is primarily a mobile platform, it can move to sports, there are some arms operate, also installed some as a force sensor and visual sensors, ultrasonic ranging sensors, etc. It’s surrounding environment for the conduct of identification, to determine its campaign to complete some work, this is service robot’s one of the basic characteristicsIn visual sensing (machine vision , computer vision ) , cameral optically sense the presence and shape of the object . A microprocessor then processes the image ( usually in less than one second ) , the image is measured , and the measurements are digitized ( image recognition ) .Machine vision is suitable particularly for inaccessible parts , in hostile manufacturing environments , for measuring a large number of small features , and in situations where physics contact with the part may cause damage .Small sensors have the capability to perform a logic function , to conducttwo-way communication , and to make a decisions and take appropriate actions . The necessary input and the knowledge required to make a decision can be built into a smart sensor . For example , a computer chip with sensors can be programmed to turn a machine tool off when a cutting tool fails . Likewise , a smart sensor can stop a mobile robot or a robot arm from accidentally coming in contact with an object or people by using quantities such as distance , heat , and noise .Sensor fusion . Sensor fusion basically involves the integration of multiple sensors in such a manner where the individual data from each of the sensors ( such as force , vibration , temperature , and dimensions ) are combined to provide a higher level of information and reliability . A common application of sensor fusion occurs when someone drinks a cup of hot coffee . Although we take such a quotidian event for granted ,it readily can be seen that this process involves data input from the person's eyes , lips , tongue , and hands .Through our basic senses of sight , hearing , smell , taste , and touch , there is real-time monitoring of relative movements , positions , and temperatures . Thus if the coffee is too hot , the hand movement of the cup toward the lip is controlled and adjusted accordingly .The earliest applications of sensor fusion were in robot movement control , missile flight tracking , and similar military applications . Primarily because these activities involve movements that mimic human behavior . Another example of sensor fusion is a machine operation in which a set of different but integrated sensors monitors (a) the dimensions and surface finish of workpiece , (b) tool forces , vibrations ,and wear ,(c) the temperature in various regions of the tool-workpiece system , and (d) the spindle power .An important aspect in sensor fusion is sensor validation : the failure of one particular sensor is detected so that the control system maintains high reliability . For this application ,the receiving of redundant data from different sensors is essential . It can be seen that the receiving , integrating of all data from various sensors can be a complex problem .With advances in sensor size , quality , and technology and continued developments in computer-control systems , artificial neural networks , sensor fusion has become practical and available at low cost .Movement is relatively independent of the number of components, the equivalent of our body, waist is a rotary degree of freedom We have to be able to hold his arm, Arm can be bent, then this three degrees of freedom, Meanwhile there is a wrist posture adjustment to the use of the three autonomy, the general robot has six degrees of freedom. We will be able to space the three locations, three postures, the robot fully achieved, and of course we have less than six degrees of freedomFiber-optic sensors are being developed for gas-turbine engines . These sensors will be installed in critical locations and will monitor the conditions inside the engine , such as temperature , pressure , and flow of gas . Continuous monitoring of the signals from thes sensors will help detect possible engine problems and also provide the necessary data for improving the efficiency of the engines .传感器技术传感器一种通过检测某一参数而产生信号的装置。
传感器的基础知识论文中英文资料对照外文翻译
中英文资料对照外文翻译Basic knowledge of transducersA transducer is a device which converts the quantity being measured into an optical, mechanical, or-more commonly-electrical signal. The energy-conversion process that takes place is referred to as transduction.Transducers are classified according to the transduction principle involved and the form of the measured. Thus a resistance transducer for measuring displacement is classified as a resistance displacement transducer. Other classification examples are pressure bellows, force diaphragm, pressure flapper-nozzle, and so on.1、Transducer ElementsAlthough there are exception ,most transducers consist of a sensing element and a conversion or control element. For example, diaphragms,bellows,strain tubes and rings, bourdon tubes, and cantilevers are sensing elements which respond to changes in pressure or force and convert these physical quantities into a displacement. This displacement may then be used to change an electrical parameter such as voltage, resistance, capacitance, or inductance. Such combination of mechanical and electrical elements form electromechanical transducing devices or transducers. Similar combination can be made for other energy input such as thermal. Photo, magnetic and chemical,giving thermoelectric, photoelectric,electromaanetic, and electrochemical transducers respectively.2、Transducer SensitivityThe relationship between the measured and the transducer output signal is usually obtained by calibration tests and is referred to as the transducer sensitivity K1= output-signal increment / measured increment . In practice, the transducer sensitivity is usually known, and, by measuring the output signal, the input quantity is determined from input= output-signal increment / K1.3、Characteristics of an Ideal TransducerThe high transducer should exhibit the following characteristicsa) high fidelity-the transducer output waveform shape be a faithful reproduction of the measured; there should be minimum distortion.b) There should be minimum interference with the quantity being measured; the presence of the transducer should not alter the measured in any way.c) Size. The transducer must be capable of being placed exactly where it is needed.d) There should be a linear relationship between the measured and the transducer signal.e) The transducer should have minimum sensitivity to external effects, pressure transducers,for example,are often subjected to external effects such vibration and temperature.f) The natural frequency of the transducer should be well separated from the frequency and harmonics of the measurand.4、Electrical TransducersElectrical transducers exhibit many of the ideal characteristics. In addition they offer high sensitivity as well as promoting the possible of remote indication or mesdurement. Electrical transducers can be divided into two distinct groups:a) variable-control-parameter types,which include:i)resistanceii) capacitanceiii) inductanceiv) mutual-inductance typesThese transducers all rely on external excitation voltage for their operation.b) self-generating types,which includei) electromagneticii)thermoelectriciii)photoemissiveiv)piezo-electric typesThese all themselves produce an output voltage in response to the measurand input and their effects are reversible. For example, a piezo-electric transducer normally produces an output voltage in response to the deformation of a crystalline material; however, if an alternating voltage is applied across the material, the transducer exhibits the reversible effect by deforming or vibrating at the frequency of the alternating voltage.5、Resistance TransducersResistance transducers may be divided into two groups, as follows:i) Those which experience a large resistance change, measured by using potential-divider methods. Potentiometers are in this group.ii)Those which experience a small resistance change, measured by bridge-circuit methods. Examples of this group include strain gauges and resistance thermometers.5.1 PotentiometersA linear wire-wound potentiometer consists of a number of turns resistance wire wound around a non-conducting former, together with a wiping contact which travels over the barwires. The construction principles are shown in figure which indicate that the wiperdisplacement can be rotary, translational, or a combination of both to give a helical-type motion. The excitation voltage may be either a.c. or d.c. and the output voltage is proportional to the input motion, provided the measuring device has a resistance which is much greater than the potentiometer resistance.Such potentiometers suffer from the linked problem of resolution and electrical noise. Resolution is defined as the smallest detectable change in input and is dependent on thecross-sectional area of the windings and the area of the sliding contact. The output voltage is thus a serials of steps as the contact moves from one wire to next.Electrical noise may be generated by variation in contact resistance, by mechanical wear due to contact friction, and by contact vibration transmitted from the sensing element. In addition, the motion being measured may experience significant mechanical loading by the inertia and friction of the moving parts of the potentiometer. The wear on the contacting surface limits the life of a potentiometer to a finite number of full strokes or rotations usually referred to in the manufacture’s specification as the ‘number of cycles of life expectancy’, a typical value being 20*1000000 cycles.The output voltage V0 of the unload potentiometer circuit is determined as follows. Let resistance R1= xi/xt *Rt where xi = input displacement, xt= maximum possible displacement, Rt total resistance of the potentiometer. Then output voltage V0= V*R1/(R1+( Rt-R1))=V*R1/Rt=V*xi/xt*Rt/Rt=V*xi/xt. This shows that there is a straight-line relationship between output voltage and input displacement for the unloaded potentiometer.It would seen that high sensitivity could be achieved simply by increasing the excitation voltage V. however, the maximum value of V is determined by the maximum power dissipation P of the fine wires of the potentiometer winding and is given by V=(PRt)1/2 .5.2 Resistance Strain GaugesResistance strain gauges are transducers which exhibit a change in electrical resistance in response to mechanical strain. They may be of the bonded or unbonded variety .a) bonded strain gaugesUsing an adhesive, these gauges are bonded, or cemented, directly on to the surface of the body or structure which is being examined.Examples of bonded gauges arei) fine wire gauges cemented to paper backingii) photo-etched grids of conducting foil on an epoxy-resin backingiii)a single semiconductor filament mounted on an epoxy-resin backing with copper or nickel leads.Resistance gauges can be made up as single elements to measuring strain in one direction only,or a combination of elements such as rosettes will permit simultaneous measurements in more than one direction.b) unbonded strain gaugesA typical unbonded-strain-gauge arrangement shows fine resistance wires stretched around supports in such a way that the deflection of the cantilever spring system changes the tension in the wires and thus alters the resistance of wire. Such an arrangement may be found in commercially available force, load, or pressure transducers.5.3 Resistance Temperature TransducersThe materials for these can be divided into two main groups:a) metals such as platinum, copper, tungsten, and nickel which exhibit and increase in resistance as the temperature rises; they have a positive temperature coefficient of resistance.b) semiconductors, such as thermistors which use oxides of manganese, cobalt, chromium, or nickel. These exhibit large non-linear resistance changes with temperature variation and normally have a negative temperature coefficient of resistance.a) metal resistance temperature transducersThese depend, for many practical purpose and within a narrow temperature range, upon the relationship R1=R0*[1+a*(b1-b2)] where a coefficient of resistance in ℃-1,and R0 resistance in ohms at the reference temperature b0=0℃ at the reference temperature range ℃.The international practical temperature scale is based on the platinum resistance thermometer, which covers the temperature range -259.35℃ to 630.5℃.b) thermistor resistance temperature transducersThermistors are temperature-sensitive resistors which exhibit large non-liner resistance changes with temperature variation. In general, they have a negative temperature coefficient. For small temperature increments the variation in resistance is reasonably linear; but, if large temperature changes are experienced, special linearizing techniques are used in the measuring circuits to produce a linear relationship of resistance against temperature.Thermistors are normally made in the form of semiconductor discs enclosed in glass vitreous enamel. Since they can be made as small as 1mm,quite rapid response times are possible.5.4 Photoconductive CellsThe photoconductive cell , uses a light-sensitive semiconductor material. The resistance between the metal electrodes decrease as the intensity of the light striking the semiconductor increases. Common semiconductor materials used for photo-conductive cells are cadmium sulphide, lead sulphide, and copper-doped germanium.The useful range of frequencies is determined by material used. Cadmium sulphide is mainly suitable for visible light, whereas lead sulphide has its peak response in the infra-red regionand is, therefore , most suitable for flame-failure detection and temperature measurement. 5.5 Photoemissive CellsWhen light strikes the cathode of the photoemissive cell are given sufficient energy to arrive the cathode. The positive anode attracts these electrons, producing a current which flows through resistor R and resulting in an output voltage V.Photoelectrically generated voltage V=Ip.RlWhere Ip=photoelectric current(A),and photoelectric current Ip=Kt.BWhere Kt=sensitivity (A/im),and B=illumination input (lumen)Although the output voltage does give a good indication of the magnitude of illumination, the cells are more often used for counting or control purpose, where the light striking the cathode can be interrupted.6、Capacitive TransducersThe capacitance can thus made to vary by changing either the relative permittivity, the effective area, or the distance separating the plates. The characteristic curves indicate that variations of area and relative permittivity give a linear relationship only over a small range of spacings. Thus the sensitivity is high for small values of d. Unlike the potentionmeter, the variable-distance capacitive transducer has an infinite resolution making it most suitable for measuring small increments of displacement or quantities which may be changed to produce a displacement.7、Inductive TransducersThe inductance can thus be made to vary by changing the reluctance of the inductive circuit. Measuring techniques used with capacitive and inductive transducers:a)A.C. excited bridges using differential capacitors inductors.b)A.C. potentiometer circuits for dynamic measurements.c) D.C. circuits to give a voltage proportional to velocity for a capacitor.d) Frequency-modulation methods, where the change of C or L varies the frequency of an oscillation circuit.Important features of capacitive and inductive transducers are as follows:i)resolution infiniteii) accuracy+- 0.1% of full scale is quotediii)displacement ranges 25*10-6 m to 10-3miv) rise time less than 50us possibleTypical measurands are displacement, pressure, vibration, sound, and liquid level.8、Linear Variable-differential Ttransformer9、Piezo-electric Transducers10、Electromagnetic Transducers11、Thermoelectric Transducers12、Photoelectric Cells13、Mechanical Transducers and Sensing Elements传感器的基础知识传感器是一种把被测量转换为光的、机械的或者更平常的电信号的装置。
无线传感器网络论文中英文资料对照外文翻译
中英文资料对照外文翻译基于网络共享的无线传感网络设计摘要：无线传感器网络是近年来的一种新兴发展技术，它在环境监测、农业和公众健康等方面有着广泛的应用。
在发展中国家，无线传感器网络技术是一种常用的技术模型。
由于无线传感网络的在线监测和高效率的网络传送，使其具有很大的发展前景，然而无线传感网络的发展仍然面临着很大的挑战。
其主要挑战包括传感器的可携性、快速性。
我们首先讨论了传感器网络的可行性然后描述在解决各种技术性挑战时传感器应产生的便携性。
我们还讨论了关于孟加拉国和加利尼亚州基于无线传感网络的水质的开发和监测。
关键词：无线传感网络、在线监测1.简介无线传感器网络，是计算机设备和传感器之间的桥梁，在公共卫生、环境和农业等领域发挥着巨大的作用。
一个单一的设备应该有一个处理器，一个无线电和多个传感器。
当这些设备在一个领域部署时，传感装置测量这一领域的特殊环境。
然后将监测到的数据通过无线电进行传输，再由计算机进行数据分析。
这样，无线传感器网络可以对环境中各种变化进行详细的观察。
无线传感器网络是能够测量各种现象如在水中的污染物含量，水灌溉流量。
比如，最近发生的污染涌流进中国松花江，而松花江又是饮用水的主要来源。
通过测定水流量和速度，通过传感器对江水进行实时监测，就能够确定污染桶的数量和流动方向。
不幸的是，人们只是在资源相对丰富这个条件下做文章，无线传感器网络的潜力在很大程度上仍未开发，费用对无线传感器网络是几个主要障碍之一，阻止了其更广阔的发展前景。
许多无线传感器网络组件正在趋于便宜化（例如有关计算能力的组件），而传感器本身仍是最昂贵的。
正如在在文献[5]中所指出的，成功的技术依赖于共享技术的原因是个人设备的大量花费。
然而，大多数传感器网络研究是基于一个单一的拥有长期部署的用户，模式不利于分享。
该技术管理的复杂性是另一个障碍。
大多数传感器的应用，有利于这样的共享模型。
我们立足本声明认为传感器可能不需要在一个长时间单一位置的原因包括：（1）一些现象可能出现变化速度缓慢，因此小批量传感器可进行可移动部署，通过测量信号，充分捕捉物理现象（2）可能是过于密集，因此多余的传感器可被删除。
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中英文资料对照外文翻译基于网络共享的无线传感网络设计摘要：无线传感器网络是近年来的一种新兴发展技术，它在环境监测、农业和公众健康等方面有着广泛的应用。
在发展中国家，无线传感器网络技术是一种常用的技术模型。
由于无线传感网络的在线监测和高效率的网络传送，使其具有很大的发展前景，然而无线传感网络的发展仍然面临着很大的挑战。
其主要挑战包括传感器的可携性、快速性。
我们首先讨论了传感器网络的可行性然后描述在解决各种技术性挑战时传感器应产生的便携性。
我们还讨论了关于孟加拉国和加利尼亚州基于无线传感网络的水质的开发和监测。
关键词：无线传感网络、在线监测1.简介无线传感器网络，是计算机设备和传感器之间的桥梁，在公共卫生、环境和农业等领域发挥着巨大的作用。
一个单一的设备应该有一个处理器，一个无线电和多个传感器。
当这些设备在一个领域部署时，传感装置测量这一领域的特殊环境。
然后将监测到的数据通过无线电进行传输，再由计算机进行数据分析。
这样，无线传感器网络可以对环境中各种变化进行详细的观察。
无线传感器网络是能够测量各种现象如在水中的污染物含量，水灌溉流量。
比如，最近发生的污染涌流进中国松花江，而松花江又是饮用水的主要来源。
通过测定水流量和速度，通过传感器对江水进行实时监测，就能够确定污染桶的数量和流动方向。
不幸的是，人们只是在资源相对丰富这个条件下做文章，无线传感器网络的潜力在很大程度上仍未开发，费用对无线传感器网络是几个主要障碍之一，阻止了其更广阔的发展前景。
许多无线传感器网络组件正在趋于便宜化（例如有关计算能力的组件），而传感器本身仍是最昂贵的。
正如在在文献[5]中所指出的，成功的技术依赖于共享技术的原因是个人设备的大量花费。
然而，大多数传感器网络研究是基于一个单一的拥有长期部署的用户，模式不利于分享。
该技术管理的复杂性是另一个障碍。
大多数传感器的应用，有利于这样的共享模型。
我们立足本声明认为传感器可能不需要在一个长时间单一位置的原因包括：（1）一些现象可能出现变化速度缓慢，因此小批量传感器可进行可移动部署，通过测量信号，充分捕捉物理现象（2）可能是过于密集，因此多余的传感器可被删除。
（3）部署时间短。
我们将会在第三节更详细的讨论。
上述所有假定的有关传感器都可以进行部署和再部署。
然而有很多的无线传感器网络由于其实时监测和快速的网络功能可能被利用作为共享资源。
其作为共同部署资源要求，需要一些高效的技术，包括对传感器的一些挑战，如便携性，流动频繁的传感器内的部署，这使我们在第四节将会有大的挑战。
在本文中，我们专注于作为共享的可行性设计的传感器网络。
下面我们开始阐述传感网络在孟加拉国和加利福尼亚州的水质检测中的应用。
2．无线传感网络在水质监测中的应用无线传感器网络是通过把小型计算机设备连接到各式传感器和无线电而组成的。
这些设备自适应的形成特殊网络（暂时的点对点网络），通过无线方式对所处环境进行监测、处理。
其硬件和软件的设计非常低功耗以此达到长期在现场部署的目的，即此种部署在所处环境中人为干预性小。
设备大小通常从四分之一个个人数据处理机到类似一个个人数据处理机的装置那么大。
在一般情况下，资源可用性和功耗与设备大小是相一致的。
例如，虽然资源可用性在很大程度上取决于传感器的功耗，但是低功率节点（通常称为微尘）用两节AA电池可以运行大约一二个月。
传感器网络提供密集的空间和时间上的采样。
此种取样即使是在偏远和难以到达的地方均可采样。
因此，它是对于在时间上和空间上要求精确采集最适用的网络技术。
例如无线传感网络在土壤中的应用就是个很好的例子。
因为土壤环境在空间上是多样性的，需要精确的时间上的采样。
对于突然发生的变化都会被精确的采样及时记录下来。
事实上，无线传感器网络是一种低功耗的网络技术，对于一些发达地区其作为一种新兴技术适用性更为广泛。
此外，对于公共健康方面的应用极为重要。
例如，参考文献（17）阐述了人们对于水质的极高的关注度，“对水质的分析起初仍然是通过实验采样的办法将采集到的样本带回实验室进行研究。
”这种类型的数据收集和分析通常是非常耗时的且大多是不准确的，并在许多情况下，错过了人们对于及时关注的焦点的分析。
我们参与了两项正在进行的关于地下水质监测的无线传感网络部署：一项系统是以了解孟加拉国地下水中砷的含量为主。
另一项系统是通过研究孟加拉国地下水和土壤来监测硝酸盐的传播。
以上我们的部署都具有类似的设置。
一个塔架，是由外围箱体式的无线设备组成，这些设备在土壤中通过长导线连接到嵌入式传感器。
每个设备可以支持7个传感器，每个塔架都有多个设备。
多路塔架被部署在目的地周围，以达到空间上垂直和水平的密集部署。
这些设备将采集到的样本以无线方式传送给基站以供分析。
该部署的基站是一个个人数据处理机类的设备，也可以是一种轻便电脑。
它是通过由太阳能提供再充电的汽车电池来进行供电。
为了能够获得外部数据，我们的基站使用Zigbee技术，或在Zigbee不可用时，使用GPRS网络。
在孟加拉国，在恒河三角洲的几千万人饮用了已被砷严重污染的地下水，如果被污染的水量一直持续，由砷引起的患病率和皮肤癌将大约每年分别增加两百万和一万例，由砷引起的癌症的死亡率每年将会大约增加三千例。
我们对于控制砷在地下水中的动态变化是难以完全了解的。
在与孟加拉国的工程技术大学和麻省理工学院进行合作中，我们于2006年1月在靠近达卡的一个水稻地里部署了一个传感网络，目的是为了帮助确认这个假说成立。
一个完整的塔架应该包含3部分完整的传感器（土壤湿度，温度，碳酸盐，钙，硝酸，氯，氧化还原电位，氨氮，pH值），每个部署都具有不同的深度（在地面以下1，1.5，2米），在此基础上的压力传感器用来监测水的深度。
在干旱地区和半干旱地区水的短缺和不断增加的对于水资源的消耗已经促进人们重新再利用被处理过的废水。
尽管对于水资源的再利用人类收获了很多益处，但是已被处理的废水对于人类的健康和环境质量仍然存在着显而易见的危害。
解决这些危害需要进行自动的分布式的观测和控制灌溉水量，查出它所传输的污染物，包括暂停处理的或是还未处理的污染物，胶状污染物，药物，有机碳，挥发性有机化合物，治病微生物，营养素例如氮或磷。
在加利福尼亚的帕姆代尔，一个水质再利用现场是为测试土壤湿度，温度和硝酸盐的传感器网络而被用作的试车台。
此网络集合了两个方面：第一，确保此环境正在被监测，第二，提供对水质控制的反馈，从而达到优化水流量和减少化学物质渗透到地下。
这种现场也可以被用来在对孟加拉国进行部署前对软件，传感器和硬件的测试。
3．传感器共享技术对于传感器网络数据收集，即使是最小的传感器资源，其共享也将让许多人受益。
我们相信以下三种技术方法特别适用于传感器共享：（1）从一系列小型传感器大范围部署到精确仿真。
（2）从密集部署到稀疏部署逐渐移动冗余传感器（3）在一些可能的地区缩短部署周期。
在这里，我们更详细地描述这些场景，包括我们自己和别人在执行有关的或支持的算法时的工作的调查。
(1)精确仿真人类功能的移动性就是通过手动来模拟一个使用较少传感器的密集部署区。
人们可以移动一个领域的一小套传感器，对密集空间收集数据。
该技术将是只适合于可持续发展应用中，所关注的现象变化非常缓慢。
(2)密集到稀疏部署一些传感器网络应用需要一个密集映射的环境。
一旦传感器密集部署和细节的现象揭示，我们可以看到它可以捕获足够的资料较少的传感器，从而释放传感器部署在其他地方。
这里，我们描述适用的工作是正在进行中的传感器网络社区。
(3)部署周期短有些应用程序只需要短时间部署，因而对传感器的共享是种理想选择。
我们在孟加拉的部署是一个带有部署周期短的应用例子。
我们要收集数据，以验证有关昼夜变化的假设，所以我们希望数天时间来对数据进行分析。
4．挑战许多挑战性技术的出现，是为了能够快速部署和移动传感器，主要因为迄今为止的工作主要集中在静态的，长期运行的部署中。
我们已经有了趋于密集化的目标，降低高密度部署使之稀疏，使周期短的部署趋于平衡，我们发现以下三个挑战是最恰当的。
算法必须是具有人机通信功能的，对于人为错误是可以解决的。
对于系统故障必须迅速查明，并最大限度地通过正确的数据进行接收。
最后，系统必须迅速做出部署。
5．结论无线传感器网络可视为一种工具，其对于可持续发展来说具有很好的潜力。
如果我们视这种发展的无线传感网络技术为共享资源的话，它就可以得到技术社区的帮助。
为了使无线传感器网络作为一种共享资源得到落实，我们确定了三个有希望的技术方法：精确仿真，从密集部署到稀疏部署，实施短周期部署。
我们讨论了我们的工作部署，这些部署已证明了这些技术，描述了我们的过去和现在需要做哪些工作去面对即将出现的重大挑战。
Designing Wireless Sensor Networks as aShared Resourcefor Sustainable DevelopmentAbstract ：Wireless sensor networks (WSNs) are a relatively new and rapidly developing technology; they have a wide range of applications including environmental monitoring, agriculture, and public health. Shared technology is a common usa ge model for technology adoption in developing countries. WSNs have great potential to be utilized as a shared resource due to their on-board processing and ad-hoc networking capabilities, however their deployment as a shared resource requires that the technical community first address several challenges. The main challenges include enabling sensor portability–the frequent movement of sensors within and between deployments, and rapidly deployable systems–systems that are quick and simple to deploy.We first discuss the feasibility of using sensor net-works as a shared resource, and then describe our research in addressing the various technical challenges that arise in enabling such senso rportability and rapid deployment. We also outline our experiences in developing and deploying water quality monitoring wireless sensor networks in Bangladesh and California.Key words: WSNs、on-board processing1 IntroductionWireless Sensor Networks (WSNs), networks of wirelessly connected sensing and computational devices, hold tremendous promise for many areas of development including public health,the environment, and agriculture. A single device has a processor, a radio, and several sensors. When a network of these devices is deployed in a field, the sensing devices measure particular aspects of the environment. The devices then communicate those measurements by radio to one another and to more powerful computers for data analysis. In this way, WSNscan provide detailed observations of various phenomena that occur in the environment.WSNs are capable of measuring diverse phenomena such as contaminant levels in water, pollutants in the air, and the flow of water for irrigation.As an example of a potential application, consider the recent incident of contamination spilling into the Songhua river in China, the main source of drinking water for many people1. Determining rate of flow and sometimes direction of the river requires coordination of multiple sampling points. Sensors periodically taking samples at multiple locations along the river could determine the rate, quantity, and direction of contaminant flow using the distributed sensing and processing of a wireless sensor network.Unfortunately, the potential of wireless sensor net-works for sustainable development2 remains largely untapped while they are designed primarily for relatively resource-rich application contexts. The cost of WSNs is one of several major barriers that prevents them from being leveraged for sustainable development applications. Many components of WSNs are becoming cheaper (e.g. computing power), but the sensors themselves remain the most expensive component3. As stated in [5], successful technology-based international development projects rely on shared technology due to excessive cost of personal devices. However, most research on sensor networks is based on long-term deployments owned by a single user, a paradigm not conducive for sharing. The complexity of technology management is another barrier. We use Grameen telecom as a successful model 4 in which the management and maintenance of shared hardware is centralized. We envision a sensor network much in the same light.Many sensor network applications are conducive to such a shared model. We base this statement on the observation that sensors may not be required in a single location for extended periods of time for reasons including: (1) a phenomenon of interest may have a slow rate of change, thus a small number of sensors can bemoved within a deployment, emulating the density required to suciently capture the physical phenomena, (2) the initial deployment may have been too dense, thus redundant sensors can be removed, and (3) the duration of the deployment may be short. We discuss these scenarios in more detail in Section 3.All of the deployment scenarios mentioned above rest on the assumption that sensors can be easily deployed and re-deployed. While WSNs have great potential to be utilized as a shared resource due to their on-board processing and ad-hoc networking capabilities, their deployment as a shared resource requires that the technical community first address several challenges, including enabling sensor portability – the frequent movement of sensors within and between deployments, and rapidly deployable systems – systems that are quick and simple to deploy. This leads us to our major challenges in Section 4.Clearly, the primary issues related to successful technology adoption are the social, policy, and logistical questions to be answered in order to enable equitable access and the design of culturally appropriate technology. Our experience, though relevant, is limited to our technical expertise. These challenges and others should be formulated more explicitly with the necessary diverse input from communities, activists, governments and NGOs.In this paper we focus on justifying the technical feasibility of designing sensor networks as a shared technology (Section 3) and describing the technical challenges that must be addressed to enable WSNs as a shared technology (Section 4). We begin by describing our applications in water quality monitoring in Bangladesh and California (Section 2).2 WSNs For Water QualityWireless sensor networks are made up of small computational devices connected to various sensors and wireless radios. The devices automatically and adaptively form ad-hoc networks (temporary point-to-point networks) over wireless radios to make decisions based on measurements of their environment. Thehardware and software are designed to be extremely low power in order to enable long-term in-situ deployments, i.e. undisturbed deployments that are left in the environment with minimal human intervention. Device sizes commonly range from that of a quarter to a PDA-like device. In general, resource availability and power consumption are commensurate with size. For example, while it largely depends on the power consumption of the sensors, the lower-power nodes (often called motes) can run for approximately one month on 2 AA batteries.Sensor networks provide dense spatial and temporal sampling even in remote and hard to reach locations. Thus, they are best applied to applications that need dense sampling in space and/or time. Soi applications are a good example, because the soi environment is heterogeneous across space, requiring dense spatial sampling. Abrupt changes can then be captured with a high temporal sampling rate.The fact that WSNs are low power and wireless makes them appealing as a technology for developing regions, but in addition the dense sampling is crucial for public health applications. For example, [17] states that while water quality concerns can be extremely critical, “analysis is still primarily conducted in a laborious manner by physical collection of a sample that is analyzed back in a laboratory.” This kind of data collection and analysis is time consuming and mostly undirected, and in many instances misses the toxin events of interest.We are involved with two ongoing WSN deployments related to groundwater quality: a system to understand the prevalence of arsenic in Bangladesh groundwater, and a system to monitor nitrate propagation through soils and ground water in California.Both of our deployments have a similar setup. A pylon [10] (Figure 2) consists of an enclosure housing the small wireless devices which connect to groups of sensors embedded at multiple depths in the soil through long wires. Each devicecan support 7 sensors and there are multiple devices per pylon. Multiple pylons are deployed around the field to attain vertical and horizontal spatial density. The devices wirelessly transmit samples back to a base-station for analysis (Figure 1). The base-station in these deployments was a PDA-class device. It could also be a laptop. It is powered by a car-battery recharged using solar panels. To make data externally accessible, our base-station is connected using Zigbee or where Zigbee is unavailable, using a GPRS (i.e. cellular) network.In Bangladesh, tens of millions of people in the Ganges Delta drink ground water that is dangerously contaminated with arsenic. If consumption of contaminated water continues, the prevalence of arsenicosis and skin cancer will be approximately 2,000,000 and 100,000 cases per year, respectively, and the incidence of death from cancer induced by arsenic will be approximately 3,000 cases per year [18].A full understanding of the factors controlling arsenic mobilization to ground water is lacking. In a joint collaboration with scientists at the Bangladesh University of Engineering and Technology and MIT, we deployed a sensor network in January of 2006 in a rice field near Dhaka,Bangladesh in order to aid in validating this hypothesis. A full pylon contains 3 complete suites of sensors (soil moisture, temperature, carbonate, calcium, nitrate, chloride, oxidation-reduction potential, ammonium, and pH), each deployed at a different depth (1, 1.5, and 2 meters below ground), and a pressure transducer at the base to monitor water depth.Water scarcity in arid and semi-arid regions and increasing demand on water supplies has stimulated interest in the reuse of treated wastewater. Despite the many benefits to irrigating with reclaimed water,there remain both real and perceived risks to human health and environmental quality stemming from residuals in the treated wastewater. Proactively addressing these risks requires automating the distributed observation and control of the irrigation water and thetrace pollutants that it conveys, including suspended or dissolved solids (TDS), colloidal solids, pharmaceuticals, organic carbon, volatile organic compounds, pathogenic microorganisms, and nutrients such as nitrogen or phosphorus. A water reuse site in Palmdale, California is being used as a testbed for a sensor network with soil moisture, temperature, and nitrate sensors. The network focuses on two things: first,ensuring that environmental regulations are being met, and second, providing feedback to a water control system in order to optimize water flow and minimize chemical penetration into the subsurface. This site is also used to test the software, sensors, and hardware before deploying in Bangladesh.3 Sensor Sharing TechniquesSensor sharing will allow many people to benefit from sensor network data collection, even with minimal sensor resources. We believe the following three technical approaches are particularly suited for enabling sensor sharing for sustainable development:(1) moving a smaller number of sensors around in a deployment to emulate density, (2) gradually removing redundant sensors from a deployment to go from dense to sparse deployments, and (3) leveraging shorter deployment cycles where possible. Here we describe each of these scenarios in greater detail, including a survey of our own and othe rs’ work in implementing related or supporting algorithms.（1）Emulating DensityHuman-enabled mobility can be used to manually emulate the effect of a dense deployment using fewer sensors. People can move a small set of sensors around in a field in order to collect data for a dense spatial map of the field. This technique will be appropriate only for sustainable development applications in which the phenomenon of interest changes very slowly, on the order of days or longer.（2）Dense to Sparse DeploymentsSome sensor network applications require a dense mapping of the environment. Once sensors are densely deployed and details of the phenomenon are revealed, we may see it is possible to capture sufficient information with fewer sensors, freeing sensors for deployment elsewhere. Here we describe applicable work which is ongoing in the sensor network community.（3）Short Deployment CyclesSome applications only require short-duration deployments and therefore are ideal for sensor sharing. Our deployment in Bangladesh is an example of an application with a short deployment cycle. We wanted to collect data to validate a hypothesis about diurnal variations, and so we wanted several days of data for analysis.4 ChallengesNumerous technical challenges arise in order to be able to quickly deploy and move sensors, primarily because the work to date has largely focused on static, long-running deployments.Given that we have the goals to emulate density, reduce dense deployments to sparse ones, and leverage short deployments c ycles, we find the following three challenges to be the most pertinent. Algorithms must be interactive and robust to human error. Faults in the system must be quickly identified to maximize the amount of good data received.Finally, systems must be made to be rapidly deployable.5 ConclusionWireless sensor networks have the potential to be a useful tool for sustainable development. This can be facilitated by the technical community if we focus on issues with developing wireless sensor net-works as a shared technology. 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