English name: transducer / sensor Sensor is a physical device or biological organ， can detect， feel the outside world signals， the physical conditions (such as light， heat， humidity) or the chemical composition (such as smoke)， and finding out information to other devices or The definition of sensor National Standard GB7665-87 under the definition of the sensor are: "can feel the provisions must be measured and in accordance with the law can be used to convert the signal device or devices， usually from sensitive components and conversion " Sensor is a detection device， can be felt by measuring the information， and can feel Detected information， according to the law of transformation must be a signal or other form of information required for the output to meet the needs of information transmission， processing， storage， display， recording and control is the automatic detection and automatic implementation of the primary Sensor classification Different points of view can be used to classify the sensor: the principle of their conversion (sensor basic physical or chemical effect); their uses; their output signal type and the production of their materials and According to the sensor working principle， can be divided into physical sensors and chemical sensors two broad categories: Sensor working principle of the classification of the physical sensor applications are physical effects， such as the piezoelectric effect， magnetostriction phenomenon， ionization， polarization， thermoelectric， photovoltaic， magnetic effects， such as Measured the volume of small changes in signal will be converted to electrical Chemical sensors， including those by chemical adsorption， electrochemical reaction， such as the situation for the causal relationship between sensors， the measured signal small changes in volume will also be converted into electrical Some sensors can not divided into the physical category， it should not be divided into chemical Most of the sensor is based on basic physical principles for the Chemical sensor technology more questions， such as reliability issues， the possibility of mass production， prices ， and have solved these problems， the application of chemical sensors will have 。 Common sensor applications and operating principle are presented in Table In accordance with its purposes， the sensor can be classified as: Stress sensitivity and the force sensor position sensor � Level sensor � energy sensor Speed Sensor � thermistor sensor Acceleration sensor �-ray radiation sensor vibration sensor humidity sensor Magnetic Sensor Gas Sensor Vacuum sensors biosensors， � � � Its output signal as the standard sensors can be divided into: Analog sensors - will be measuring the volume of non-electrical converted into analog � � sensor - will be measured non-electrical quantity into a digital output signal (both direct and indirect conversion) � � Ying digital sensor - will be measuring the amount of signal into frequency signals， or short-cycle signal output (including direct or indirect conversion) � � 。 Switch sensor - When a measurement signal to achieve a specific threshold， the sensor output corresponding to a specified low or high � � At the role of external factors， all materials will be made accordingly， with a characteristic Them the role of those outside the most sensitive material， that is， those with functional properties of the material， was used to produce a sensor sensitive Materials from the application point of view of the sensor can be divided into the following categories: � (1) in accordance with the category of Materials � according to the physical properties of materials at (2) � � conductor insulator semiconductor � � magnetic � (3) The crystal structure of sub-Materials � Single crystal polycrystalline � � � Amorphous Materials ：� With the use of new materials is closely related to the development of the sensor can be summed up in the following three directions: � (1) at a known material to explore new phenomena， effects and response， and then enable them to be at sensor technology actually � � (2) to explore new materials， application of those known phenomenon， effects and response to improve the sensor � � (3) at the basic research on new materials to explore new phenomena and new effects and reactions， and in sensor technology to be the specific � � Modern sensor manufacturing progress depends on sensor technology for new materials and sensitive components of the development of The basic trend of the sensor development are dielectric materials and semiconductor applications， as well as closely Table 2 can be used to give a number of sensor technology， be able to convert energy forms of � � ： In accordance with its manufacturing process， the sensor can be divided into: Integrated thin-film sensor sensors � � � ceramic thick-film sensor sensor Integrated sensors are produced using standard silicon-based technology of semiconductor integrated circuits Will also be used for the initial treatment is usually measured part of the signal circuit is also integrated in the same � � Thin-film sensor is deposited on dielectric substrates through (substrate) on the corresponding sensitive material film The use of hybrid technology， the same part of the circuit can be manufactured on this � � Thick-film sensor is the use of the corresponding material slurry， coating on the ceramic substrate made of， the substrate is usually made of Al2O3， and then heat-treated， so that thick-film Ceramic sensors using standard ceramic technology， or some variant of process (sol - gel， ) � � Completed the appropriate preparatory actions， have been forming components at high temperature in Thick film and ceramic sensors that between two kinds of processes have many common characteristics， in some respects， you can think of ceramic art thick film technology is a � � Each technology has its own strengths and Because of the research， development and production of a lower capital investment requirements， as well as the high stability of the sensor parameters and other reasons， the use of ceramics and thick film sensors more
这是一篇 PHD的论文，谈论有关 无线传感网络 的，你看下，是否符合你需要，如果类型都不一致，那就没必要翻译了。Mechanisms for energy conservation in wireless sensor networksSupervisor: Maurizio BonuccelliThesis commettee: Paolo Ferraggina， Piero MaestriniExternal referees: Stefano Basagni， Mani SrivastavaNational commettee: Bugliesi， Meo， and Panzieri December 27， 2005 AbstractIn this thesis we address the problem of reducing energy consumption in wireless sensor We propose a suit of techniques andstrategies imported from other research areas that can be applied to design energy-efficient protocols in sensor They includetime series forecasting， quorums systems， and the interaction between sensor properties and protocol We apply these techniques to the time synchronization problem， to efficiently collecting data from a sensor network， and to ensuring stronger data consistency guarantees in mobile We show in [1，2，3，4] that time series forecasting techniques， and in particular autoregressive (AR) models， can be applied to sensor networks to conserve We study a simple type of time series models with a short prediction We have chosen this model because it is capableof predicting data produced by real-world sensors measuring physical phenomena， and it is computationally tractable on modern-generation sensor We apply these models to solve two relevant problems in sensor networks: the problem of efficiently collecting sensor data at the sink， and the time synchronization We propose an energy-efficient framework， called SAF Similarity--based Adaptable query Framework [1，2] )， for approximate querying and detecting outlier values in sensor The idea is to combine local AR models built at each node into a global model stored at the root of the network(the sink) that is used to approximately answer user Our approach uses dramatically fewer transmissions than previous approximate approaches by using AR models and organizing the network into clusters based on data similarity between Our definition of data similarity is based on the coefficients of the local AR models stored at the sink， which reduces energy consumption over techniques that directly compare data values， and allows us to derive an efficient clustering algorithm that is provably optimal in the number of clusters formed by the Our clusters have several interesting features that make them suitable also for mobile networks: first， they can capture similarity between nodes that are not geographically adjacent; second， cluster membership adapts at no additional cost; third， nodes within a cluster are not required to track the membership of other nodes in the Furthermore， SAF provides provably correct error bounds and allows the user to dynamically tune answer quality to answer queries in an energy and resource efficient In addition， we apply the AR models to solve the time synchronization problem from a novel perspective which is complementary to the well-studied clock synchronization problem [3，4] More precisely， we analyze the case in which a sensor node decides to skip one or more clock adjustments to save energy， or it is temporarily isolated， but still requires an accurate estimate of the We propose a provably correct clock method based on AR models， which returns a time estimate within a constant (tunable) error bound and error This method is highly adaptable and allows the sensor to decide how manyclock adjustments it can skip while maintaining the same time accuracy， thus saving In addition， we propose a suit of deterministic methods that reduce the time estimation error by at least a factor More precisely， we propose a provably correct deterministic clock reading method， called the DCR method， which exploits information regarding the sign of the clock deviation， and can be applied to reduce by half the frequency of the periodic clock adjustments， while maintaining the same error bound [3，4] This method is of both practical and theoretical In fact， it leads to a noticeable energy saving， and shows that a stronger but realistic clock model can lead to a refinement of the optimality bound for the maximum deviation of a clock that is periodically In addition， we propose a generalized version of the DCR method that enhances its accuracy depending on the clock stability， and a method that guarantees the monotonicity of the time values We analyze for the first time quorum system techniques in the context of sensor networks: we redesign them and show their benefits in terms of energy consumption [6] Quorum systems have the potential to save energy in sensor networks since they can reduce noticeably the amount of communication， improve the load balance among sensor nodes， and enhance the scalability of the However， previous quorum systems and quorum metrics， proposed for wired networks， are unsuitable for sensor networks since they do not address their properties and These observations have motivated us to redesigning quorum systems and their metrics， taking into account the limitations and characteristics of sensors (， transmission costs， limited energysource， physical radio broadcast)， and the network More precisely， we redefine the following quorum metrics: load balance， access cost and quorum capacity， and devise some strategies based on some characteristics of sensor networks that reduce the amount of communication when designing quorum systems for sensor We apply these strategies to design a family of energy-efficient quorum systems with high In particular， we propose a quorum construction that reduces the quorum access cost， and propose an energy-efficient data diffusion protocol built on top of it that reduces the energy consumption by reducing the amount of transmissions and In addition， we analyze quorum systems in case of high node More precisely， we study the difficult problem of guaranteeing the intersection between two quorums in case nodes move continuously along unknown paths [7] We address this problem by defining a novel mobility model that provides a minimum set of constraints sufficient to derive strong data guarantees in highly mobile Also in this case， we show the unsuitability of previous quorum systems， and provide a condition which is necessary to guarantee data availability and atomic consistency under high node We propose a new classof quorum systems， called Mobile Dissemination (MD) quorums， suitable for highly mobile networks， and propose a quorum construction which is optimal with respect to the quorum size (， message transmissions) [7] Then， we apply the MD quorum system to implement a provably correct atomic read/write shared memory for mobile and sparse Bibliography [1] D Tulone， S M PAQ: Time series forecasting for approximate query answeringin sensor In P of the 3rd European Workshop on Wireless Sensor Networks， 21-37， Feb [2] D Tulone， S M An energy-efficient querying framework in sensor networks for detecting node Submitted to [3] D T On the feasibility of global time estimation under isolation conditions in wireless sensor To appear in A[4] D T A resource-efficient time estimation for wireless sensor In P of the 4th Workshop of Principles of Mobile Computing， 52-59， Oct [5] D T How efficiently and accurately can a process get the reference time? I S on Distributed Computing， O Brief announcement， 25-[6] DTulone， E D D Redesigning quorum systems for wireless sensor Submitted to [7] D T Is it possible to ensure strong data guarantees in highly mobile networks? Submitted to
