(contact author) Wireless Sensor Networking Support to Military Operations on Urban Terrain
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。
Wireless Sensor Networks based on Compressed Sensing
School of Automation Engineering University of Electronic Science and Technology of China Chengdu, China zhuangxyan@
Abstract-The data collected through high densely distributed wireless sensor networks is immense. The asymmetry between the data acquisition and information processing makes a great challenge to the restriction of energy and computation consumption of the sensor nodes, and it limits the application of wireless sensor networks. However, the recent works show that compressed sensing can break through this limitation of asymmetry. Compressed sensing is an emerging theory that is based on the fact that a signal can be recovered through a relatively small number of random projections which contain most of its salient information. In this paper, we introduce the background of compressive sensing, and then applications of compressed sensing in wireless sensor networks are presented. Keywords-wireless sensor networks; data compression; compressed sensing; sparsity
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。
Wireless sensor network
The Greenhouse Environment Monitoring System Based on Wireless Sensor Network TechnologyΙ..INTRODUCTIONПZIGBEE TECHNOLOGYWireless sensor network(WSN) integrates the sensor network techonology, information processing technology and network communication technology with the feature of small size, low cost and easy maintenance, which has a wide application in the area of environment data collection,security monitoring and target tracking.无线传感器网络（WSN）集成了传感器网络的技术，信息处理技术和网络通信技术，具有体积小，成本低，维护方便的特点，在环境数据采集，安全监控和跟踪目标领域具有广泛的应用。
It comprises a great many wireless sensor nodes deployed in the monitoring region, and through wireless communication a multi-hop self-organizing network system is formed.它包括许多部署在监测区域的无线传感器节点，并且通过无线通信一个多跳的自组织网络系统形成了。
Its role is to coordinate the perception , acquisition and process of the information of its perceptual objects within the area covered by the network, and returned data to the observer.At present, large amount of widely-distributed electronic detection devices and implementing facilities are greatly used in greenhouse production , resulting in intertwining cables（相互交织的电缆）in the greenhouse production .目前，大量分布广泛的电子检测设备和执行设备被广泛地运用在温室生产中，导致了温室生产中存在相互交织的电缆。
Wireless Sensor Networks
Wireless Sensor Networks Wireless Sensor Networks (WSNs) have become an essential part of modern technology, with applications ranging from environmental monitoring to industrial automation. These networks consist of a large number of sensor nodes that are wirelessly connected to gather and transmit data. However, WSNs face several challenges and issues that need to be addressed to ensure their efficient and reliable operation. One of the primary problems with WSNs is the limited power supply of sensor nodes. Most sensor nodes are powered by batteries, which have a finite lifespan and need to be replaced or recharged periodically. This limitation poses a significant challenge for WSNs, especially in applications where the sensor nodes are deployed in remote or inaccessible locations. The need for frequent maintenance and replacement of batteries can increase the overall cost and complexity of WSNs, making them less practical for long-term deployments. Another issue that WSNs face is the limited processing and storage capabilities of individual sensor nodes. Due to their small size and low power consumption requirements, sensor nodes typically have limited processing power and memory. This limitation can affect the ability of WSNs to perform complex data processing and analysis tasks, especially in applications that require real-time or near-real-time decision-making. Additionally, the limited storage capacity of sensor nodes can restrict the amount of data that can be collected and stored locally, requiring frequent data transmission and storage in a central location. Furthermore, WSNs are susceptible to various security and privacy threats, which can compromise the integrity and confidentiality of the data collected and transmitted by the sensor nodes. Since WSNs are often deployed in open and uncontrolled environments, they are vulnerable to physical attacks, tampering, and eavesdropping. Moreover, the wireless nature of communication in WSNs makes them susceptible to interception and unauthorized access by malicious entities. Ensuring the security and privacy of data in WSNs is crucial, especially in applications where sensitive or critical information is being collected and transmitted. In addition to these technical challenges, the design and deployment of WSNs also need to consider the environmental impact and sustainability of the network. The disposal of batteries and electronic components from sensor nodes cancontribute to electronic waste, posing environmental hazards if not managed properly. Moreover, the energy consumption of WSNs, especially in large-scale deployments, can have a significant carbon footprint. Addressing these environmental concerns is essential to ensure the long-term viability and acceptance of WSNs as a sustainable technology. Despite these challenges, there are ongoing efforts and research initiatives aimed at addressing the issues faced by WSNs. For instance, advancements in energy harvesting technologies, such assolar panels and kinetic energy harvesters, can help extend the lifespan of sensor nodes and reduce the reliance on battery replacements. Similarly, the development of low-power and energy-efficient communication protocols and algorithms can help minimize the energy consumption of WSNs, prolonging their operational lifetime and reducing their environmental impact. Furthermore, the integration of advanced security mechanisms, such as encryption, authentication, and intrusion detection systems, can enhance the resilience of WSNs against security threats. Additionally, the use of secure and reliable communication protocols, along with physicalsecurity measures, can help mitigate the risks associated with unauthorized access and tampering. By addressing these technical and security challenges, WSNs can be made more robust and trustworthy for a wide range of applications. In conclusion, while WSNs face several challenges and issues, there are ongoing efforts to address these concerns and improve the efficiency, reliability, and security of these networks. By leveraging advancements in energy harvesting, communication protocols, and security mechanisms, WSNs can overcome their limitations and become a sustainable and dependable technology for various applications. It is essential to continue investing in research and development to ensure the long-termviability and success of WSNs in the rapidly evolving landscape of wireless communication and sensing technologies.。
无线传感器网络管理技术
第38卷 第1期2011年1月计算机科学Computer Science Vo l .38No .1Jan 2011到稿日期:2010-03-03 返修日期:2010-06-17 本文受国家自然科学基金(60873241),国家重大专项(2009ZX03006-001-01),北京市自然科学基金(4092011)和中国科学院专项(KGC X2-YW -149)资助。
赵忠华(1983-),男,博士生,CCF 会员,主要研究方向为无线传感器网络管理,E -mail :zhaozhongh ua @is .iscas .ac .cn ;皇甫伟(1975-),男,博士,助理研究员,主要研究方向为无线网络、自组织网络和无线传感器网络;孙利民(1966-),男,博士,研究员,主要研究方向为无线传感器网络和多媒体通信技术。
无线传感器网络管理技术赵忠华1,2,3 皇甫伟1 孙利民1 杜腾飞4(中国科学院软件研究所 北京100190)1(信息安全国家重点实验室 北京100049)2(中国科学院研究生院 北京100049)3 (北京大学软件与微电子学院 北京100871)4摘 要 无线传感器网络是一个资源受限、应用相关的任务型网络,与现有的计算机网络有显著差异。
现有的网络管理不再适用于无线传感器网络,面临着诸多新的挑战。
首先简要介绍了无线传感器网络管理的技术背景,并结合无线传感器网络自身的特点,给出了相应的无线传感器网络的管理技术应具备的特征等。
然后提出了一个通用的无线传感器网络管理框架,并对其中的各管理内容及研究进展进行了详细论述。
最后探讨了无线传感器网络管理领域面临的公开难题,并针对目前发展现状提出了今后的研究方向。
关键词 无线传感器网络,网络管理,管理技术中图法分类号 T P311 文献标识码 A Wireless Sensor Network Management TechnologyZ H AO Zhong -hua 1,2,3 H U A NG F U W ei 1 SU N Li -min 1 D U T eng -fei 4(In stitu te of S oftw are ,Chinese Academy of Sciences ,Beijing 100190,C hina )1(State Key Lab oratory of In formation S ecurity ,Institute of Softw are ,C AS ,Beijing 100049,China )2(Graduate Univers ity of Chines e Academy of Sciences ,Beijing 100049,C hina )3(Sch ool of S oftw are and M icroelectronics ,Beijing University ,Beijin g 100871,Chin a )4A bstract Wireless sensor netw o rks a re resource -constrained and applicatio n -r elated .Wireless senso r netwo rks are dif -fe rent f rom o ther t raditio nal com puter netwo rks ,so the traditio nal netw or k management is no longer applied to wireless sensor netw o rks and w ireless se nso r ne tw o rk manag ement is faced with many challenges .T his pape r briefly described the techno log y backg round of the wireless sensor netwo rk ma nag ement ;g ave the cor responding manag ement characteri -stics in w ireless sensor ne tw o rks w ith the cha racteristics o f wirele ss senso r ne two rk itself ;then put for war d a commo n framew ork o f wirele ss senso r netwo rk manag ement and discussed the contents of the various ma nag eme nt and re sear ch prog ress in detail ;finally ,w e discussed the public challenges facing the wireless sensor ne tw o rk manag ement and poin -ted o ut the future research directio ns .Keywords Wireless senso r netwo rks ,N etw or k management ,M anag ement techno lo gy 无线传感器网络(Wirele ss Senso r Ne tw o rks ,WS N ,简称传感器网络)由大量低成本的微型传感器节点组成,协作地实现所部署区域的感知信息收集、处理和传输任务,可广泛应用于安全反恐、智能交通、医疗救护、环境监测、精准农业和工业自动化等诸多领域,受到了工业界和学术界的普遍重视,近年来不仅取得了大量的科研成果,也得到了一定的实际应用。
无线传感器网络应用文章英文
无线传感器网络应用文章(英文) Wireless Sensor Network ApplicationsIntroduction:Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their potential for numerous applications in various fields. A WSN consists of a large number of small, low-cost sensor nodes that are wirelessly connected to monitor physical or environmental conditions. These nodes can collect, process, and transmit data to a central base station for further analysis. This article aims to explore some of the most promising applications of WSNs.Environmental Monitoring:One of the most common applications of WSNs is environmental monitoring. These networks can be deployed in remote or hazardous areas to monitor parameters such as temperature, humidity, air pollution, and water quality. For instance, in forest fire detection, sensor nodes can detect abnormal temperature increases and transmit an alert to authorities, enabling timely intervention. In agriculture, WSNs can monitor soil moisture levels and provide farmers with real-time data to optimize irrigation.Healthcare:WSNs have also found applications in the healthcare industry. They can be used to monitor vital signs of patients, such as heart rate, blood pressure, and body temperature. Sensor nodes attached to patients can wirelessly transmit data to healthcare professionals, enabling continuous monitoring and early detection of any abnormalities. WSNs areparticularly useful in remote patient monitoring, allowing patients to receive medical attention from the comfort of their homes.Smart Homes and Buildings:WSNs can play a crucial role in creating smart homes and buildings. By deploying sensor nodes throughout a building, various parameters such as temperature, lighting, occupancy, and energy consumption can be monitored and controlled. This enables energy-efficient operations by optimizing heating, cooling, and lighting systems based on real-time data. Additionally, WSNs can enhance security by detecting unauthorized access or unusual activities within a building.Industrial Automation:WSNs are widely used in industrial automation to monitor and control different processes. For example, in manufacturing plants, sensor nodes can collect data on machine performance, temperature, and vibration levels, allowing for preventive maintenance and reducing downtime. WSNs can also be used for inventory management, tracking the movement of goods within a warehouse, and ensuring timely restocking.Traffic Management:WSNs can significantly contribute to improving traffic management in urban areas. By deploying sensor nodes along roads, real-time traffic data, such as vehicle density and speed, can be collected. This information can be used to optimize traffic signal timings, detect congestion, and provide drivers with alternative routes, reducingoverall travel time and fuel consumption. WSNs also enable the implementation of intelligent transportation systems, enhancing safety and reducing accidents.Conclusion:Wireless Sensor Networks have found numerous applications in various fields, ranging from environmental monitoring to healthcare, smart homes, industrial automation, and traffic management. These networks offer a cost-effective and scalable solution for collecting and analyzing datain real-time. As technology continues to advance, it is expected thatthe applications of WSNs will continue to expand, revolutionizing different industries and improving the quality of life for people around the world.。
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。
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12TH ICCRTS “Adapting C2 to the 21st Century”Wireless Sensor Networking Supportto Military Operations on Urban TerrainTrack 2: Networks and NetworkingTrack 8: C2 Technologies and SystemsDr. António Grilo IST/UTL, INESC, Rua Alves Redol, nº 9 1000-029 LISBOA,PortugalTel: +351-213100226 antonio.grilo@inesc.pt (contact author)Rui SilvaESTIG,Rua Afonso III,nº17800-050 Beja,Portugalrs.beja@Lt Col Paulo NunesCINAMIL/Academia MilitarPaço da Rainha, 291169-203 LISBOA,Portugalpfvnunes@net.sapo.ptMaj José MartinsCINAMILAcademia MilitarPaço da Rainha, 291169-203 LISBOA,Portugaljosecarloslm@netcabo.ptProf. Mário NunesIST/UTL, INESC,Rua Alves Redol, nº 91000-029 LISBOA,PortugalMario.nunes@inesc.ptWireless Sensor Networking Support to Military Operations on Urban Terrain1Dr. António Grilo IST/UTL, INESC, Rua Alves Redol, nº 9 1000-029 LISBOA,PortugalTel: +351-213100226 antonio.grilo@inesc.pt (contact author)Rui SilvaESTIG,Rua Afonso III,nº17800-050 Beja,Portugalrs.beja@Lt Col Paulo NunesCINAMIL/Academia MilitarPaço da Rainha, 291169-203 LISBOA,Portugalpfvnunes@net.sapo.ptMaj José MartinsCINAMILAcademia MilitarPaço da Rainha, 291169-203 LISBOA,Portugaljosecarloslm@netcabo.ptProf. Mário NunesIST/UTL, INESC,Rua Alves Redol, nº 91000-029 LISBOA,Portugalmario.nunes@inesc.ptAbstractFP6 IST research project Ubiquitous Sensing and Security in the European Homeland (UbiSeq&Sens) aims at providing a comprehensive architecture for medium and large scale Wireless Sensor Networks (WSN)s, with the full level of security and reliability required to make them trusted and secure for all applications, while considering early-warning and tracking in a Homeland Security/Defense context (e.g., support of anti-terrorist SWAT team operations) as one of the scenarios for system demonstration. This paper extrapolates from this scenario, defining an architecture for WSNs supporting Military Operations in Urban Terrain (MOUT) in the context of XXIst century Operations Other Than War (OOTH). Based on the defined architecture, the authors identify the main WSN Networking and Security issues and challenges that must be overcome to provide the assurance, efficiency and reliability required by the warfighter, which constitute the focus of ongoing work in IST FP6 UbiSeq&Sens.Keywords:.Wireless Sensor Networks, Network Centric Military Communications, Military Operations on Urban Terrain, IST FP6 UbiSec&Sens1 IntroductionWireless Sensor Networks (WSNs) have motivated intense research, in academia, industry and on the military sector due to its potential to support distributed micro-sensing in environments for which conventional networks are impractical or when the required sensor density demands a robust, secure and cost-effective solution. WSNs rely on large numbers of cheap devices, which are greatly limited in terms of processing, communications and autonomy capabilities. Despite reduced, the capabilities of these devices are leveraged through collaboration in distributed in-network data fusion and processing tasks, with final results that are equivalent to those obtained with centralized processing.Early-warning and tracking is an application where WSNs have seen significant progress in the last few years, with some practical solutions already existing on the market. However, these commercial WSN systems still lack the security, reliability and efficiency required for this kind of application. FP6 IST research project Ubiquitous Sensing and Security in the European Homeland (UbiSeq&Sens) tries to overcome these limitations. The overall objective of UbiSeq&Sens is to provide a comprehensive architecture for medium and large scale WSNs, with the full level of security and reliability required to make them trusted and secure for all applications, while considering early-warning and tracking in a Homeland Security/Defense context (e.g., support of anti-terrorist SWAT team operations) as one of the scenarios for system demonstration. This 1 The work described in this paper is based on results of IST FP6 project UbiSec&Sens (/). UbiSec&Sens receives research funding from the European Community's Sixth Framework Programme. Apart from this, the European Commission has no responsibility for the content of this paper.The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability.project, which started in the beginning of 2006 has now completed the scenario specification phase, triggering the beginning of the design work.The end of the Cold War in the beginning of the 1990s and the reality brought by the dramatic events of September 11th 2001 have led to a shift of the focus of military operations to Operations Other Than War (OOTW) with emphasis on Peace Keeping, Making and Building. The Rules Of Engagement (ROE) associated with these missions significantly constrain the options available to warfighters engaged on Military Operations on Urban Terrain (MOUT). In fact, most of the reality experienced by these warfighters bears more similarity with Homeland Security and counterterrorism operations than with traditional military operations. Consequently, many of the operational concepts are common to both types of scenarios, and so are the supporting technologies. In effect, in the context of so called Three Block War2 [1], characteristics of the XXIst century, the use of disproportionate force in MOUT is unacceptable, requiring clearing of hostile urban areas to be made block-by-block, or even room-by-room by infantry teams that must directly intervene on the scene. The complexity of urban environments usually precludes full situation awareness. This, coupled with the fact that the adversary is usually expected to have a better understanding about the operational environment, poses significant risks to the life and integrity of warfighters. In such missions, Network Centric Warfare [2] assisted by robust sensor networking is paramount to reduce situation uncertainty, providing early-warning and tracking of unpredicted intrusions in areas considered already cleared, thus denying the intruder the advantage of surprise.This paper proposes a WSN architecture in the context of MOUT (section 2), extrapolating from the UbiSeq&Sens Homeland Security scenario definition, but taking into account MOUT specificities. Based on the delineated architecture, it identifies the main WSN Networking and Security issues and challenges (sections 3 and 4 respectively) that must overcome in order to provide the assurance and reliability required by the warfighter, which constitute the focus of ongoing work in IST FP6 UbiSeq&Sens. Finally, the paper presents some conclusions and the envisaged way ahead (section 5).2 MOUT WSN ArchitectureThe MOUT WSN architecture is depicted in Figure 1. The MOUT WSN nodes are deployed by infantry team elements as they clear the terrain. Once deployed, these nodes self-organize into a multi-hop network, establishing data paths from each individual sensor to special sink nodes. Sink nodes may operate as gateways between the WSN and higher echelon tactical networks, using appropriate technologies like the Joint Tactical Radio System (JTRS) or SATCOM to connect to Command Posts (CP)s where the information from several information systems, sensors and sensor networks is gathered, fused and analysed and where higher-level tactical decisions are made. Deployment must take into account robustness in the connectivity to CPs and consequently it is desirable to deploy several sink nodes, conveniently positioned in a way that minimizes the risk of WSN partition. Warfighters may also be equipped with Personal Digital Assistants (PDAs) or other wireless terminals that allow direct connection to the WSN, turning them into mobile sink nodes. Similar capabilities may be available to robotic elements like Unmanned Aerial Systems (UAS)s or Unmanned Ground Vehicles (UGV)s. Both warfighters and robots may also carry sensors, making them mobile source nodes as well.2 General Charles Krulak (USMC) set the stage for the importance of the flexibility and innovation required from the Strategic Corporal when he discussed the need to fight the “three block war.” He stated that, "in one moment in time, our service members will be feeding and clothing displaced refugees - providing humanitarian assistance. In the next moment, they will be holding two warring tribes apart - conducting peacekeeping operations. Finally, they will be fighting a highly lethal mid-intensity battle. All on the same day, all within three city blocks. It will be what we call the three block war." In. Krulak, Charles. “The Strategic Corporal: Leadership in the Three Block War.” Marine Corps Gazette. Vol 83, No 1. January 1999. pp. 18-22.Figure 1: Architecture of the MOUT WSN.Functional requirements point to the use of the following sensor types:•Presence/Intrusion (e.g., based on a combination of infrared, photoelectric, laser, acoustic, vibration, etc.);•Ranging3 (e.g., RADAR, LIDAR, ultrasonic, etc.);•Imaging (including infrared and LADAR imaging)4;•Noise (acoustic sensor able to produce an audio stream);•Chemical, Biological, Radiological, Nuclear and Explosive (CBRNE) and Toxic Industrial Material (TIM) detectors.Presence/intrusion sensors doubtless constitute the most useful type of sensor for this scenario. Ranging and Imaging (which can also be used as presence sensors) come next as extra means to increase situation awareness. CBRNE and TIM sensors will also be useful to equip robots, once they become available with the required degrees of miniaturization and effectiveness, which still present many issues and currently constitute active research topics.3 Ranging sensors can sometimes be used as presence sensors.4 Imaging sensors can sometimes be used as presence sensors.Intrusion and CBRNE/TIM detectors are the most suitable to operate as alarm triggers. Imaging, noise and ranging sensors present special requirements demanding them to be more capable than other sensors in terms of processing, communications and energy capabilities.The identified distribution patterns – which greatly rely on multicast and multiple reverse-multicast – and the fact that there is potentially more than one sink, point to a WSN topology that consists of the overlay of several trees, where each sink node forms the root of one element tree.The density of sensor deployment, network longevity, the nature and size of the area to be covered, constitute important factors that define the required number of nodes and the selected communication technology. Most often, these factors result into multi-tier heterogeneous network solutions, integrating different wireless technologies, each with its own advantages and constraints in terms of energy consumption, range, data rate, latency, etc. Common examples are IEEE 802.15.4 [3] and IEEE 802.11 [4] (see Figure 2). Presence/intrusion and CBRNE/TIM sensors provide the worst case, asking for greater number and density of sensors and a short-range low-power communications technology such as IEEE 802.15.4 (although for reasons of performance and hop number reduction, a heterogeneous solution might be preferred). Due to their bandwidth requirements, ranging, noise and imaging sensors should be directly connected to high bandwidth backbone networks (e.g., IEEE 802.11).Figure 2: Two-tier MOUT WSN.3 Networking Issues and ChallengesWithin IST FP6 UbiSeq&Sens, “Networking” refers to the functions traditionally expected from the Transport (e.g., Delivery Reliability, Quality of Service and Congestion Control), Network (e.g., Topology Control, Routing) and Data Link (e.g., Medium Access Control – MAC –, link reliability) layers. Energy-efficiency is another aspect that spans all layers of the WSN protocol stack. In fact, current WSN node constraints prevent the use of complex and demanding IP-based protocol architectures. Close-coupling between the traditional Transport, Network and Data Link layers is required instead. This section provides an overview of the main networking challenges and achievements of IST FP6 UbiSeq&Sens.3.1 Transport LayerIn order to characterise QoS requirements, several data flows were identified and characterised according to the following factors (see Table 1):•Source (sinks or sensors);•Destination (sinks or sensors);•Traffic distribution pattern (Unicast, Multicast, Reverse-Multicast5, Broadcast, Geocast6); •Traffic generation pattern (on-demand, alarm driven, periodic);•Delay sensitivity: High (less than 5 s) or Low (less than a few tens of seconds);Table 1: Data flows identified for the MOUT WSN.Source Destination TrafficDistributionTrafficGenerationDelaySensitivityIntrusion report Sensor Sink Ucast/RMcast all High CBRNE report Sensor Sink Ucast/RMcast all High Ranging report Sensor Sink Ucast/RMcast all HighImaging / Noisereport Sensor Sink Ucast/RMcast all LowWSN status Sensor Sink Ucast / RMcast On-demand AverageData request Sink Sensor Ucast / Mcast /Bcast / GcastOn-demand HighConfiguration command Sink Sensor Ucast / Mcast /Bcast / GcastOn-demand LowSome data flows require guaranteed delivery. However, not all data requires full reliability, a fact that can be exploited to increase transport efficiency. For example in alarm triggering sensors, abrupt measurement change reports require full reliability. Where measurements are stable within well-defined bounds, periodic reporting can tolerate some loss, which means that these reports can be sent with partial (< 100%) reliability. The transport mechanisms must be flexible enough to adapt to the best trade-off between reliability and efficiency. Table 2 shows the different reliability requirements envisaged for each type of data flow, taking into account the following factors:•Reliability grade (none, partial, total): Partial reliability requires a lower amount of resources.•Reliability mode: Message-oriented (reliability looks at individual messages) or timeliness-oriented (most recent messages replace older ones in a flow). Timeliness-oriented reliability requires a lower amount of resources.5 Also designated Convergecast.6 Geocast is a form of location-based broadcast in which a packet is broadcast to every node within a defined geographical area.Table 2: Reliability requirements.Reliability grade Reliability modeIntrusion report On-Demand / Alarm / End of Alarm: TotalPeriodic reporting within bounds: PartialOn-Demand / Alarm / End ofAlarm: Message-oriented Periodic reporting within bounds: Timeliness-orientedCBRNE report On-Demand / Alarm / End of Alarm: TotalPeriodic reporting within bounds: PartialOn-Demand / Alarm / End ofAlarm: Message-oriented Periodic reporting within bounds: Timeliness-orientedRanging report Total Timeliness-orientedImaging / Noisereport Total or Partial (depends on error resiliencemechanisms implemented by the codecs)Message-orientedWSN status Total Message–oriented Data request Total Message–orientedConfiguration command Total Message–orientedState-of-the-art reliable transport protocols like Pump Slowly, Fetch Quickly (PSFQ) [5] or Reliable Multi-Segment Transport (RMST) [6] are not designed to offer this degree of flexibility. On the other hand, Event-to-Sink Reliable Transport ERST) [7] supports partial reliability but presents efficiency issues that make it unpractical for utilization in real WSNs. This is the reason why the ongoing development of new reliable and efficient transport protocol is regarded as one of the main networking challenges in IST FP6 UbiSec&Sens. An initial specification of a Distributed Transport for Sensor Networks (DTSN) [8] was already produced, but performance evaluation is still ongoing. DTSN bears some resemblance to RMST and PSFQ regarding some basic mechanisms like caching in relay nodes and selective repeat Automatic Repeat Request (ARQ), but includes new functionalities and optimization that confer more flexibility and efficiency. An example of these mechanisms is the support of partial reliability through probabilistic memorization at the source, defined by different classes of service. Performance results have shown that a significant throughput gain can be achieved with partial reliability relative to full reliability (see Figure 3), a feature that can be exploited by some types of flows.Figure 3: Throughput achieved by a service class with 50% of memorization probability at the source,versus 100% reliability.DTSN is also designed to be closely-coupled with routing, although its only requirement is the support of individual node addressing by the routing protocol (see below). An implementation of the DTSN total reliability service in TinyOS 1.1 is already available. The DTSN implementation is now being extended and ported to TinyOS 2.0.3.2 Routing and In-Network ProcessingAn important performance requirement is to minimize the probability of false alarms in intrusion and CBRNE detection (the alarm-triggering sensors). Sensor redundancy can accomplish this, allowing the system to look at the results of the aggregation/fusion of measurements reported by individual sensors covering the same vicinity. An option is to perform aggregation/fusion at the CPs or warfighter terminals, provided that measurement reports from all relevant individual sensors are delivered by the WSN. Another option is to exploit sensor redundancy in a way that also increases network efficiency. This is possible if data aggregation/fusion is performed inside the WSN. In-network processing leads to traffic reduction because only the results of the aggregation/fusion are delivered to the sink nodes instead of individual sensor measurements. When the fused sensor vicinity is large, it is mandatory that sensing sensor nodes are undoubtedly identified with respect to their location, or at least to identify a well defined area from where fused data stem. Election of aggregation/fusion nodes is another issue that must be addressed by the Routing layer.Some operations are performed over specific sensor nodes (e.g. on-demand imaging requests) and some sensor data must bear node-specific positioning information. For other sensor data types – even if geographically referenced – only the result from the fusion/aggregation of data from several nodes is required (e.g. intrusion or CBRNE detection in an area covered by several sensors). When sensor nodes must be individually addressed, a pure data-centric routing architecture such as Directed Diffusion [9] is not enough.A hybrid node-centric / data-centric solution is then necessary. Another important research challenge consists of assuring low-latency energy-efficient routing to/from mobile sinks/sources, which also requires low-level support at the MAC layer. This issues are currently under investigation in IST FP6 UbiSec&Sens.3.3 Medium Access ControlThe low delays required by intrusion and CBRNE alert reports cannot be achieved by the Transport layer only. The maximization of WSN longevity through low duty cycles will likely compromise established delay bounds, even if the Transport and Routing protocols behave optimally. In order to achieve an acceptable trade-off of the low duty-cycle and low delay bound requirements, a new MAC protocol is required, sincestate-of-the-art solutions do not address this issue. This is an area where significant progress has already been made in IST FP6 UbiSec&Sens.A new MAC protocol was developed designated Tone-Propagated MAC (TP-MAC) [10]. In order to achieve low duty cycle, the proposed TP-MAC protocol inherits some important features from other MAC protocols, namely synchronized wake-up periods (S-MAC [11], T-MAC [12], SCP-MAC [13]), and synchronized wake-up-tone announcement of data availability associated with scheduled channel polling (SCP-MAC). However, in TP-MAC the wake-up-tones are propagated across the WSN so that the nodes in the path from source to destination are woken-up as quickly as possible, before the arrival of the heralded data packets. In this way, TP-MAC is able to achieve low delivery latency even if the WSN node duty-cycle is extremely low, preventing or at least ameliorating the early-sleeping problem.TP-MAC is based on the convergecast communication paradigm, assuming that the WSN is organized in a logical tree topology, associated with one sink, which corresponds to the root node. This again imposes some cross-layer constraints on the Network (i.e. Routing) layer, which is not a real limitation, since most typical WSN scenarios require convergecast of sensor data towards sink nodes. In fact, TP-MAC supports topologies with more than one sink node, though at the cost of some energy-efficiency. The detailed multi-sink support mechanism will not be explained in detail due to space limitations.In a tree-structure rooted at the sink node, it is possible to define different levels defined by the minimum hop distance relative to the sink node. In this way, the sink node constitutes level 0 and the level number increases as hop distance to the sink node increases. The establishment of network levels is at the core of the wake-up-tone propagation mechanism.TP-MAC establishes super-frame periods for channel access, each starting by a synchronization wake-up-tone and two wake-up-tone propagation windows (upstream and downstream), followed by a data transmission window (see Figure 4). The size of the tone propagation window can be different for upstream and downstream, depending on the latency requirements. The channel access method in the transmission window can be based on any MAC protocol, e.g. plain CSMA/CA, S-MAC, T-MAC, SCP-MAC, etc.The synchronization tone marks the beginning of the super-frame structure. This tone is periodically activated by the sink node and slowly propagated downstream to announce the transmission of a broadcast synchronizing/re-synchronizing SYNC packet in the data transmission window. The details of synchronization establishment/maintenance will also not be explained in this paper due to space limitations. The wake-up-tone propagation windows allow the announcement of data and establishment of fast paths from source to destination.When no data traffic is generated, each node only has to poll the channel once in each wake-up-tone propagation window (only in the slot that corresponds to its level), and sometimes also in the synchronization slot. The nodes are allowed to sleep during the rest of the super-frame.When a node has data to transmit, it first sends a wake-up upstream tone (e.g., for sensing data destined to the sink node), or a waking downstream tone (e.g., for control messages issued by the sink node to sensor nodes). The wake-up-tone propagation window structure guarantees that nearby nodes in the next upper/lower level listen to the generated wake-up-tone. They then propagate the tone upstream/downstream, as it can be seen in the tone propagation windows of Fig. 1. If a node detects a wake-up-tone in the last slot of a propagation window, then it shall only propagate it in the next super-frame. The tone propagation mechanism, which resembles the data propagation mechanism of D-MAC [14], assures that nodes within some hop distance are woken-up in just one operation cycle, forming a fast-path before actual data arrives. The maximum distance that a wake-up tone can reach in a single super-frame is equal to the number of tones in each tone propagation window, which is a configuration parameter.The nodes that form a fast path stay active in the data transmission window, for a pre-defined time interval, which is dimensioned to keep those nodes active until the announced data arrives. The timeout mechanism is similar to that defined in T-MAC.TP-MAC nodes only poll the media for a number slightly above two times per cycle (two polls, respectively for upstream and downstream propagated tones in each super-frame, and more seldom for the synchronization/re-synchronization tone), propagating the wake-up tones fast and deeply through the network (and thus opening fast data transmission paths). In this way it is possible to achieve low latencies simultaneously with low duty cycles.Tone listening slot Tone transmission slot Frame transmission (includes contention period) S1 S2 Sn Synchronization toneFigure 4: TP-MAC super-frame structure and wake-up-tone propagation.An analytical model was developed to compare TP-MAC with SCP-MAC, under the assumption that SCP-MAC is used by TP-MAC for data transmission. This model addresses the relationship between duty-cycle during periods without traffic, and the minimum latency that can be achieved once the first packet of an active stream is generated.Figure 5 shows the ratio between the duty cycles of TP-MAC and SCP-MAC as a percentage, for different numbers of hops, and different sizes of the wake-up-tone propagation window. Other TP-MAC parameters are the following: number of transmission slots: 10; synchronization tone period: 5 cycles. It is worth to note that TP-MAC duty cycle decreases with increasing number of hops, but its energy efficiency gain with respect to SCP-MAC stabilizes for high numbers of hops. It is also shown that higher number of tones can give higher energy efficiency gain. For instance, for 10 tones, we can obtain a duty cycle as low as 22% of the SCP-MAC duty cycle, for large network sizes.Figure 5: Ratio between the duty cycles of TP-MAC and SCP-MAC as a function of the number of hopsand the size of the wake-up-tone propagation window.The development of TP-MAC is still not fully completed. Among the remaining issues is the support of efficient mechanisms to deal with sink/source mobility. The INESC team is also developing another MAC protocol, this time based on TDMA principles for implementation simplicity, but incorporating some features of TP-MAC.4 Security Issues and ChallengesInformation and network assurance are vital to the successful conduct of Network Centric Operations. At the WSN level, these requirements are reflected as protection against physical attacks against the network equipment (WSN nodes) or logical attacks against WSN communications. This section will focus on the main issues and technical challenges that these possible attacks entail in a MOUT WSN scenario.4.1 Physical AttacksConsidering the physical attacks it is desired that the capture of one or more nodes of the WSN do not compromise the security of the whole system [15]. Mechanisms must be in place to minimize the probabilities of successful physical tampering of captured WSN nodes on the part of the attacker. Physical analysis of one or more captured WSN nodes by the attacker would expose essential security information such as encryption keys, allowing the attacker to enter and expand its control of the MOUT WSN. This could then be used to passively exploit MOUT WSN sensing data to his own benefit, or otherwise to cause ruptures in MOUT WSN operation.The tampering protection mechanisms implemented in each WSN node must take into account the possible use by the attacker of sophisticated procedures in order to analyse the WSN node. For example, the WSN nodes should have the capability to detect these physical attacks and should self-destruct upon detection of a physical attack. The kind of mechanisms used to detect a physical attack can range from a simple “open box sensor”, or an “acceleration sensor”, or even a “GPS movement sensor”, installed inside the tamper resistant box that contains the WSN node. More sophisticated mechanisms considered for implementation include the detection of environmental actions taken by attackers, namely temperature, clock frequency or voltage decrease/increase beyond the operating range of WSN nodes, so that the sensing and/or communications behaviour of the node become compromised.4.2 Logical AttacksLogical attacks are of more concern than physical attacks because they are not so easily detected. Taking a global view on the logical attacks we can classify them into passive and active attacks [16]. Following is a description of these two kinds of attacks, clearly identifying their target Security Services in the MOUT WSN.Passive attacks are those that simply gather and process the information exchanged between the WSN nodes, and are here designated Passive Man-in-the-Middle attacks. These attacks will likely be targeted at the Confidentiality Security Service of the WSN. To counter this kind of attack a One Time Pad encryption system must be used for all the messages in the WSN, which means that every message in the WSN will be encrypted using a different key. Due to the particularities of WSN communications, and looking at the MOUT WSN in particular, which is mostly alarm-oriented with little traffic being exchanged during normal operation, the simple analysis of network activity may indicate that an alarm condition was triggered. Even if there is network activity in the absence of alarm conditions (e.g. periodic exchange of control messages), since the messages are generally short and some message types may be periodically repeated or at least present very similar content or format, the attacker may be able to identify unusual traffic patterns as indicators of alarm triggering. This is a characteristic that makes WSNs very susceptible to a special kind of Passive Attack that simply relies on the analysis of the traffic pattern of the WSN nodes. In order to counter this, the WSN nodes should send some special messages with the purpose of confusing an attacker performing Traffic Analysis. In addition to this and assuming the use of a different key for each single message, INESC is currently developing a system in which the content of the message is itself reorganized in a way that even for equal messages, the encrypted payload is always different. As result, cryptanalysis of the captured messages becomes twice difficult because even for equal messages encrypted used different keys, the content itself is modified in a unique manner using a different mechanism for each message. We are currently disregarding attacks directed at the system’s encryption key as the latter will never be repeated between different messages. Active attacks, which are here designated Active Man-in-the-Middle attacks, can assume several forms that can be grouped in three main classes:•Forgery of a message to be inserted into the WSN;。