Fundamentals of Wireless SensorNetworks_chapter5_solut
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。
WSNs中优化移动信宿路径的数据收集算法
第19卷 第2期 太赫兹科学与电子信息学报Vo1．19，No．2 2021年4月 Journal of Terahertz Science and Electronic Information Technology Apr．，2021文章编号：2095-4980(2021)02-0224-05WSNs中优化移动信宿路径的数据收集算法吕 虹(贵州广播电视大学(贵州职业技术学院)信息工程学院，贵州贵阳 550001)摘要：收集数据是部署无线传感网络(WSNs)的根本目的。
采用移动信宿策略可有效缓解 WSNs的能耗问题，信宿的移动路径是该策略的关键。
为此，提出基于伪驻留点的数据收集(VRDC)算法。
VRDC算法先依据驻留点规划信宿路径，再依据路径选择伪驻留点(VRPs)。
VRPs可通过一跳直接向移动信宿传输数据，而其他的节点则将数据传输至最近的VRPs，进而减少传输跳数，降低能耗。
仿真结果表明，提出的VRDC算法能有效降低能耗，并平衡节点间的能耗。
关键词：无线传感网络；数据收集；路径规划；伪驻留点；能耗中图分类号：TN926；TP393 文献标志码：A doi：10.11805/TKYDA2019493Data collection algorithm for optimizing mobile accommodation path in WSNsLYU Hong(Electronic and Computer Engineering，Guizhou Radio & TV University，Guiyang Guizhou 550001，China)Abstract：Data collection is the fundamental purpose of deploying Wireless Sensor Networks(WSNs).The adoption of mobile accommodation strategy can effectively alleviate the problem of energyconsumption of WSNs. The mobile path is the key of the strategy. Therefore, Virtual Rendezvous points-based Data Collecting(VRDC) algorithm is proposed in this paper. The VRDC algorithm first planslodging paths based on the host Points, and then selects Virtual Rendezvous Points(VRPs) based on thehost Points. VRPs can transmit data directly to the mobile address via one hop, while other nodes cantransmit data to the nearest VRPs, thereby reducing the number of transmission hops and energyconsumption. Simulation results show that VRDC algorithm can effectively reduce energy consumption andbalance energy consumption among nodes.Keywords：Wireless Sensor Networks；data collection；path planning；Virtual Rendezvous Points；energy consumption无线传感网络(WSNs)[1]已在多个应用范围内使用，如军事监测、环境勘察、智慧农业、智能家居、康复医疗等。
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 TheoryABSTRACThe 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.ey-words: Wireless sensor network; Complex network; Scale-free networkI. INTRODUCTIONn 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].omplex 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 RESEARCHailin 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).HEN 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.EI 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 WSNecause 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].n 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, whoseenergy 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 ALGORITHMhe 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.bove all, the probability that the existing node i will be connected with the newly generatednode v can be shown as follows:n 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.iN j 1ak Kjπ=≈∑ 0N=m 1t +- (2)ith The varying rate with time of ki, we get:0m 112i i i i t jj k amk amk m t mt m k δπδ+-====-∑ (3)hen t→∞,ondition: k i (t i )=m, we get the solution: i 2,i t k t aββ=（t ）=m （）(4) he probability that the degree of node I is smaller than k is:(5)he 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 i t m t P P k ββββ=<=>=-∑ (6)1101(t m )m t k ββ-+ So we get: 110(k (t)k)21(k).i P m t P k m t kββδδ<==+ (7) When t →∞, we get:2(k)2m r P k -= (8) In which 12=1+=1+a γβ, 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 time parameter 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 communication radius 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 into Eqs. and eventually we get: 22S 21122(k)2m 2e a P km k π----==（1-P ）d .V. SIMULATIONhis paper used Java GUI mode of BRITE topology generator to generate the topology, and parameter settings were as follows:) N=5000means the quantity of the sensor nodes at the end of theopology generation.) m=m0 =1means the quantity of the new generated edges by the new generated node at each time interval.) HS=500S means the given region was divided into HS*HS big squares.) .LS=50 LS means each big square was divided into LS*LS small squares.d=10) minis the mininum restriction on communication radius of each sensor node.d=128) maxis the maxinum restriction on communication radius of each sensor node.) PC=1C means wether preferential connectivity or not.) .IG=1G means wether incremental grouth or not.) e P=0.01, m=1his 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 .hen 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.ig. 1 Degree distribution of Improved Modelompared 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 Model VI. CONCLUSIONhis 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.n 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。
Chapter8-Power Management
Fundamentals of Wireless Sensor Networks: Theory and Practice Waltenegus Dargie and Christian Poellabauer © 2010
9
Power Management
It has been described in the literature as a linear optimisation problem
the objective function is the expected performance
related to the expected waiting time and the number of jobs in the queue
the constraint is the expected power consumption
Battery
DC – DC Converter
Dynamic Power Management
Dynamic Operation Modes
Transition Costs
Dynamic Scaling Task Scheduling
Conceptual Architecture
Chapter 8: Power Management
Outline
Local Power Management Aspects
局部能量管理 处理器子系统分
Processor Subsystem
Communication Subsystem
Bus Frequency and RAM Timing Active Memory Power Subsystem
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。
物联网工程专业人才培养方案
数学计算机科学学院物联网工程专业人才培养方案（应用类）一、业务培养目标坚持“系统设计、分类指导、强化实践、突出能力”的原则，培养能系统地掌握物联网相关理论、方法和技能，具备专业覆盖通信技术、网络技术、传感技术等信息领域、知识宽广的高级工程技术人才，能够在物联网相关的企业、行业，从事物联网的通信架构、网络协议和标准、无线传感器、信息安全等产品及系统的科学研究、工程设计、产品开发、技术管理与设备维护等专业技术和管理工作，并为高等学校输送优质研究生。
二、业务培养要求1、努力学习马列主义、毛泽东思想、邓小平理论、“三个代表” 的重要思想和科学发展观，逐步树立为国家富强和民族昌盛而奋斗的责任感，具有良好的道德品质和情操，遵纪守法，立志为社会主义建设服务。
2、掌握本专业所必须的基本理论、基本知识、基本技能与方法；具备从事物联网相关工作的分析、规划、设计、开发、运营、管理的工程技术知识；通过深入物联网行业中的企业工程实践，了解该领域规划设计、运营管理控制的新技术和需求；具有综合运用多学科知识、技术和现代工程工具，分析解决物联网领域实际问题的能力；了解与物联网相关的法规；了解物联网工程领域的发展动态；掌握文献检索、资料查询的基本方法，具有获取信息的能力；熟练掌握一门外国语。
3、具有合理的知识结构和能力结构，对新事物具有敏感性和适应性；对已有知识具有综合应用能力和创新能力；具有独立分析问题、解决问题的能力以及自我拓展获取新知识的能力；具有合作共事、协同工作的能力和竞争能力；具有良好的社会道德和职业道德。
4、达到国家规定的大学生体锻标准，养成良好的体育锻炼和卫生习惯，身心健康。
三、学制与毕业学分学制：本专业标准学制为4年，实行弹性学制为3-6年。
学分：总学分不低于174+（6）学分。
四、授予学位授予工学学士学位。
五、课程设置与教学进程总体安排（一）物联网工程专业教学活动时间安排表3、专业课程（4）注：（1）根据各专业类别自行确定各部分课程的学时（学分）、授课时间、考核方式等内容。
Wireless Sensor Networks and Applications
Wireless Sensor Networks andApplicationsWireless Sensor Networks (WSNs) have gained significant attention in recent years due to their potential to revolutionize various industries and applications. These networks consist of small, low-cost sensor nodes that are wirelessly connected to collect and transmit data from the environment. The applications of WSNs are diverse, ranging from environmental monitoring, healthcare, smart homes, industrial automation, agriculture, and more. However, despite their promising potential, WSNs also face several challenges and limitations that need to be addressed for their widespread adoption and success. One of the primarychallenges of WSNs is their limited power supply. 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 constraint on the deployment and maintenance of WSNs, especially in remote or inaccessible areas. Researchers and engineers are actively working on developing energy-efficient protocols, algorithms, and hardware designs to prolong the battery life of sensor nodes and enable self-sustainability through energy harvesting techniques such as solar, kinetic, or thermal energy. Another critical issue facing WSNs is their vulnerability to security threats and attacks. Since WSNs are often deployed in unattended or hostile environments, they are susceptible to various security risks, including eavesdropping, data tampering, node impersonation, and denial-of-service attacks. Ensuring the confidentiality, integrity, and availability of data in WSNs is a complex and ongoing research area, requiring the development of robust encryption, authentication, key management, and intrusion detection mechanisms to protect against malicious activities and safeguard sensitive information. Furthermore, the scalability and reliability of WSNs are significant concerns, particularly as the number of deployed sensor nodes increases. As WSNs grow insize and complexity, it becomes challenging to maintain efficient communication, data aggregation, and network management. The dynamic nature of wireless communication, environmental interference, and node failures can lead to packet loss, latency, and network congestion, affecting the overall performance andreliability of WSNs. Addressing these scalability and reliability issues requires the design of adaptive routing protocols, fault-tolerant mechanisms, and quality-of-service optimizations to ensure seamless and dependable operation in diverse WSN applications. In addition to technical challenges, the real-world deployment and commercialization of WSNs also face economic, regulatory, and societal barriers. The high initial deployment costs, interoperability with existing infrastructure, compliance with industry standards, and privacy concerns are all factors that impact the widespread adoption and acceptance of WSNs in various domains. Moreover, the ethical implications of collecting and analyzing large volumes of data from WSNs, such as personal health information or environmental surveillance, raise important questions about consent, transparency, and accountability in the use of sensor-generated data. Despite these challenges, the potential benefits of WSNs in enabling smart, connected, and sustainable systems are driving continued research, innovation, and investment in this field. The development of advanced sensor technologies, wireless communication protocols, data analytics, and edge computing capabilities is unlocking new opportunities for WSNs to enhance efficiency, productivity, and quality of life in diverse applications. By addressing the technical, operational, and ethical challenges, WSNs can realize their full potential as a foundational infrastructure for the Internet of Things (IoT) and contribute to a more interconnected and intelligent world.。
华北电力大学智能电网信息工程专业人才培养方案
Title of the Major:Smart Grid Information Engineering Code: 080645S
一、学制与学位Length of Schooling and Degree
学制：四年Duration:Four years
学位:工学学士Degree:Bachelor of Engineering
1.Subject Foundation Courses: Advanced Mathematics，College Physics, Advanced Language programming(C)，Fundamentals of Information Technology, Linear Algebra, Complex Function and Integral Transformation，Probability and Mathematical Statistics B
总周数分配
Arrangement of the Total Weeks
学期Semester
教学环节Teaching Program
一
二
三
四
五
六
七
八
合计
理论教学Theoretic Teaching
16
16
17
16
16
16
16
0
113
复习考试Review and Exam
1
2
2
2
1
2
2
0
12
集中进行的实践环节Intensive Practical Training
College Physics (1）
无线传感器网络中的分布式随机感知理论研究
无线传感器网络中的分布式随机感知理论研究随着科技的不断发展，无线传感器网络（Wireless Sensor Network，WSN）作为一种新兴的网络通信技术也被广泛应用于多个领域中，如环境监测、智能交通、医疗保健等。
在无线传感器网络中，分布式随机感知（Distributed Random Sensing，DRS）技术的应用及研究已成为当前热点领域。
一、Distributed Random Sensing技术概述Distributed Random Sensing技术是一种利用多个分布式传感器节点随机感知环境中的信息，并将采集的信息进行整合、分析和传输的技术。
该技术利用了多个节点的协同作用，实现了大规模环境信息的感知及处理，从而能够提高网络的性能和可靠性。
DRS技术相对于其他传统的感知技术，具有以下优点：（1）能够充分利用网络中传感器节点的分布式特性，减少了单个节点对网络的影响，提高了网络的鲁棒性。
（2）DRS技术采用随机化的方法，保证了网络节点的均衡负载，减少了感知节点之间的冲突和重复。
（3）DRS技术对于节点失效和阻塞情况具有强大的容错能力，能够保证网络的长期稳定运行。
二、Distributed Random Sensing算法研究当前，DRS算法的研究重点主要集中在两个方面：一是感知信息的采集，包括节点选择和感知范围的确定；二是数据处理和传输，包括节点数据的处理和整合、协议设计等。
（1）节点选择和感知范围的确定传感器节点选择是一个非常重要的问题。
在DRS技术中，节点选择旨在确定哪些节点将参与到感知过程中。
当前研究主要关注以下两种节点选择算法：①基于覆盖的节点选择。
该算法是根据节点感知范围对节点进行选择的。
选择的节点能够监控所选择的区域，以提高网络感知的效率和精度。
②基于均衡负载的节点选择。
该算法是根据节点当前负载和饱和度来进行节点选择的。
选择的节点应该能够满足所指定的感知负载条件，以保证网络感知过程平衡和均衡。
无线传感器网络论文英文版
无线传感器网络论文英文版Wireless Sensor Networks: A Research PaperAbstract:Wireless Sensor Networks (WSNs) have emerged as a revolutionary technology in the field of wireless communication. This research paper aims to provide an overview of WSNs, their applications, challenges, and future prospects.1. Introduction:Wireless Sensor Networks are interconnected nodes that can communicate with each other through wireless protocols. These nodes, equipped with sensors, provide real-time data from physical environments. WSNs have gained significant attention due to their applicability in various industries such as healthcare, agriculture, environmental monitoring, and surveillance.2. Architecture of Wireless Sensor Networks:The architecture of WSNs consists of three main components: sensor nodes, sinks or base stations, and a network infrastructure. Sensor nodes gather information from the environment and transmit it to the sink or base station via multi-hopping or direct transmission. The network infrastructure manages the routing and data aggregation processes.3. Applications of Wireless Sensor Networks:3.1 Environmental Monitoring:WSNs play a crucial role in monitoring environmental parameters such as temperature, humidity, air quality, and water quality. This data is essential for environmental research, disaster management, and habitat monitoring.3.2 Healthcare:WSNs have revolutionized the healthcare industry by enabling remote patient monitoring, fall detection, and medication adherence. These networks assist in providing personalized and timely healthcare services.3.3 Agriculture:In the agricultural sector, WSNs are deployed for crop monitoring, irrigation management, and pest control. The data collected by these networks help farmers enhance crop productivity and reduce resource wastage.3.4 Surveillance:WSNs are extensively employed in surveillance systems to monitor public areas, monitor traffic congestion, and ensure public safety. These networks provide real-time data for efficient decision-making and threat detection.4. Challenges in Wireless Sensor Networks:4.1 Energy Efficiency:Sensor nodes in WSNs are usually battery-powered, making energy efficiency a critical challenge. Researchers are focused on developing energy-efficient protocols and algorithms to prolong the network's lifespan.4.2 Security and Privacy:As WSNs collect sensitive data, ensuring the security and privacy of transmitted information is crucial. Encryption techniques, intrusion detection systems, and secure routing protocols are being developed to address these concerns.4.3 Scalability:Scalability is a critical challenge in large-scale deployment of WSNs. Designing scalable architectures and protocols enable efficient communication and management of a large number of sensor nodes.5. Future Prospects of Wireless Sensor Networks:The future of WSNs is promising, with advancements in technologies such as Internet of Things (IoT) and Artificial Intelligence (AI). Integration of WSNs with IoT devices will enable seamless communication and data exchange. AI algorithms can facilitate intelligent data analysis and decision-making.Conclusion:Wireless Sensor Networks have shown tremendous potential in various fields and continue to evolve with advancements in technology. Addressing energy efficiency, security, and scalability challenges will contribute to the widespread adoption of WSNs. As researchers continue to explore new possibilities, WSNs will become an integral part of our daily lives, transforming industries and enhancing our quality of life.。
无线传感器网络中的群智感知技术研究
无线传感器网络中的群智感知技术研究无线传感器网络（Wireless Sensor Networks, WSN）是一个由大量分布式、具有自主性、能够感知环境变化并相互通信的无线传感器节点构成的网络。
近年来，随着物联网技术的发展，无线传感器网络在环境监测、安防监控、医疗保健等领域得到了广泛应用。
而群智感知技术（Crowd Sensing）作为无线传感器网络的一种应用，极大地拓展了无线传感器网络的使用场景和功能。
群智感知技术是指利用群体智慧来完成数据采集、处理和分析的技术。
它可以更加精准地获取人类无法直接感知、无法被其他传感器网络检测到的数据。
同时，它可以通过无线传感器网络，将这些数据实时地传输到数据中心，让决策者及时地获取远程数据，做出判断和决策。
在无线传感器网络中，群智感知技术的应用可以分为三个环节：数据采集、数据处理和数据应用。
在数据采集方面，群智感知技术利用大量的无线传感器节点来收集环境数据。
这些节点可以分布在不同的区域，通过自主协调完成数据采集任务。
例如，一次车祸发生后，城市中可以通过无线传感器网络采集到在车祸周围行走的行人们的体温、心率、呼吸等数据信息，以及大气中的轻重烃等污染物浓度。
这些数据信息可以形成一个群智感知的数据集，为车祸现场的处理提供参考。
在数据处理方面，群智感知技术需要将采集到的数据信息进行大规模的数据分析。
这些数据可以从多个角度进行分析，例如时间序列分析、空间分析、以及时空分析等。
通过分析这些数据，可以揭示数据之间的相互关系，发现数据的潜在规律，进而辅助决策者做出相应的决策。
在数据应用方面，群智感知技术可以应用于多个领域，例如道路交通、城市管理、环境保护等。
在道路交通方面，通过无线传感器网络采集到的数据信息可以被用来优化城市交通流量和道路运行状况管理。
在城市管理方面，无线传感器网络可以采集城市建筑物、河流、水源、模型等数据。
在环境保护方面，无线传感器可以分布在烟囱或者化工厂附近进行环境监测，帮助环境保护机构提前预警环境问题。