無(wú)線傳感器網(wǎng)絡(luò)模型設(shè)計(jì)_英文文獻(xiàn)翻譯.doc

1、Model Design of Wireless Sensor Network based on Scale-Free Network TheoryABSTRACTThe key issue of researches on wireless sensor networks is to balance the energy costs across the whole network and to enhance the robustness in order to extend the survival time of the whole sensor network. As a speci

2、al 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

3、 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

4、. 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 bala

5、nced and reasonable. This improved model which is based on the complex network theory proves more applicable to the research of wireless sensor network. Key-words: Wireless sensor network; Complex network; Scale-free network I. INTRODUCTION In recent years, wireless sensor networks have attracted mo

6、re 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 sen

7、sor network research is to balance the energy consumption of all sensor nodes and to minimize the impact of random failure of sensor nodes or random attacks to sensor nodes on the entire network 1. Complex network theory has been for some time since first proposed by Barabasi and Albert in 1998, but

8、 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 comple

9、x 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 senso

10、r 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

11、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. PR

12、OGRESS OF RELATED RESEARCH Hailin 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

13、the networks reliance against random failure of the sensor nodes. In the second model named Energy-balanced evolution model (EBEM), the maximum number of links for each node is introduced into the algorithm, which can make energy consumption more balanced than the previous model (EAEM). CHEN Lijun a

14、nd 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

15、nodes deployed randomly. They proved their proposed scheme can decrease working nodes, guarantee network topology sparseness, predigest routing complexity and prolong network survival period. LEI Ming and LI Deshi have proposed a research on self-organization reliability of wireless sensor network5,

16、 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 selforganized topological models of WSN and proposes a refined routing algorithm based on WSN. III. IMPROVED S

17、CALE-FREE MODEL FOR WSN Because 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 re

18、striction 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

19、 sensor node 6. In response to these points, based on the traditional scale-free model, this paper has made the following improvements in the process of model establishment: (1) A large number of researches have shown that many complex networks in nature are not only the result from internal forces,

20、 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, wh

21、en 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

22、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 rest

23、riction on communication radius of sensor nodes has been proposed in the improved model. (3) In sensor network, if there exists a sensor node with a seriously high degree, whose energy consumption is very quickly, it will be seriously bad. The whole sensor network would surely collapse if enough ene

24、rgy 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 o

25、f 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 ALGORITHM The specific algorithm of the improved model formation are described as follows : (1) A given region (assumed to be s

26、quare) 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 t

27、o 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 vconn

28、ects 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 se

29、nsor nodes. Above all, the probability that the existing node i will be connected with the newly generated node v can be shown as follows: In order to compute it conveniently, here assumed that few nodes had reached the degree of saturation value kimax . That is, N is very minimal in Eqs.(1) so that

30、 it can be ignored here. And in Eqs. (2)With The varying rate with time of ki, we get: (3)When t, condition: ki (ti)=m, we get the solution: (4)The probability that the degree of node I is smaller than k is: (5)The time interval when each newly generated node connected into the network is equal, so

31、that probability density of ti is a constant parameter: 1/ we replace it into Eqs. (5), then we get:(6) So we get: (7)When t , we get: (8) In which , and the degree distribution we get and the degree distribution of traditional scale-free network are similar. Approximately, it has nothing to do with

32、 the time parameter t and the quantity of edges m generated at each time interval. 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 is = Then we replace= andinto Eqs. and eventually we get: .V. SIMULA

33、TION This paper used Java GUI mode of BRITE topology generator to generate the topology, and parameter settings were as follows: 1) N=5000N means the quantity of the sensor nodes at the end of the topology generation. 2) m=m0 =1M means the quantity of the new generated edges by the new generated nod

34、e at each time interval. 3) HS=500 HS means the given region was divided into HS*HS big squares. 4) .LS=50 LS means each big square was divided into LS*LS small squares. 5) =10is the mininum restriction on communication radius of each sensor node. 6) =128 is the maxinum restriction on communication

35、radius of each sensor node. 7) PC=1 PC means wether preferential connectivity or not. 8) .IG=1 IG means wether incremental grouth or not. 9) =0.01, m=1This means that any newly generated node has 1% chance to be node failure and the newly generated node if normal only connect with one existing node

36、. Then we got each degree of the sensor network nodes from BRITE topology generator. To analyze the degree distribution, we use Matlab to calculate datas and draw graph. As can easily be seen from Fig. 1, the distribution of degree k subjected approximately to Power-Law distribution. However, the va

37、lue of is no longer between 2 and 3, but a very large value, which is caused by the random damage probability Pe to new generated nodes when deployed and the max in um of communication radius dmax of each sensor node. It can be easily seen that the slope of P(k) is very steep and P(k) rears up becau

38、se sensor node has a limited degree of saturation value by 180. The existence of 0 degree nodes is result from the random damage to new generated nodes when deployed. Fig. 1 Degree distribution of Improved ModelCompared with the degree distribution produced by traditional scale-free network as is sh

39、own 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 hig

40、her degree than the quantity of 180 while there are some nodes whose degree are of higher degree than the quantity of 180. Fig. 2 Degree distribution of traditional Scale-free ModelVI. CONCLUSION This paper has added a random damage to new generated nodes when deployed; considering multi-hop transmi

41、ssion 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 radiu

42、s of each sensor node to the improved model; to balance the energy comsuption of the entire network, this paper has proposed a limited degree of saturation value on each sensor node. In this paper, an improved scale-free network model was proposed to introduce the theory of traditional scale-free ne

43、twork 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. REFERENCES1 R. Albert, H. Jeong and A.-L. Barabasi. Error and attack tolerance of complex networks. Nature, 2000; 406: 378

44、-382. 2 Albert R, Barabasi A. Statistical mechanics of complex networks. Rev Mod Phys 2002; 74: 4797. 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

45、 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;

46、 556: 30037. 7 Peng J, Li Z. An Improved Evolution Model of Scale-Free Network . Computer application. 2008 , 2; 1: 1-4. 基于無(wú)范圍網(wǎng)絡(luò)理論的無(wú)線傳感器網(wǎng)絡(luò)模型設(shè)計(jì)張戌源通信工程部通信與信息工程學(xué)院上海，中國(guó)摘要無(wú)線傳感器網(wǎng)絡(luò)的研究的關(guān)鍵問(wèn)題是是平衡整個(gè)網(wǎng)絡(luò)中的能源成本并且為了延長(zhǎng)整個(gè)傳感器網(wǎng)絡(luò)的生存時(shí)間要增強(qiáng)魯棒性。作為一個(gè)特殊的復(fù)雜網(wǎng)絡(luò)特別是由于環(huán)境的限制，傳感器網(wǎng)絡(luò)很不同于傳統(tǒng)的復(fù)雜的網(wǎng)絡(luò)，例如Internet網(wǎng)絡(luò)，生態(tài)網(wǎng)絡(luò)，社交網(wǎng)絡(luò)等。這就有必要來(lái)介紹一種通過(guò)復(fù)雜網(wǎng)絡(luò)

47、理論和分析方法如何研究無(wú)線傳感器網(wǎng)絡(luò)的方式，其中的關(guān)鍵在于能夠作出一個(gè)成功的建模，它能夠使復(fù)雜網(wǎng)絡(luò)理論和分析方法更適合無(wú)線傳感器網(wǎng)絡(luò)的應(yīng)用，以達(dá)到根據(jù)某些特定無(wú)線傳感器網(wǎng)絡(luò)的特點(diǎn)進(jìn)行優(yōu)化?；趥鹘y(tǒng)的無(wú)尺度的網(wǎng)絡(luò)生成規(guī)則，本文對(duì)改進(jìn)后的模型增加了一些限制。仿真結(jié)果表明，在本文中進(jìn)行的改進(jìn)，使整個(gè)網(wǎng)絡(luò)對(duì)隨機(jī)故障有一個(gè)更好的魯棒性并且能源成本更平衡和合理。這種基于復(fù)雜網(wǎng)絡(luò)理論的改進(jìn)模型證明更適用于無(wú)線傳感器網(wǎng)絡(luò)的研究。關(guān)鍵詞：無(wú)線傳感器網(wǎng)絡(luò)，復(fù)雜網(wǎng)絡(luò)，無(wú)尺度網(wǎng)絡(luò)引言 近年來(lái)，由于無(wú)線傳感器網(wǎng)絡(luò)的優(yōu)點(diǎn)，吸引了越來(lái)越多的相關(guān)研究人員 。傳感器節(jié)點(diǎn)通常是低功率和非可再充電的。如果某些節(jié)點(diǎn)的能量消耗完，原來(lái)

48、網(wǎng)絡(luò)的完整性將被破壞并且其他 進(jìn)行數(shù)據(jù)傳輸?shù)墓?jié)點(diǎn)，會(huì)有更多的業(yè)務(wù)負(fù)擔(dān)。 傳感器網(wǎng)絡(luò)的研究的關(guān)鍵問(wèn)題是是平衡所有的傳感器節(jié)點(diǎn)能源消耗和把傳感器節(jié)點(diǎn)隨機(jī)錯(cuò)誤影響或者是對(duì)整個(gè)網(wǎng)絡(luò)的傳感器節(jié)點(diǎn)隨機(jī)攻擊降到最低。復(fù)雜網(wǎng)絡(luò)理論，自從Barabasi和Albert在1998年提出已經(jīng)有一段時(shí)間了，但應(yīng)用于無(wú)線傳感器網(wǎng)絡(luò)的復(fù)雜網(wǎng)絡(luò)理論和分析方法研究嚴(yán)重稀缺并且開(kāi)發(fā)進(jìn)展緩慢。作為一個(gè)尤其是環(huán)境的限制特殊的復(fù)雜的網(wǎng)絡(luò)，傳感器網(wǎng)絡(luò)是遠(yuǎn)遠(yuǎn)不同于傳統(tǒng)的復(fù)雜網(wǎng)絡(luò)并且現(xiàn)有的復(fù)雜網(wǎng)絡(luò)理論和分析法不能直接應(yīng)用到分析傳感器網(wǎng)絡(luò)上?；跓o(wú)標(biāo)度網(wǎng)絡(luò)理論（BA模型）2，（1）本文中當(dāng)部署在生成規(guī)則時(shí)添加到每個(gè)傳感器節(jié)點(diǎn)隨機(jī)損傷機(jī)制 （

49、2）考慮到無(wú)線傳感器網(wǎng)絡(luò)的實(shí)際聲明，傳感器通信半徑參數(shù)最大和最小限制 添加到每個(gè)傳感器節(jié)點(diǎn); （3）為了維持整個(gè)網(wǎng)絡(luò)平衡的的能源消耗率，本文每個(gè)傳感器節(jié)點(diǎn)加入了和度值的限度。這對(duì)無(wú)尺度模型改善不僅提到是以上的改善，而且還具有傳統(tǒng)的無(wú)標(biāo)度網(wǎng)絡(luò)大量的優(yōu)點(diǎn)，如很好的抵抗隨機(jī)的攻擊的能力，從而使現(xiàn)有的復(fù)雜網(wǎng)絡(luò)的理論和分析方法，將是更適合于無(wú)線傳感器網(wǎng)絡(luò)的研究。相關(guān)的研究進(jìn)展海林朱和香羅提出了兩種復(fù)雜的基于網(wǎng)絡(luò)的無(wú)線傳感器網(wǎng)絡(luò)模型3，第一個(gè)名叫能量感知的演化模型（EAEM）可以以高效節(jié)能的方式組織網(wǎng)絡(luò)，并能產(chǎn)生無(wú)尺度網(wǎng)絡(luò)，它可以提高網(wǎng)絡(luò)抵御傳感器節(jié)點(diǎn)隨機(jī)失誤的可靠性。第二個(gè)模型命名為能量均衡演化模型（E

50、BEM），為每個(gè)節(jié)點(diǎn)的最大鏈接數(shù)被引入算法，它可以使能源消費(fèi)比以前的型號(hào)更均衡（EAEM）。陳立軍和毛馳提出了拓?fù)錈o(wú)線傳感器網(wǎng)絡(luò)控制下平均程度約束條件4。在無(wú)線傳感器網(wǎng)絡(luò)拓?fù)溥B通性的前提下，在大量的隨機(jī)部署的傳感器節(jié)點(diǎn)中，如何解決稀疏性網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)是一個(gè)非常重要的問(wèn)題。他們證明了自己的方案可以減少工作節(jié)點(diǎn)，保證網(wǎng)絡(luò)拓?fù)湎∈瑁?jiǎn)化路由的復(fù)雜性和延長(zhǎng)網(wǎng)絡(luò)的生存期。雷鳴和李德氏提出了自組織無(wú)線傳感器網(wǎng)絡(luò)的可靠性的研究5，這針對(duì)兩種情況：缺乏WSN節(jié)點(diǎn)和外部的攻擊下，分析了不同拓?fù)浣Y(jié)構(gòu)的無(wú)線傳感器網(wǎng)絡(luò)錯(cuò)誤的耐受能力，并最終獲得優(yōu)化自組織的無(wú)線傳感器網(wǎng)絡(luò)的拓?fù)淠Ｐ停⑻岢隽嘶跓o(wú)線傳感器網(wǎng)絡(luò)的精制路由

51、算法。改進(jìn)的無(wú)標(biāo)度模型的WSN由于能量有限和惡劣的應(yīng)用環(huán)境，某些傳感器節(jié)點(diǎn)的能量耗盡或被自然力損壞，無(wú)線傳感器網(wǎng)絡(luò)會(huì)很容易遭到破壞，甚至還有一些傳感器節(jié)點(diǎn)被部署時(shí)已經(jīng)損壞。還設(shè)有一個(gè)限制最大限度和最低限度的傳感器節(jié)點(diǎn)的通信半徑，而不是其它已知的無(wú)標(biāo)度網(wǎng)絡(luò)，如互聯(lián)網(wǎng)絡(luò)，它沒(méi)有限制通信半徑。為了有一個(gè)平衡的能源消耗，有必要設(shè)置一個(gè)傳感器的有限度的飽和度值6。 為了回答這些要點(diǎn)，根據(jù)傳統(tǒng)的無(wú)標(biāo)度模型，模型建立的過(guò)程中本文已做出以下改進(jìn)：（1）大量的研究表明，許多在本質(zhì)上是復(fù)雜的網(wǎng)絡(luò)不僅是內(nèi)力，也是外部勢(shì)力的結(jié)果。形成一個(gè)完整的復(fù)雜網(wǎng)絡(luò)時(shí)，這不應(yīng)被忽略。在傳感器網(wǎng)絡(luò)工作的進(jìn)展中，節(jié)點(diǎn)故障不僅可能出現(xiàn)在

52、節(jié)點(diǎn)的能量消耗或他們?cè)馐茈S機(jī)攻擊時(shí)，也出現(xiàn)在外力情況下，如當(dāng)部署時(shí)的自然力。本文一種小概率的隨機(jī)損傷機(jī)制已被添加到傳感器網(wǎng)絡(luò)的形成中。（2）不同于互聯(lián)網(wǎng)網(wǎng)絡(luò)，其中能夠直接連接到彼此兩個(gè)節(jié)點(diǎn)，它們的連接永遠(yuǎn)不會(huì)受到真實(shí)位置的限制，無(wú)線傳感器網(wǎng)絡(luò)，兩個(gè)相連的節(jié)點(diǎn)由多跳（multi-hop）的方式彼此連接，使每個(gè)節(jié)點(diǎn)在通信半徑上都有一個(gè)最大長(zhǎng)度的限制。為了確保全網(wǎng)絡(luò)的稀疏性，還必須是有一個(gè)最小長(zhǎng)度的通信半徑限制。在本文中，改進(jìn)后的模型中。傳感器節(jié)點(diǎn)的通信半徑長(zhǎng)度的限制，已經(jīng)提出來(lái)了。（3）在無(wú)線傳感器網(wǎng)絡(luò)，如果存在一個(gè)傳感器很高的節(jié)點(diǎn)，其能源消耗是非?？?，它會(huì)嚴(yán)重不利。如果沒(méi)有足夠的能量支持某個(gè)節(jié)點(diǎn)，整個(gè)傳感器網(wǎng)絡(luò)一定會(huì)崩潰，。為了避免這種情況，本文在每個(gè)傳感器節(jié)點(diǎn)建立了一個(gè)飽和值限度。通過(guò)加入上述提到的限制形成無(wú)尺度的模式，改進(jìn)后的新模型將更加符合傳感器網(wǎng)絡(luò)的真實(shí)陳述。復(fù)雜網(wǎng)絡(luò)理論和分析方法將更加適合用來(lái)研究和分析傳感器網(wǎng)絡(luò)。改進(jìn)算法說(shuō)明具體算法改進(jìn)模型形成描述如下：給定的區(qū)域（假設(shè)是正方形）（1）分為HS* HS正方形（命名為BS）;（2）每個(gè)BS（假設(shè)是正方形）分為L(zhǎng)S* LS小正方形（命名為SS），并且每個(gè)SS中在其覆蓋區(qū)域只能