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1、外文资料翻译原文部分：Model Design of Wireless Sensor Network based on Scale-Free Network Theory ZHANG Xuyuan dept. Communication Engineering School of Communication and Information Engineering Shanghai, ChinaAbstractThe key issue of researches on wireless sensor networks is to balanc the energy costs across t
2、he 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, s
3、ocial 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 wire
4、less sensor network in order to achieve the optimization of some certain network characteristics of wireless sensor network. Based on generation rules of traditional scalefree networks, this paper added several restrictions to the improved model. The simulation result shows that improvements made in
5、 this paper have made the entire network have a better robustness to the random failure and the energy costs are more balanced and reasonable. This improved model which is based on the complex network theory proves more applicable to the research of wireless sensor network.Key-words:Wireless sensor
6、network; Complex network; Scale-free network I. INTRODUCTIONIn recent years, wireless sensor networks have attracted more and more related researchers for its advantages. Sensor nodes are usually low-power and non-rechargeable. The integrity of the original networks will be destroyed and other nodes
7、 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 e
8、ntire network1. Complex network theory has been for some time since first proposed by Barabasi and Albert in 1998, but complex network theory and analysis method applied to wireless sensor networks research is seriously rare and develops in slow progress. As a special complex network limited especia
9、lly 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 me
10、chanism 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 networ
11、k, 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 theo
12、ry and analysis methods of complex network will be more suitable for the researches of wireless sensor network. II. PROGRESS OF RELATED RESEARCH Hailin Zhu and Hong Luo have proposed two complex networks-based models for wireless sensor networks3, the first of which named Energy-aware evolution mode
13、l (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 introd
14、uced into the algorithm, which can make energy consumption more balanced than the previous model (EAEM). CHEN Lijun and MAO Yingchi have proposed a topology control of wireless sensor networks under an average degree constraint4. In the precondition of the topology connectivity of wireless sensor ne
15、tworks, 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
16、period. LEI Ming and LI Deshi have proposed a research on selforganization reliability of wireless sensor network5, 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 optim
17、ized selforganized topological models of WSN and proposes a refined routing algorithm based on WSN. III. IMPROVED SCALE-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 dep
18、letion or destruction by the nature, and even some sensor nodes have been damaged when deployed. There is also a restriction onMaxinum 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
19、radius. To have a balanced energy consumption, it is necessary to set up a saturation value limited degree of each sensor node6. In response to these points, based on the traditional scalefree model, this paper has made the following improvements in the process of model establishment: (1) A large nu
20、mber 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
21、 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 co
22、nnect 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
23、, there must also be a minimum of length restriction on their communication radius. In this paper, a length restriction on communication radius of sensor nodes has been proposed in the improved model. (3) In sensor network, if there exists a sensor node with a seriously high degree, whose energy con
24、sumption 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
25、 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 ALGORITHM The s
26、pecific algorithm of the improved model formation are described as follows: (1) A given region (assumed to be square) is divided into HS*HS big 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 cover
27、age region; (3) m 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 is a quantity parameter);(4) The newly generated node v, has a certain probability of to be damaged di
28、rectly so that it will never be connected with any existing nodes; (5) The newly generated node v connects 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 between the newly generated node v connect
29、s and the existing node i shall be shorter than the maximum of the communication radius of sensor nodes. Above all, the probability that the existing node i will be connected with the newly generated node v can be shown as follows: (1)In order to compute it conveniently, here assumed that few nodes
30、had reached the degree of saturation value .That is, n is very minimal in Eqs.(1) so that it can be ignored here. And in Eqs.(1), can be regarded as a constant parameter, so we have,and Eqs.(1) can be rewritten as: (2)With The varying rate with time of , we get: (3)When .According to the initial con
31、dition:, 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 that probability density of is a constant parameter: ,we replace it into Eqs. (5), then we get: (6)So we get: (
32、7)When ,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 the time parameter t and the quantity of edges m generated at each time interval. could be calculated by the maxinum re
33、striction on communication radius of each sensor node and the area of the entire coverage region S, that is . Then we we replace and into Eqs. (5), and eventually we get: (9)V. SIMULATIONThis paper used Java GUI mode of BRITE topology generator to generate the topology, and parameter settings were a
34、s follows: 1) N=5000N means the quantity of the sensor nodes at the end of the topology generation. 2)m means the quantity of the new generated edges by the new generated node at each time interval. 3)HS=500HS means the given region was divided into HS*HS big squares. 4) LS=50LS means each big squar
35、e was divided into LS*LS small squares. 5) is the mininum restriction on communication radius of each sensor node. 6) is the maxinum restriction on communication radius of each sensor node. 7) PC=1PC means wether preferential connectivity or not. 8) IG=1IG means wether incremental grouth or not. 9)
36、This means that any newly generated node has 1% chance to be node failure and the newly generated node if normal only connect with one existing node .Then we got each degree of the sensor network nodes from BRITE topology generator. To analyze the degree distribution, we use Matlab to calculate data
37、s and draw graph. As can easily be seen from Fig. 1, the distribution of degree k subjected approximately to Power-Law distribution. However, the value of is no longer between 2 and 3, but a very large value, which is caused by the random damage probability to new generated nodes when deployed and t
38、he maxinum of communication radius 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 deplo
39、yed.Compared 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 lef
40、t 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. VI. CONCLUSIONThis paper has added a random damage to new generated nodes when deplo
41、yed; 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 rest
42、riction 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 a limited degree of saturation value on each sensor node. In this paper, an improved scale-free network model was proposed to introduce the
43、 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.外文资料翻译译文部分：基于无尺度网络理论的无线传感器网络模型设计摘要：关于无线传感器网络的研究主要任务就是平衡整个网络的能量消耗，并且为了延长整个网络的生存期而加强其健壮性。
44、作为一种一种特殊复杂的受环境限制的网络，传感器网络和传统的复杂网络有很大不同，譬如因特网，生态网络，社交网络等等。这里有必要介绍如何通过复杂网络理论和分析方法来研究无线传感器网络，这关键在于建立一个成功的模型，能够使复杂网络和分析方法能更适应于无限传感器网络的应用，实现无线传感器网络的某些网络特点的优化。基于传统的无尺度网络的产生规则，本文增加了一些限制改进模型。仿真结果表明，本文提到的改进方法使整个网络对于随机差误性有更好的健壮性，能量消耗也更平衡和合理。基于复杂网络改进的模型在无线传感器网络具有较强的实用性。关键词：无线传感器网络；复杂网络；无尺度网络1 介绍近年来，无线传感器网络吸引了越
45、来越多的研究者。传感器节点通常是低能量且不可充电的。原始网络的完整性会受到破坏，并且如果某些特定节点能量耗尽，其它节点会对数据传输有更多的经济负担。无线传感器网络的关键任务就是平衡所有传感器节点的能量消耗和最小化随机差误或传感器节点的随机攻击对整个网络带来的影响。复杂网络理论懂首次是由Barabasi和Albert在1998年提出的，但是复杂网络理论和分析方法应用到无线传感器网络还相当稀少，发展比较缓慢。作为一种一种特殊复杂的受环境限制的网络，传感器网络和传统的复杂网络有很大不同，现存复杂网络理论和分析方法不能直接运用到分析传感器网络。基于无尺度网络理论，当在产生规则配置时，对每个传感器节点增
46、加了随机差误机制。考虑到传感器网络的实际情况，增加了对传感器通信范围的最大值和最小值的限制。为了保持整个网络能量消耗的平衡，本文还增加了对每个传感器节点的饱和度限制。改进的无尺度网络不仅提到了上面的改进，并且具有不少传统无尺度网络的优点，譬如避免随机攻击的能力，这样现有的复杂网络的理论和分析方法会适应于无线传感器网络的研究。2 相关研究的进程Halin zhu和Hong Luo已经提出两种无线传感器网络的复杂网络模型，第一个称之为可感知能量的改进模型（EAEM），能够以能量有效的方式组织网络，能产生改进网络传感器节点对随机差误的可靠性。第二个称之为能量平衡的改进模型（EBEM），每个节点的连接
47、最大数目被应用到算法中，这能使能量消耗比前者更平衡。CHEN Lijun和MAO Yingchi提出一种在平均水平限制下的无线传感器网络的拓扑控制，主要应用在两种情形：在外部攻击下的WSN节点不足，分析WSN不同拓扑的容错能力，最终得到优化有组织的WSN拓扑模型，提出了基于WSN的精炼路由器算法。3 改进的WSN无尺度网络模型由于能量限制和较差的应用环境，当某些特定传感器节点能量耗尽，被自然摧毁或者在配置的时候被破坏，无线传感器网络可能较容易崩溃。相比其它已知的无尺度网络，譬如对通信范围没有限制的因特网，对传感器通信范围的最大值和最小值进行了限制。为了由一个平衡的能量消耗，有必要设置一个每个传感器节点的饱和度值。基于传统的无尺度网络模型，对应这些点，本文在模型建立过程中进行了如下改进：大量研究表明，自然中的许多复杂网络不仅源于内部能量，而且也来自外部推动，这对于整个复杂网络而言是不可忽略的。节点差误可能不仅通过节点工作过程中的能量耗尽或者随机攻击而发生，也可能通过外部因素发生，譬如在配置过程中通过大自然。本文，随即破坏的最小可能性机制已增加到传感器网络的形成中。不像任意两个节点可通信是直接的并不受实际位置限制的因特网，传感器网络两个节点需要通过多跳连接，这样每个节点在通信范围上具有一个最大值的限制。为保证整个网络的稀疏，必须有一个通信范围的最小值限制。本文中，传
