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1、专业英语课程论文论文题目：Wireless sensor network node Positioning algorithm 学院(系)： 信息工程学院 专 业： 电子科学与技术 班 级： 信研 135 班 学生姓名： 学 号： 1049721303194 教 师： 2014年 5月 16 日Wireless sensor network node positioning algorithmHu jianSchool of Information Engineering, Wuhan University of Technology, Wuhan, China434474935Abstrac
2、t- Wireless sensor networks as a new type of data acquisition technology, combined with microelectronics, wireless communications and wireless networks, such as multi-discipline, have broad application prospects in the field of industrial control, military, medical assistance, and environmental moni
3、toring. In most applications, the physical location of the guide to the sensor node is a basic requirement, however, due to the large number of sensor nodes, randomly distributed, and the software and hardware resources are limited, so study effective positioning algorithm to determine the location
4、of each node has an important theoretical significance and practical value. Access to large amounts of literature on the basis of the lessons do an overview of the wireless sensor network-based positioning technology, wireless sensor networks, highlights several typical distributed positioning algor
5、ithm principle and characteristics, including Amorphous , APIT, Centroid, DV-Hop, RSSI, etc., its Matlab simulation environment simulation analysis, and compare the positioning accuracy of the various algorithms and error. Keywords: wireless sensor networks, localization algorithm, Matlab, simulatio
6、n analysisI.INTRODUCTIONPositioning of wireless sensor networks is the wireless, self-organizing network to provide location information of nodes in the network in some way, self-organizing network localization process can be divided into self-positioning and targeting node node positioning itself t
7、o determine the coordinates of the information network node . The targeting information is needed to determine the coordinates of a target within the network coverage or an event. Node itself is the process of determining the positioning properties of the network itself, or you can use the manual ca
8、libration of various node localization algorithm to complete. Targeting the location of the network nodes is known as the reference node, the destination node determines that the event or the location in the network.II.SYSTEM DESIGNTIn sensor networks, most existing node localization algorithms, ref
9、erence anchor nodes are positioned to take advantage of the way place. A large number of sensor nodes in the target area in the layout: a portion called the particular node, also called anchor node (beacon), which themselves can be obtained by carrying the exact location of the GPS positioning appar
10、atus or artificial means, and have more than node powerful capabilities, but such a small proportion of nodes; node other unknown locations themselves, through their neighbor nodes to communicate to get information of each anchor nodes, these nodes using the location information as a reference, and
11、use some calculations to get their position known to the unknown is called a node (node)In wireless sensor networks usually used only two-dimensional coordinate system of .So long as we know from the unknown node with three anchor nodes can calculate the position of the unknown node.Figure 1. Schema
12、tic trilateral positioning Assuming three anchor node coordinates are (X1, Y1), (X2, Y2), (X3, Y3), the coordinates of the unknown node (Xu, Yu), unknown node distances from three anchor nodes are R1, R2 , R3, shown in Figure 3-2, the distance formula based on a two-dimensional coordinate system of
13、equations can be obtained as follows: (1)The above equations are usually solved using the maximum likelihood method estimates the unknown node coordinate multilateral used (Xu, Yu)： (2) In summary, it may obtain a plurality of unknown nodes as long as the anchor node that the unknown distance from t
14、he node to the anchor node 3 may be positioned on the practical application of the unknown node, this calculation can be different for each selected three points, and finally The results were averaged for several times and thus improve the positioning accuracy.III.Specific positioning algorithmA. AP
15、IT AlgorithmAPIT algorithm theoretical basis is the best point inside the triangle test method PIT. PIT test principle is that if there is a direction unknown nodes simultaneously moving along this direction away from or close to three beacon nodes, then the unknown nodes located in three beacon nod
16、es outside the triangle; otherwise unknown nodes located within the triangle. Point test using the network in a relatively high density of nodes to simulate the mobile nodes using wireless signal propagation characteristics to determine whether far or near beacon nodes within the approximate triangl
17、e, usually in a given direction, a node from another node the farther the received signal strength is weaker. Neighbor nodes exchange their received signal strength determination of a distance of beacon nodes, the nodes to move mimic PIT.B.Centroid Positioning AlgorithmCentroid algorithm, the beacon
18、 node to a neighboring node periodically broadcasts a beacon packet, a beacon packet contains the identification number and the location information of beacon nodes. When the node receives the unknown number of different beacon beacon packet from a node or reception exceeds a certain threshold time,
19、 the position of which determines its beacon nodes consisting of the centroid of the polygon. Centroid algorithm based solely on network connectivity, and therefore relatively easy to implement. However, this method is affected by the density of the beacon nodes. Centroid algorithm for improved algo
20、rithm, density adaptive HEAP algorithm, by increasing the beacon beacon nodes nodes in a low density area in order to improve the positioning accuracy.C. DV-Hop Positioning Algorithm An advantage of the proposed method of ideological distance vector routing and GPS positioning. Consists of three pha
21、ses: First, all nodes in the network to obtain the number of hops from a beacon node; Secondly, when obtaining the position and the other beacon nodes hop distance apart, the beacon nodes calculate the average hop distance of the network, giving their survival period, then the survival of the school
22、 with a positive value in the webcast. Unknown node receives only record the first correction, and forwarded to the neighbors. This strategy ensures that the vast majority of node receives an average hop distance from the nearest beacon node. According unknown node hops records to calculate distance
23、 to jump beacon nodes.D. RSSI AlgorithmRSSI measurement model and the theoretical model of general experience using the signal propagation. For empirical model before the actual positioning, first select a number of test points, records the received signal strength at these points of the base statio
24、ns, to establish the relationship between position and signal strength line database (x, y, ss1, ss2 respective points, ss3 ). In the actual positioning, based on the measured signal strength (ss1 , ss2, ss3 ) and the signal strength recorded in the database by comparing the variance of the coordina
25、tes of the minimum signal strength that are used as the coordinates of the node point.IV. The simulation resultsA. APIT AlgorithmFigure 2. Node Distribution（300 nodes, including 60 anchor nodes, red * indicates anchor nodes, blue O represents the unknown node）Figure 3. Neighbor relationship diagram（
26、300 nodes, including 60 anchor nodes, red * indicates anchor nodes, red O indicates unknown node communication radius: 200m, anchor node communication radius:200m, communication model: Regular Model, the average connectivity of the network is: 31.1133, the average number of neighbor nodes of the net
27、work anchor is: 6.18）Figure 4. Figure positioning error（Red * indicates anchor nodes, blue O represents an estimate of the position of the unknown node, black O that they can not be positioned unknown nodes, blue - shows the positioning error of unknown nodes (nodes connected to an unknown location
28、and estimate the true position), a total of 300 node: 60 anchor nodes, 240 unknown nodes, 0 unknown nodes can not be located, the positioning error of 0.29857）V. CONCLUSIONS Five algorithms are square_random selected node distribution, GPS errors are 30m, communication radius comm-r are 200 unified
29、communication model for the same communication model Regular Model folder, a list of error will be calculated by the five algorithms，As shown in Figure 5:AlgoritmAmorphousAPITCentroidDV-HopRSSIDeviation0.279460.331820.327650.298570.071629 Figure 5. Positioning deviationSeen from the table, the maxim
30、um error and the Centroid APIT algorithm followed DV-Hop algorithm then Amorphous algorithm is the smallest error RSSI algorithm.REFERENCES 1 OU Dexiang, WANG Zhizhong. “The Design for Intelligent Node of DCS Based CAN Bus”. Electronic & Computer Design World, vol 19,2002.2 SUN Huixian, ZHANG Yuhua,
31、 LUO Feilu. “Data Collection System for Power Monitor Based on USB and CAN Bus”. Proceedings of the CSUEPSA, vol 21, pp: 99-103, 2009.3 ZHANG Zhen-wei, HUANG Shi-hong, “The Design of Vibration Signal Acquisition System,” Turbine Technology, 49 (3), pp.187-188, March 2007. 4 XU Huazhong, “FENG Bo. Design of USB Module Based on PDIUSBD12.”.Journal of Wuhan University of Technology(Information&Management Engineering), vol 02, 2008.5 LI Jinbo. “Design and Realization of USB20 Interface in Equipment Condition Monitoring Instrument”. Process Automation Instrumentation.Vol 29, pp: 14-17, 2008.