無線傳感器網(wǎng)絡(luò)中DV-Hop算法的改進(jìn)研究
發(fā)布時(shí)間：2018-07-13 17:04
【摘要】：現(xiàn)在越來越多的人對(duì)無線傳感器網(wǎng)絡(luò)（Wireless Sensor Network，WSN）有了接觸與了解。由最初的簡(jiǎn)單傳感器到無線傳感器現(xiàn)階段無線傳感器網(wǎng)絡(luò)的發(fā)展，使得無線傳感器網(wǎng)絡(luò)在國(guó)際上被認(rèn)為是繼互聯(lián)網(wǎng)之后的第二大網(wǎng)絡(luò)。無線傳感器網(wǎng)絡(luò)與通信技術(shù)和計(jì)算機(jī)技術(shù)共同構(gòu)成信息技術(shù)的三大支柱。由于通信技術(shù)、嵌入式技術(shù)及傳感器網(wǎng)絡(luò)的相互融合與使用，越來越多的行業(yè)將其作為研究新項(xiàng)目的手段依據(jù)。 在無線傳感器網(wǎng)絡(luò)中，最重要的功能是傳感器節(jié)點(diǎn)自身的定位，需要網(wǎng)絡(luò)能發(fā)現(xiàn)事件發(fā)生的具體位置。如果沒有具體的位置信息，得到的數(shù)據(jù)信息也就沒有意義。所以在無線傳感器網(wǎng)絡(luò)中，只有得到節(jié)點(diǎn)發(fā)布的自身位置信息和搜集到的數(shù)據(jù)才可以決定下一步做什么，才便于找到節(jié)點(diǎn)傳輸?shù)穆肪�軌跡。GPS全球定位系統(tǒng)已經(jīng)發(fā)展的較為成熟，其定位準(zhǔn)確率高、效率快、抗干擾能力強(qiáng)，但是由于GPS定位需要有一定的設(shè)施支持，能量消耗大，使用成本高，且GPS系統(tǒng)只適用于無遮擋的空曠環(huán)境，因此，研究節(jié)點(diǎn)的定位算法成了關(guān)鍵之處。無線傳感器網(wǎng)絡(luò)的布置環(huán)境及一些設(shè)備支持相對(duì)來說會(huì)簡(jiǎn)單些，對(duì)于定位采用設(shè)置的定位算法，與硬件設(shè)備的需求不會(huì)太緊密，這對(duì)于研究無線傳感器網(wǎng)絡(luò)的定位提供了很大的方便。 無線傳感器網(wǎng)絡(luò)的定位算法是近些年來研究的熱點(diǎn)之一，根據(jù)網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)變化，節(jié)點(diǎn)自組織選擇定位算法進(jìn)行定位。技術(shù)人員需要從檢測(cè)的目標(biāo)節(jié)點(diǎn)獲得關(guān)鍵數(shù)據(jù)信息，從信息中分析當(dāng)前的節(jié)點(diǎn)處于環(huán)境中什么狀態(tài)，這就需要目標(biāo)節(jié)點(diǎn)將自身的位置反應(yīng)給技術(shù)人員。例如森林大火，傳感器節(jié)點(diǎn)不僅能發(fā)出火警信號(hào)，還需要將火災(zāi)的大致位置傳給監(jiān)測(cè)人員。因此對(duì)于定位算法，之前的研究人員、學(xué)者做出了很大的貢獻(xiàn)，給出了多種定位算法。本文對(duì)常見的定位算法進(jìn)行介紹，測(cè)距相關(guān)的及測(cè)距無關(guān)的分類方法中，針對(duì)典型的算法進(jìn)行了詳細(xì)的描述。最終將重點(diǎn)放在DV-Hop算法。 文章分析了DV-Hop算法誤差產(chǎn)生的原因，就未知節(jié)點(diǎn)計(jì)算平均每跳距離估計(jì)值及未知節(jié)點(diǎn)定位計(jì)算提出了改進(jìn)思想。其中，對(duì)于未知節(jié)點(diǎn)在利用周圍錨節(jié)點(diǎn)計(jì)算距離時(shí)，選擇一跳范圍內(nèi)的錨節(jié)點(diǎn)。原DV-Hop算法是未知節(jié)點(diǎn)接收第一個(gè)錨節(jié)點(diǎn)傳來的平均每跳距離值，但第一個(gè)錨節(jié)點(diǎn)傳來的值不一定是距離最近的。由于假設(shè)實(shí)驗(yàn)環(huán)境是一定的，節(jié)點(diǎn)是均勻分布的，，所以一跳范圍內(nèi)的錨節(jié)點(diǎn)傳來的平均每跳距離值近似的可以看成是第一個(gè)錨節(jié)點(diǎn)傳來的，算出平均的PJhopSize。這是與原算法的不同之處。其次用百分比是用來對(duì)錨節(jié)點(diǎn)求出的平均每跳距離進(jìn)行修正的，得到Dhop。最后用PJhopSize和Dhop算加和平均得到最終的平均每跳距離PDhop。當(dāng)未知節(jié)點(diǎn)用三邊測(cè)量法或極大似然估計(jì)法算出估計(jì)坐標(biāo)，往往會(huì)有很大的誤差。針對(duì)誤差過大影響定位精度的問題，需要找到一種算法能夠快速找到并且能夠優(yōu)化最終的求解，從而達(dá)到使精度提高的目的。文章又提出了智能算法——基于差分進(jìn)化的粒子群算法來對(duì)坐標(biāo)結(jié)果進(jìn)行優(yōu)化。對(duì)于粒子群算法和差分進(jìn)化粒子群算法，進(jìn)行了詳細(xì)的介紹。 文章在第五節(jié)就改進(jìn)的算法PDDV-Hop進(jìn)行了實(shí)驗(yàn)仿真驗(yàn)證。實(shí)驗(yàn)表明，改進(jìn)的算法在定位精度上有一定的提高。但依舊存在一些不足之處，例如實(shí)驗(yàn)環(huán)境是假設(shè)節(jié)點(diǎn)散布均勻的情況下進(jìn)行的，而現(xiàn)實(shí)生活中，節(jié)點(diǎn)的分布不會(huì)均勻。由于未知節(jié)點(diǎn)估算出來的坐標(biāo)本身就存在很大的誤差，智能算法就是改進(jìn)修正也只是對(duì)誤差值進(jìn)行修正，減小誤差范圍，而不能更精確的接近目標(biāo)位置。所以在之后的工作中，無論是對(duì)未知節(jié)點(diǎn)自身的定位還是用智能算法修正都有待進(jìn)一步的研究。
[Abstract]:Nowadays, more and more people have contact and understanding of Wireless Sensor Network (WSN). From the initial simple sensor to the development of wireless sensor network at the present stage of wireless sensor, wireless sensor network is considered to be the second largest network after the Internet. Communication technology and computer technology constitute the three pillars of information technology. Because of the integration and use of communication technology, embedded technology and sensor networks, more and more industries use it as a means to study new projects.
In the wireless sensor network, the most important function is the location of the sensor node itself, the network can find the specific location of the event. If there is no specific location information, the data information is meaningless. So in the wireless sensor network, only the location information published by the node and the collection of the data are obtained. Data can decide what to do next, only to find the route path of node transmission,.GPS global positioning system has developed more mature, its positioning accuracy is high, efficiency is fast, anti-interference ability is strong, but because GPS positioning needs a certain facility support, high energy consumption, high cost, and GPS system is only applicable to no cover. Therefore, the location algorithm of the research node is the key point. The layout environment of the wireless sensor network and the support of some equipment are relatively simple. The location algorithm for positioning and the need of hardware devices will not be too close. This provides a great deal of research on the location of wireless sensor networks. It is convenient.
The location algorithm of wireless sensor networks is one of the hot topics in recent years. According to the changes in the topology of the network, the node is self-organizing and localize the location algorithm. The technical personnel need to obtain the key data information from the target node of the detection, and analyze the status of the current nodes in the environment from the information, which requires the goal. The node reacts its own position to the technical personnel. For example, the forest fire, the sensor nodes not only send out the fire alarm signal, but also need to transmit the rough location of the fire to the monitoring personnel. Therefore, the researchers and scholars have made a great contribution to the location algorithm, and the scholars have given a variety of location algorithms. This article introduces the typical algorithm in detail, and focuses on the DV-Hop algorithm.
In this paper, the reasons for the error of DV-Hop algorithm are analyzed. An improved idea is proposed for the estimated average per hop distance of unknown nodes and the location calculation of unknown nodes. The original DV-Hop algorithm is the first anchor node for the unknown node to choose the anchor node in the one hop range when the unknown node calculates the distance from the surrounding anchor nodes. The average per hop distance is transmitted, but the value of the first anchor node is not necessarily the nearest distance. Since the experimental environment is certain and the node is uniformly distributed, the average per jump distance derived from the anchor node within the one jump can be seen as the first anchor node, and the average PJhopSize. is calculated. The difference between the original algorithm and the original algorithm. Secondly, the percentage is used to correct the average per hop distance obtained by the anchor node. The Dhop. finally uses PJhopSize and Dhop to add and average the final average per hop distance PDhop.. When the unknown node uses the three edge measurement or maximum likelihood estimation to calculate the estimated coordinates, it is often very large. It is necessary to find an algorithm to quickly find and optimize the final solution in order to achieve the goal of improving the precision. In this paper, an intelligent algorithm, based on the differential evolution particle swarm optimization, is proposed to optimize the result of the sitting standard. The evolutional particle swarm optimization (PSO) is introduced in detail.
In the fifth section, the improved algorithm PDDV-Hop is verified by experimental simulation. The experiment shows that the improved algorithm has a certain improvement in the positioning accuracy. But there are still some shortcomings, for example, the experimental environment is assumed that the node is distributed uniformly, and the distribution of the nodes is not uniform in real life. There is a great error in the coordinates of the nodes estimated by the nodes. The intelligent algorithm is an improvement and correction, which only corrects the error value and reduces the error range, but can not be closer to the target position. Therefore, in the later work, it is necessary to further study the location of the unknown node itself or the correction of the intelligent algorithm. Study.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP212.9;TN929.5
本文編號(hào)：2120118
[Abstract]:Nowadays, more and more people have contact and understanding of Wireless Sensor Network (WSN). From the initial simple sensor to the development of wireless sensor network at the present stage of wireless sensor, wireless sensor network is considered to be the second largest network after the Internet. Communication technology and computer technology constitute the three pillars of information technology. Because of the integration and use of communication technology, embedded technology and sensor networks, more and more industries use it as a means to study new projects.
In the wireless sensor network, the most important function is the location of the sensor node itself, the network can find the specific location of the event. If there is no specific location information, the data information is meaningless. So in the wireless sensor network, only the location information published by the node and the collection of the data are obtained. Data can decide what to do next, only to find the route path of node transmission,.GPS global positioning system has developed more mature, its positioning accuracy is high, efficiency is fast, anti-interference ability is strong, but because GPS positioning needs a certain facility support, high energy consumption, high cost, and GPS system is only applicable to no cover. Therefore, the location algorithm of the research node is the key point. The layout environment of the wireless sensor network and the support of some equipment are relatively simple. The location algorithm for positioning and the need of hardware devices will not be too close. This provides a great deal of research on the location of wireless sensor networks. It is convenient.
The location algorithm of wireless sensor networks is one of the hot topics in recent years. According to the changes in the topology of the network, the node is self-organizing and localize the location algorithm. The technical personnel need to obtain the key data information from the target node of the detection, and analyze the status of the current nodes in the environment from the information, which requires the goal. The node reacts its own position to the technical personnel. For example, the forest fire, the sensor nodes not only send out the fire alarm signal, but also need to transmit the rough location of the fire to the monitoring personnel. Therefore, the researchers and scholars have made a great contribution to the location algorithm, and the scholars have given a variety of location algorithms. This article introduces the typical algorithm in detail, and focuses on the DV-Hop algorithm.
In this paper, the reasons for the error of DV-Hop algorithm are analyzed. An improved idea is proposed for the estimated average per hop distance of unknown nodes and the location calculation of unknown nodes. The original DV-Hop algorithm is the first anchor node for the unknown node to choose the anchor node in the one hop range when the unknown node calculates the distance from the surrounding anchor nodes. The average per hop distance is transmitted, but the value of the first anchor node is not necessarily the nearest distance. Since the experimental environment is certain and the node is uniformly distributed, the average per jump distance derived from the anchor node within the one jump can be seen as the first anchor node, and the average PJhopSize. is calculated. The difference between the original algorithm and the original algorithm. Secondly, the percentage is used to correct the average per hop distance obtained by the anchor node. The Dhop. finally uses PJhopSize and Dhop to add and average the final average per hop distance PDhop.. When the unknown node uses the three edge measurement or maximum likelihood estimation to calculate the estimated coordinates, it is often very large. It is necessary to find an algorithm to quickly find and optimize the final solution in order to achieve the goal of improving the precision. In this paper, an intelligent algorithm, based on the differential evolution particle swarm optimization, is proposed to optimize the result of the sitting standard. The evolutional particle swarm optimization (PSO) is introduced in detail.
In the fifth section, the improved algorithm PDDV-Hop is verified by experimental simulation. The experiment shows that the improved algorithm has a certain improvement in the positioning accuracy. But there are still some shortcomings, for example, the experimental environment is assumed that the node is distributed uniformly, and the distribution of the nodes is not uniform in real life. There is a great error in the coordinates of the nodes estimated by the nodes. The intelligent algorithm is an improvement and correction, which only corrects the error value and reduces the error range, but can not be closer to the target position. Therefore, in the later work, it is necessary to further study the location of the unknown node itself or the correction of the intelligent algorithm. Study.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP212.9;TN929.5
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