本文選題：無(wú)線(xiàn)傳感器網(wǎng)絡(luò) + 交通監(jiān)控系統(tǒng)　； 參考：《西南交通大學(xué)》2014年博士論文

【摘要】：汽車(chē)為人類(lèi)的出行帶來(lái)了極大便利,但隨著汽車(chē)數(shù)量的快速增加,交通擁堵問(wèn)題越來(lái)越嚴(yán)重。雖然政府不斷地修建高速公路、城市快速路,但是道路地增長(zhǎng)速度遠(yuǎn)低于汽車(chē)數(shù)量地增長(zhǎng)。為了解決這個(gè)問(wèn)題,政府近年來(lái)投入越來(lái)越多的資金和精力用于開(kāi)發(fā)智能交通系統(tǒng),希望通過(guò)提高信息化水平和管理水平來(lái)提高道路的通行效率,緩解交通擁堵。智能交通系統(tǒng)的首要任務(wù)就是對(duì)道路道路交通情況進(jìn)行實(shí)時(shí)地監(jiān)控和數(shù)據(jù)采集,然后在收集到的數(shù)據(jù)信息基礎(chǔ)上,及時(shí)作出高效、合理的控制決策。目前常用的交通監(jiān)測(cè)技術(shù)主要包括電磁感應(yīng)線(xiàn)圈回路檢測(cè)、雷達(dá)檢測(cè)和圖像處理技術(shù)等,但這些技術(shù)均因受其本身或特定環(huán)境因素的限制,存在著一些不足。這些缺點(diǎn)主要包括：構(gòu)建成本高昂、惡劣天氣識(shí)別度低、組網(wǎng)結(jié)構(gòu)復(fù)雜等缺點(diǎn)。而無(wú)線(xiàn)傳感器網(wǎng)絡(luò)(Wireless Sensor Networks, WSN)作為一種全新的信息獲取和處理技術(shù),能較好解決上述問(wèn)題。WSN結(jié)合了傳感器、微機(jī)電系統(tǒng)(Micro-Electro-Mechanical System, MEMS)和網(wǎng)絡(luò)通信等技術(shù),具有網(wǎng)絡(luò)自組織、自適應(yīng)性等特點(diǎn)。WSN由大量傳感器節(jié)點(diǎn)組成,每個(gè)節(jié)點(diǎn)都可以收發(fā)無(wú)線(xiàn)電信號(hào)信息,并將這些信息在網(wǎng)絡(luò)中進(jìn)行傳輸,最后將信息交給數(shù)據(jù)處理能力強(qiáng)的一些節(jié)點(diǎn)處理。由于無(wú)線(xiàn)傳感器網(wǎng)絡(luò)中的傳感器個(gè)體結(jié)構(gòu)簡(jiǎn)單、成本低廉,網(wǎng)絡(luò)具有自組織性等顯著優(yōu)點(diǎn)�？紤]將WSN技術(shù)作為新一代智能交通監(jiān)控系統(tǒng)的關(guān)鍵技術(shù),構(gòu)建基于WSN的分布式自適應(yīng)交通監(jiān)控系統(tǒng),但要將無(wú)線(xiàn)傳感器網(wǎng)絡(luò)應(yīng)用于交通監(jiān)控系統(tǒng)必須解決一系列技術(shù)問(wèn)題。本論文反映的研究工作以基于WSN的分布式自適應(yīng)交通監(jiān)控系統(tǒng)為對(duì)象,重點(diǎn)研究了傳感器網(wǎng)絡(luò)能量管理,底層結(jié)構(gòu)布局及密度優(yōu)化,節(jié)點(diǎn)定位等問(wèn)題。本論文的主要貢獻(xiàn)如下：(1)本文結(jié)合高速公路自身交通流量及物理上的特性,再充分對(duì)WSN路由協(xié)議及傳感器節(jié)點(diǎn)兩方面進(jìn)行改進(jìn),提出一種針對(duì)高速公路監(jiān)控系統(tǒng)的能量管理策略——基于TTL(Timeout Threshold LEACH)的交通監(jiān)控系統(tǒng)最小能耗模型。該模型基于低功耗自適應(yīng)分層路由協(xié)議(Low Energy Adaptive Clustering Hierarchy, LEACH),可以從整體上提升網(wǎng)絡(luò)的生命周期。在此基礎(chǔ)上,進(jìn)一步對(duì)每一個(gè)傳感器節(jié)點(diǎn)的超時(shí)閡值(Timeout Threshold, TT)進(jìn)行計(jì)算,并動(dòng)態(tài)設(shè)置節(jié)點(diǎn)的功率可管理部件(Power Manageable Component, PMC),將空閑時(shí)間的累計(jì)值與超時(shí)閾值比較而進(jìn)入到不同深度的休眠狀態(tài),達(dá)到進(jìn)一步降低傳感器節(jié)點(diǎn)能耗的目的。上述方式中,一個(gè)是降低網(wǎng)絡(luò)整體的能量消耗,一個(gè)是降低網(wǎng)絡(luò)中單個(gè)節(jié)點(diǎn)的能量消耗,通過(guò)以上點(diǎn)面結(jié)合的方式,爭(zhēng)取最大程度降低高速公路交通監(jiān)控系統(tǒng)的能量消耗,提升網(wǎng)絡(luò)的生命周期。(2)通過(guò)優(yōu)化無(wú)線(xiàn)傳感器網(wǎng)絡(luò)中各傳感器節(jié)點(diǎn)的位置使得由節(jié)點(diǎn)組成的網(wǎng)絡(luò)的覆蓋和連通性能達(dá)到最優(yōu)。根據(jù)高速公路的物理特性,并考慮高速公路中傳感器節(jié)點(diǎn)(Sensor Node)的感知覆蓋和通信能力對(duì)信號(hào)采集的影響,建立面向交通信息采集的多目標(biāo)約束優(yōu)化問(wèn)題模型,使用幾何加權(quán)法將其轉(zhuǎn)化為單一約束優(yōu)化問(wèn)題。最后采用化學(xué)反應(yīng)優(yōu)化算法(Chemical Reaction Optimization, CRO)求解該問(wèn)題。無(wú)線(xiàn)傳感器網(wǎng)絡(luò)節(jié)點(diǎn)合理布局使得系統(tǒng)的信號(hào)采集,后期維護(hù)擴(kuò)展以及成本節(jié)省等都有較大提高。(3)在基于WSN的分布式自適應(yīng)監(jiān)控系統(tǒng)中,常用DV-Hop算法來(lái)對(duì)網(wǎng)絡(luò)中的未知節(jié)點(diǎn)進(jìn)行定位,但定位出來(lái)的未知節(jié)點(diǎn)精度較低,誤差較大,因此我們?cè)谠械亩ㄎ荒Ｐ蜕?提出了一種采用粒子群優(yōu)化(Particle Swarm Optimization, PSO)和模擬退火(Simulated Annealing, SA)對(duì)DV-Hop進(jìn)行改進(jìn)的混合智能算法,實(shí)現(xiàn)更高的定位精度,并能大大降低未知節(jié)點(diǎn)的定位誤差。該算法更加適用于高速公路監(jiān)控系統(tǒng)的定位操作。(4)該文提出了基于WSN的分布式自適應(yīng)高速公路交通監(jiān)控系統(tǒng)的設(shè)計(jì)方案,并結(jié)合路面能見(jiàn)度、交通流量等具體數(shù)據(jù)構(gòu)建了車(chē)間間距監(jiān)控模型。該模型能夠利用WSN的優(yōu)勢(shì),實(shí)時(shí)將天氣、車(chē)流量等參數(shù)信息導(dǎo)入系統(tǒng),計(jì)算出合適的汽車(chē)間安全行車(chē)距離,當(dāng)汽車(chē)間行車(chē)距離小于安全距離時(shí),就將通過(guò)車(chē)載廣播、車(chē)載GPS或RFID等智能設(shè)備向駕駛?cè)藛T提出警示,避免交通事故和追尾的發(fā)生。本文的相關(guān)研究成果對(duì)于構(gòu)建基于WSN的分布式自適應(yīng)高速公路交通監(jiān)控系統(tǒng)具有參考意義和實(shí)際應(yīng)用價(jià)值,特別是對(duì)提高無(wú)線(xiàn)傳感器網(wǎng)絡(luò)在高速公路環(huán)境下的生命周期,提高傳感器網(wǎng)絡(luò)結(jié)構(gòu)布局和密度優(yōu)化,提高車(chē)輛定位精度等方面具有重要的應(yīng)用價(jià)值和經(jīng)濟(jì)價(jià)值。
[Abstract]:Cars have brought great convenience to human travel, but with the rapid increase of the number of cars, traffic congestion is becoming more and more serious. Although the government has continuously built the freeway and urban expressway, the growth rate of the road is far below the number of cars. In order to solve this problem, the government has invested more and more funds in recent years. The first task of the intelligent transportation system is to monitor and collect the traffic in the road in real time. Then, on the basis of the collected data and information, the first task of the intelligent transportation system is to make the high level of the traffic in the road. The current commonly used traffic monitoring techniques include electromagnetic induction coil circuit detection, radar detection and image processing technology, but these technologies are limited by their own or specific environmental factors. These shortcomings include high cost, low weather recognition, and a group of disadvantages. Wireless Sensor Networks (WSN), as a new information acquisition and processing technology, can better solve the above problems,.WSN combined with sensors, microelectromechanical systems (Micro-Electro-Mechanical System, MEMS) and network communication technology, with network self-organization, self-adaptive and so on. The point.WSN is composed of a large number of sensor nodes. Each node can send and receive radio signal information and transmit the information in the network. Finally, the information is delivered to some nodes with strong ability of data processing. Because of the simple structure, low cost, and self-organization of the sensor in wireless sensor network, the sensor has a simple structure and low cost. Considering the WSN technology as the key technology of the new generation of intelligent traffic monitoring and control system, a distributed adaptive traffic monitoring system based on WSN is constructed, but a series of technical problems must be solved to apply the wireless sensor network to the traffic monitoring system. The research work in this paper is based on the distributed adaptive traffic based on WSN. The main contributions of this paper are as follows: (1) in this paper, the main contributions of this paper are as follows: (1) in this paper, the traffic flow and physical characteristics of the freeway are combined, and the two aspects of the WSN routing protocol and sensor nodes are improved. The energy management strategy for the expressway monitoring system - the minimum energy consumption model of the traffic monitoring system based on the TTL (Timeout Threshold LEACH). Based on the low power adaptive hierarchical routing protocol (Low Energy Adaptive Clustering Hierarchy, LEACH), the life cycle of the network can be improved on the whole. On this basis, Furthermore, the Timeout Threshold (TT) of each sensor node is calculated, and the power manageable component (Power Manageable Component, PMC) of the node is dynamically set, and the cumulative value of idle time is compared with the timeout threshold to enter the dormant state of different depths to further reduce the energy consumption of sensor nodes. Objective. One is to reduce the energy consumption of the network as a whole, one is to reduce the energy consumption of a single node in the network. Through the combination of the above points, the energy consumption of the highway traffic monitoring system is reduced to the maximum degree and the life cycle of the network is improved. (2) by optimizing the sensors in the wireless sensor network The location of the node makes the network coverage and connectivity of the network optimal. According to the physical characteristics of the expressway, and considering the impact of the sensing coverage and communication ability of the sensor node (Sensor Node) on the expressway, a multi target constrained optimization model for traffic information acquisition is established. The geometric weighting method is transformed into a single constraint optimization problem. Finally, the Chemical Reaction Optimization (CRO) is used to solve the problem. The rational layout of the wireless sensor network nodes makes the system signal acquisition, the later maintenance extension and the cost saving and so on. (3) distributed self based on WSN. In the adaptive monitoring system, the DV-Hop algorithm is used to locate the unknown nodes in the network, but the unknown nodes have low precision and large error. Therefore, we put forward a kind of Particle Swarm Optimization (PSO) and simulated annealing (Simulated Annealing, SA) to DV-Hop on the original location model. The improved hybrid intelligent algorithm can achieve higher positioning accuracy and greatly reduce the location error of the unknown nodes. The algorithm is more suitable for the positioning operation of the expressway monitoring system. (4) the design of the distributed adaptive highway traffic monitoring system based on WSN is proposed in this paper, and the traffic visibility and traffic are combined with the road traffic. This model can make use of the advantages of WSN to import parameters such as weather and traffic flow into the system in real time, and calculate the appropriate distance of vehicle safe driving. When the distance of the vehicle is less than the safe distance, it will pass through the vehicle broadcasting, the vehicle GPS or RFID and other intelligent equipment to drive. The research results of this paper have reference significance and practical application value for the construction of distributed adaptive highway traffic monitoring system based on WSN, especially to improve the life cycle of wireless sensor network in the expressway environment, and improve the sensor network node. The layout and density optimization have important application value and economic value in improving vehicle positioning accuracy.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：U495;TP212.9;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 承向軍,楊肇夏;基于多智能體技術(shù)的城市交通控制系統(tǒng)的探討[J];北方交通大學(xué)學(xué)報(bào);2002年05期

2 鮑曉東;;智能交通系統(tǒng)的現(xiàn)狀及發(fā)展[J];北京工業(yè)職業(yè)技術(shù)學(xué)院學(xué)報(bào);2007年02期

3 陳茜;裘紅妹;林群;李鋒;關(guān)志超;趙一斌;張昕;蔡五三;杜勇;陳智宏;汪祖云;樂(lè)娟;謝振東;張孜;田夫;陶云;盧一夫;劉延?xùn)|;周飛雄;陳觀宙;;全國(guó)智能交通系統(tǒng)示范城市建設(shè)示例[J];城市交通;2008年02期

4 林亞平,王雷,陳宇,張錦,陳治平,童調(diào)生;傳感器網(wǎng)絡(luò)中一種分布式數(shù)據(jù)匯聚層次路由算法[J];電子學(xué)報(bào);2004年11期

5 侯惠峰;劉湘雯;于宏毅;胡捍英;;一種基于地理位置信息的無(wú)線(xiàn)傳感器網(wǎng)最小能耗路由算法[J];電子與信息學(xué)報(bào);2007年01期

6 胡堅(jiān)明;宋靖雁;李偉;;基于無(wú)線(xiàn)定位技術(shù)的交通信息獲取方法研究[J];公路交通科技;2007年10期

7 張文愛(ài);劉麗芳;李孝榮;;基于粒子進(jìn)化的多粒子群優(yōu)化算法[J];計(jì)算機(jī)工程與應(yīng)用;2008年07期

8 袁凌云;朱云龍;瞿立成;;分布式無(wú)線(xiàn)交通監(jiān)控系統(tǒng)的研究與實(shí)現(xiàn)[J];計(jì)算機(jī)工程;2006年08期

9 趙飛;張金榮;;智能交通系統(tǒng)在城市道路安全中的應(yīng)用研究[J];交通標(biāo)準(zhǔn)化;2008年01期

10 張存保;楊曉光;嚴(yán)新平;;基于浮動(dòng)車(chē)的交通信息采集系統(tǒng)研究[J];交通與計(jì)算機(jī);2006年05期

相關(guān)博士學(xué)位論文 前2條

1 李威武;城域智能交通系統(tǒng)中的控制與優(yōu)化問(wèn)題研究[D];浙江大學(xué);2003年

2 王亮;城市快速路交通流采集與控制相關(guān)問(wèn)題研究[D];天津大學(xué);2005年

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