基于WSN的分布式自适应交通监控系统的关键技术研究

发布时间：2018-05-18 18:55

本文选题：无线传感器网络 + 交通监控系统　；参考：《西南交通大学》2014年博士论文

【摘要】：汽车为人类的出行带来了极大便利,但随着汽车数量的快速增加,交通拥堵问题越来越严重。虽然政府不断地修建高速公路、城市快速路,但是道路地增长速度远低于汽车数量地增长。为了解决这个问题,政府近年来投入越来越多的资金和精力用于开发智能交通系统,希望通过提高信息化水平和管理水平来提高道路的通行效率,缓解交通拥堵。智能交通系统的首要任务就是对道路道路交通情况进行实时地监控和数据采集,然后在收集到的数据信息基础上,及时作出高效、合理的控制决策。目前常用的交通监测技术主要包括电磁感应线圈回路检测、雷达检测和图像处理技术等,但这些技术均因受其本身或特定环境因素的限制,存在着一些不足。这些缺点主要包括：构建成本高昂、恶劣天气识别度低、组网结构复杂等缺点。而无线传感器网络(Wireless Sensor Networks, WSN)作为一种全新的信息获取和处理技术,能较好解决上述问题。WSN结合了传感器、微机电系统(Micro-Electro-Mechanical System, MEMS)和网络通信等技术,具有网络自组织、自适应性等特点。WSN由大量传感器节点组成,每个节点都可以收发无线电信号信息,并将这些信息在网络中进行传输,最后将信息交给数据处理能力强的一些节点处理。由于无线传感器网络中的传感器个体结构简单、成本低廉,网络具有自组织性等显著优点。考虑将WSN技术作为新一代智能交通监控系统的关键技术,构建基于WSN的分布式自适应交通监控系统,但要将无线传感器网络应用于交通监控系统必须解决一系列技术问题。本论文反映的研究工作以基于WSN的分布式自适应交通监控系统为对象,重点研究了传感器网络能量管理,底层结构布局及密度优化,节点定位等问题。本论文的主要贡献如下：(1)本文结合高速公路自身交通流量及物理上的特性,再充分对WSN路由协议及传感器节点两方面进行改进,提出一种针对高速公路监控系统的能量管理策略——基于TTL(Timeout Threshold LEACH)的交通监控系统最小能耗模型。该模型基于低功耗自适应分层路由协议(Low Energy Adaptive Clustering Hierarchy, LEACH),可以从整体上提升网络的生命周期。在此基础上,进一步对每一个传感器节点的超时阂值(Timeout Threshold, TT)进行计算,并动态设置节点的功率可管理部件(Power Manageable Component, PMC),将空闲时间的累计值与超时阈值比较而进入到不同深度的休眠状态,达到进一步降低传感器节点能耗的目的。上述方式中,一个是降低网络整体的能量消耗,一个是降低网络中单个节点的能量消耗,通过以上点面结合的方式,争取最大程度降低高速公路交通监控系统的能量消耗,提升网络的生命周期。(2)通过优化无线传感器网络中各传感器节点的位置使得由节点组成的网络的覆盖和连通性能达到最优。根据高速公路的物理特性,并考虑高速公路中传感器节点(Sensor Node)的感知覆盖和通信能力对信号采集的影响,建立面向交通信息采集的多目标约束优化问题模型,使用几何加权法将其转化为单一约束优化问题。最后采用化学反应优化算法(Chemical Reaction Optimization, CRO)求解该问题。无线传感器网络节点合理布局使得系统的信号采集,后期维护扩展以及成本节省等都有较大提高。(3)在基于WSN的分布式自适应监控系统中,常用DV-Hop算法来对网络中的未知节点进行定位,但定位出来的未知节点精度较低,误差较大,因此我们在原有的定位模型上,提出了一种采用粒子群优化(Particle Swarm Optimization, PSO)和模拟退火(Simulated Annealing, SA)对DV-Hop进行改进的混合智能算法,实现更高的定位精度,并能大大降低未知节点的定位误差。该算法更加适用于高速公路监控系统的定位操作。(4)该文提出了基于WSN的分布式自适应高速公路交通监控系统的设计方案,并结合路面能见度、交通流量等具体数据构建了车间间距监控模型。该模型能够利用WSN的优势,实时将天气、车流量等参数信息导入系统,计算出合适的汽车间安全行车距离,当汽车间行车距离小于安全距离时,就将通过车载广播、车载GPS或RFID等智能设备向驾驶人员提出警示,避免交通事故和追尾的发生。本文的相关研究成果对于构建基于WSN的分布式自适应高速公路交通监控系统具有参考意义和实际应用价值,特别是对提高无线传感器网络在高速公路环境下的生命周期,提高传感器网络结构布局和密度优化,提高车辆定位精度等方面具有重要的应用价值和经济价值。
[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.
【学位授予单位】：西南交通大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：U495;TP212.9;TN929.5

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