基于改进DV-HOP的道路交通拥堵传感节点快速监测

以传感节点为基础的交通拥堵监控设备，在海量三维交通信息中，处理数据能力较差，导致监测目标延迟高。为缓解交通拥堵为出行带来的压力，及时有效疏散拥堵区域，本文设计一种基于改进DV-HOP的道路交通拥堵传感节点快速监测方法。在待监测区域安置无线视觉传感器，划分子节点与Sink节点，初始化本地节点数据存储器、定时计数器等设备，依照无线传感网采集车辆通行状况和整体长度；把异常道路数据作为小概率事件，确立速率采集周期及交通状态采集周期，推算历史车辆速率均值和交通数据方差，设定拥堵临界值，分析路段是否产生拥堵；对道路交通拥堵节点进行初始化，确定全部道路交通拥堵节点，然后，将三维的道路交通拥堵节点变换成二维坐标中，实现道路交通拥堵节点二维变换，对粒子群优化方法进行改进， 利用改进DV-HOP算法获取道路交通拥堵节点位置信息，得出道路交通拥堵节点监测结果。实验分析表明：本文方法的均等系数值可达0.998，数据传输延时仅为3.5s，表明交通拥堵监测精度较高。

Rapid Monitoring of Road Traffic Congestion Sensor Nodes Based on Improved DV-HOP

Traffic congestion monitoring devices based on sensor nodes have poor data processing capabilities in massive three-dimensional traffic information, resulting in high delay in monitoring targets. To alleviate the pressure of traffic congestion on travel and evacuate congested areas in a timely and effective manner, this paper designs a fast monitoring method for road traffic congestion sensor nodes based on improved DV-HOP.Install wireless electromagnetic sensors in the area to be monitored, divide sub nodes and Sink nodes, initialize local node data memory, timing counter and other equipment, and collect vehicle traffic conditions and overall length according to the wireless sensor network; Take the abnormal road data as a small probability event, establish the rate collection cycle and the traffic state collection cycle, calculate the average value of the historical vehicle speed and the variance of the traffic data, set the congestion threshold, and analyze whether there is congestion on the road section; Initialize the road traffic congestion nodes, determine all road traffic congestion nodes, and then transform the three-dimensional road traffic congestion nodes into two-dimensional coordinates to achieve the two-dimensional transformation of road traffic congestion nodes. Improve the particle swarm optimization method, use the improved DV-HOP algorithm to obtain the location information of road traffic congestion nodes, and obtain the monitoring results of road traffic congestion nodes. The experimental analysis shows that the equality coefficient value of this method can reach 0.998, and the data transmission delay is only 3.5s, indicating that the traffic congestion monitoring accuracy is high.