[1]周鹏,肖晓强,宁伟勋. 基于车辆轨迹社会属性的VANETs路由算法[J].计算机技术与发展,2017,27(11):28-32.
　ZHOU Peng,XIAO Xiao-qiang,NING Wei-xun. A VANETs Routing Algorithm Based on Trace Data’ s Social Attribute[J].,2017,27(11):28-32.
点击复制

 基于车辆轨迹社会属性的VANETs路由算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年11期
页码:
28-32
栏目:
智能、算法、系统工程
出版日期:
2017-11-10

文章信息/Info

Title:
 A VANETs Routing Algorithm Based on Trace Data’ s Social Attribute
作者:
 周鹏; 肖晓强; 宁伟勋
 国防科学技术大学 计算机学院
Author(s):
 ZHOU Peng; XIAO Xiao-qiang; NING Wei-xun
关键词:
 ; GPS轨迹数据; 车载自组织网络; 社会属性; 路由算法; 模拟
Keywords:
 ; GPS trace data; vehicular ad hoc networks; social attributes; routing algorithm; simulation
分类号:
TP393
文献标志码:
A
摘要:
 出租车的GPS轨迹数据在发展智慧交通方面有很大的潜力.通过分析这些数据可以发现车辆之间存在的社会关系或属性,而这些发掘出来的信息对于设计出性能更优的VANETs路由算法可以进行更好的指导.通过对GPS轨迹数据进行分析,获得了车辆节点的中心性和偏好活动区域;依据节点的这些属性,设计了基于中心性的路由和基于偏好性的路由,并进一步设计了同时考虑两者的路由,即TDSAR(Trace Data’s Social Attribute Routing,基于真实轨迹社会属性的路由);最后借助ONE作为平台,模拟路由算法并对算法的性能进行了评价.实验结果表明,在测试场景下,TDSAR算法可以在保持较小开销并保证传输延时不提高的前提下,获得较高的投递成功率.通过深入的挖掘车辆间的社会关系,有助于更好地选择中继节点,从而促进VANETs的发展.
Abstract:
 The GPS trace data for taxi has great potential in the development of intelligent transportation. By analysis of the data,the social relations or attributes between vehicles can be found,which could play a guiding role in design of VANETs routing with greater perform-ance. The vehicle nodes’ centricity and preference are obtained through analysis of the GPS trace data,and based on these properties,the routing algorithms are designed based on both centricity and preference,as well as both of them,which is named TDSAR( Trace Data’ s Social Attribute Routing) . Finally,with the ONE as the platform,the routing algorithm is simulated and its performance is evaluated. The results show that TDASR could get high delivery ratio on the premise of lower occupancy and stable delay. Through the in-depth mining of the vehicular relationships,the VANETs could get a better development when the better relay nodes are chosen.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(11):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(11):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(11):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(11):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(11):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(11):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(11):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(11):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(11):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(11):47.

更新日期/Last Update: 2017-12-25