一种基于多参数模糊逻辑的车载通信资源协同优化方法

doi:10.3969/j.issn.0255-8297.2018.05.004

应用科学学报 ›› 2018, Vol. 36 ›› Issue (5): 765-773.doi: 10.3969/j.issn.0255-8297.2018.05.004

一种基于多参数模糊逻辑的车载通信资源协同优化方法

张玉婷^1,2, 赵海涛^1,2, 唐紫浩^1,2, 朱洪波^1,2

1. 南京邮电大学通信与信息工程学院, 南京 210003;
2. 南京邮电大学江苏省无线通信重点实验室, 南京 210003

收稿日期:2017-06-26 修回日期:2017-07-27 出版日期:2018-09-30 发布日期:2018-09-30
通信作者: 赵海涛,副教授,研究方向:物联网与边缘计算,E-mail:zhaoht@njupt.edu.cn E-mail:zhaoht@njupt.edu.cn
基金资助:
国家自然科学基金（No.61771252，No.61471203）；江苏省自然科学基金（No.20171444）；江苏高等学校自然科学研究重大项目（No.18KJA510005）；江苏省“六大人才高峰”资助项目（No.DZXX-014）；江苏省普通高校专业学位研究生实践创新计划项目（No.JSCX17_0224）资助

On Resource Management in Vehicular Ad Hoc Networks: Multi-parameter Fuzzy Optimization Scheme

ZHANG Yu-ting^1,2, ZHAO Hai-tao^1,2, TANG Zi-hao^1,2, ZHU Hong-bo^1,2

1. College of Telecommunication and Information System, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2. Key Laboratory of Wireless Communications of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Received:2017-06-26 Revised:2017-07-27 Online:2018-09-30 Published:2018-09-30

摘要/Abstract

摘要： 车载自组织网络（vehicular ad hoc networks，VANETs）中存在大量文本、音频、视频等资源，但网络的高动态性和本地服务器的存储限制了资源的使用，不仅会导致用户请求资源失败，而且因传输无效零散数据而浪费带宽和存储.为解决这个问题，改进了一种基于模糊逻辑的资源管理（fuzzy logic resource management，FLRM）方法——多参数模糊逻辑资源管理（multi-parameter fuzzy logic resource management，MP-FLRM）.首先基于V2I通信模式收集并记录每种资源的请求时间、下载时间、上传时间，然后根据多参数的去模糊化运算得到每种资源的存在时间以实时更新资源列表.对比仿真与现有的多种资源管理方案，结果显示MP-FLRM能够提高系统的吞吐量，有效改善系统性能.

关键词: 车载自组织网络, 多参数模糊逻辑资源管理, 模糊逻辑, 资源管理

Abstract: Large amount of information like text, audio and video exists in vehicular ad hoc networks (VANETs). However, the highly dynamic network feature and the limited memory of the local server pose challenges to resource management, leading to the failure of resource presentations for users, moveover the transmission of the invalid fragment data would also result in the significant waste of the precious bandwidth and memory. To solve this problem, this paper proposes an improved fuzzy logic resource management (FLRM)-multi-parameter fuzzy logic resource management (MP-FLRM). In this scheme, we firstly gather and record the request time, download time and upload time for each resource by the designed vehicle to infrastructure (V2I) communication mode. Then survival time can be reached after defuzzifier of multi-parameters thus, used for updating the resource list in real time. Simulation results show that MP-FLRM will improve the system throughput, accordingly, enhancing the system performance.

Key words: fuzzy logic, vehicular ad hoc networks (VANETs), multi-parameter fuzzy logic resource management (MP-FLRM), resource management

中图分类号:

TN911.73

张玉婷, 赵海涛, 唐紫浩, 朱洪波. 一种基于多参数模糊逻辑的车载通信资源协同优化方法[J]. 应用科学学报, 2018, 36(5): 765-773.

ZHANG Yu-ting, ZHAO Hai-tao, TANG Zi-hao, ZHU Hong-bo. On Resource Management in Vehicular Ad Hoc Networks: Multi-parameter Fuzzy Optimization Scheme[J]. Journal of Applied Sciences, 2018, 36(5): 765-773.

参考文献

[1] Luan T H, Cai L X, Chen J, Shen X S, Bai F. Engineering a distributed infrastructure for large-scale cost-effective content dissemination over urban vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2014, 63(3):1419-1435.
[2] Arkian H R, Atani R E, Diyanat A, Pourkhalili A. A cluster-based vehicular cloud architecture with learning-based resource management[J]. Journal of Supercomputing, 2015, 71(4):1401-1426.
[3] Lee W C, Tsang K F, Chi H R, Hung F H, Wu C K, Chui K T. A high fuel consumption efficiency management scheme for Phevs using an adaptive genetic algorithm[J]. Sensors, 2015, 15(1):1245-1251.
[4] Sousa T, Morais H, Pinto T. Energy resource management under the influence of the weekend transition considering an intensive use of electric vehicles[C]//Power Systems Conference. IEEE, 2015:1-6.
[5] Su Z, Xu Q, Qi Q. Big data in mobile social networks:a QoE-oriented framework[J]. IEEE Network, 2016, 30(1):52-57.
[6] Su Z, Xu Q, Zhu H. A novel design for content delivery over software defined mobile social networks[J]. IEEE Network, 2015, 29(4):62-67.
[7] Cueva-Fernandez G, Gonzalez-Crespo R. Fuzzy decision method to improve the information exchange in a vehicle sensor tracking system[J]. Applied Soft Computing, 2015, 35(C):708-716.
[8] Lin G, Soh S, Chin K W. Energy-aware traffic engineering with reliability constraint[J]. Computer Communications, 2015, 57(1):115-128.
[9] Wang T, Song L, Han Z. Coalitional graph games for popular content distribution in cognitive radio VANETs[J]. IEEE Transactions on Vehicular Technology, 2014, 62(8):4010-4019.
[10] Aron R, Chana I, Abraham A. A hyper-heuristic approach for resource provisioning-based scheduling in grid environment[J]. Journal of Supercomputing, 2015, 71(4):1427-1450.
[11] Liu Y, Ma J, Niu J. Roadside units deployment for content downloading in vehicular networks[C]//IEEE International Conference on Communications. IEEE, 2013:6365-6370.
[12] Kapade N. TLC:trust point load balancing method using coalitional game theory for message forwarding in VANET[C]//Wireless Computing and Networking. IEEE, 2015:160-164.
[13] Miao Z, Li C, Zhu L. On resource management in vehicular ad hoc networks:a fuzzy optimization scheme[C]//Vehicular Technology Conference. IEEE, 2016:1-5.
[14] Limouchi E, Mahgoub I. BEFLAB:bandwidth efficient fuzzy logic-assisted broadcast for VANET[C]//IEEE Symposium Series on Computational Intelligence. IEEE, 2016:1-8.
[15] Cordeschi N, Amendola D, Baccarelli E. Reliable adaptive resoruce management for cognitive cloud vehicular networks[J]. IEEE Transactions on Vehicular Technology, 2015:2528-2537.

一种基于多参数模糊逻辑的车载通信资源协同优化方法

On Resource Management in Vehicular Ad Hoc Networks: Multi-parameter Fuzzy Optimization Scheme

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	孙茂鑫, 钱红燕, 陈兵, 张轩. SDN环境下基于MPTCP协议的切换管理[J]. 应用科学学报, 2017, 35(1): 117-127.
[2]	唐良瑞1，杨筝1，赵琳2. 有效均衡异构无线网络负载的接入选择[J]. 应用科学学报, 2013, 31(5): 441-447.
[3]	温祥西，孟相如，李明迅. 基于最优样本子集的在线模糊LSSVM混沌时间序列预测[J]. 应用科学学报, 2013, 31(4): 411-417.
[4]	王庆文1，史浩山1，戚茜2. 解决Ad Hoc网络广播冗余竞争冲突的高效广播算法[J]. 应用科学学报, 2011, 29(5): 447-452.
[5]	胡越黎;计慧杰. 短期电力负荷预测的模糊神经网络方法[J]. 应用科学学报, 2009, 27(1): 79-83 .
[6]	卢光跃;陈文艺;王军选. 多种业务下OFDMA无线资源管理[J]. 应用科学学报, 2008, 26(5): 495-498 .
[7]	周晶;景志宏. 基于GPS调度的OFDM系统低复杂度资源管理[J]. 应用科学学报, 2008, 26(1): 12-12 .
[8]	曲子芳. 一类非线性系统的模糊自适应控制[J]. 应用科学学报, 2007, 25(2): 207-207 .
[9]	芮世民, 朱继懋. 自适应模糊建模及其在船舶动力定位中的应用[J]. 应用科学学报, 2000, 18(3): 211-213.