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.
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.
[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.
