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基于SDN的社会网络隐私数据均衡调度算法研究

Social Network Privacy Data Equalization Scheduling Algorithm Based on SDN
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摘要 为了提高社会网络隐私数据的优化传输能力,降低数据传输的误码率,提出基于SDN的社会网络隐私数据均衡调度算法。构建社会网络隐私数据传输信道模型,采用匹配滤波检测方法进行社会网络隐私数据传输的滤波干扰抑制,提取社会网络隐私数据的正相关特征量,采用SDN均衡器,根据特征分布的均衡性进行优化调度设计,实现社会网络隐私数据的均衡调度。仿真结果表明,该算法的数据传输稳态控制能力较强,输出误码率较低。 In order to improve the optimal transmission ability of social network privacy data and reduce the bit error rate (BER) of data transmission, a balanced scheduling algorithm of social network privacy data based on SDN is proposed. The channel model of social network privacy data transmission is constructed, and the matched filter detection method is used to suppress the filtering interference of social network privacy data transmission, and the positive correlation features of social network privacy data are extracted. SDN equalizer is used to optimize the scheduling design according to the equalization of feature distribution, and the balanced scheduling of social network privacy data is realized. The simulation results show that the algorithm has strong steady-state control ability of data transmission and low output bit error rate (BER).
作者 盛权为 Sheng Quanwei(Changsha Medical University, Changsha, Hunan 410219, China)
机构地区 长沙医学院
出处 《黑龙江工业学院学报(综合版)》 2019年第9期37-42,共6页 Journal of Heilongjiang University of Technology(Comprehensive Edition)
基金 湖南省大学生思想道德素质提升工程省级项目“以‘微课’为载体的高校实习生思想道德素质教育互动平台建设”(编号:18WL19)
关键词 SDN 社会网络 隐私数据 均衡调度 SDN social network privacy data balanced scheduling
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  • 1李德毅,刘常昱,杜鹢,韩旭.不确定性人工智能[J].软件学报,2004,15(11):1583-1594. 被引量:405
  • 2张光卫,李德毅,李鹏,康建初,陈桂生.基于云模型的协同过滤推荐算法[J].软件学报,2007,18(10):2403-2411. 被引量:196
  • 3Domingos P, Richardson M. Mining the network value of customers//Proceedings of the 7th ACM SIGKDD Interna- tional Conference on Knowledge Discovery and Data Mining. San Francisco, USA, 2001: 57-66.
  • 4Richardson M, Domingos P. Mining knowledge-sharing sites for viral marketing//Proeeedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Canada, 2002:61-70.
  • 5Kempe D, Kleinberg J, Tardos L. Maximizing the spread of influence through a social network//Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington DC, USA, 2003: 137-146.
  • 6Leskovec J, Krause A, Guestrin C, et al. Cost-effective out- break detection in networks//Proeeedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, USA, 2007:420-429.
  • 7Chen Wei, Wang Ya-Jun, Yang Si-Yu. Efficient influence maximization in social networks//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009: 199-207.
  • 8Estavez Pablo A, Vera P, Saito K. Selecting the most influential nodes in social networks//Proceedings of the 2007 International Joint Conference on Neural Networks. Orlando, USA, 2007:2397-2402.
  • 9Chen Wei, Wang Chi, Wang Ya-Jun. Scalable influence maximization for prevalent viral marketing in large-scale social networks//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington DC, USA, 2010:1029-1038.
  • 10Chen Wei, Yuan Yi-Fei, Zhang Li. Scalable influence maxi- mization in social networks under the linear threshold model //Proceedings of the 10th IEEE International Conference on Data Mining. Sydney, Australia, 2010:88-97.

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