北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (1): 8-13,27.doi: 10.13190/j.jbupt.2019-061

• 论文 • 上一篇    下一篇

基于链路预测的手机节能方法

徐九韵1, 孙忠顺2, 张如如3   

  1. 1. 中国石油大学(华东)计算机科学与技术学院, 青岛 266580;
    2. 中国石油大学(华东)海洋与空间信息学院, 青岛 266580;
    3. 中移(苏州)软件技术有限公司, 苏州 215010
  • 收稿日期:2019-04-21 出版日期:2020-02-28 发布日期:2020-03-27
  • 作者简介:徐九韵(1966-),男,教授,E-mail:jyxu@upc.edu.cn.
  • 基金资助:
     

Mobile Phone Energy Saving Based on Link Prediction

XU Jiu-yun1, SUN Zhong-shun2, ZHANG Ru-ru3   

  1. 1. College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China;
    2. College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China;
    3. The China Mobile(Suzhou) Software Technology Company, Suzhou 215010, China
  • Received:2019-04-21 Online:2020-02-28 Published:2020-03-27
  • Supported by:
     

摘要: 移动云计算为部署大量移动服务提供了技术支持,但用户在不稳定的通信条件下访问云资源往往需要大量的能耗,制约了移动云计算的广泛应用.对此,提出了基于用户交互行为最大化的链路预测方法.首先在数据预测模型的基础上利用基于互动关系改进的交互度方法对已知用户访问的数据进行预测;再结合基于用户行为的社交网络Friendlink方法对预测数据进行数据分析筛选,利用数据预存储机制来预存上述预测数据.实验结果表明,在保证不涉及用户隐私信息,并提高用户下次访问命中率的情况下,达到了预期的手机节能目的.

关键词: 手机节能, 访问数据预测, 社交网络, 互动次数

Abstract: The technology of mobile cloud computing is benefit for deploying various mobile applications. However, there is an energy consumption problem to access cloud resources via mobile phone, which needs to establish connections many times under unstable communication conditions. To solve this problem, a link prediction method based on maximum user interaction behavior was proposed. Firstly, based on data prediction model, an interaction degree method based on improved interaction relationship is used to predict the data accessed by users. Then, combined with the friend link method of social network based on user behavior, the prediction data is analyzed and filtered, and the pre-storage mechanism is used to pre-store the above prediction data. Experiments show that the expected energy saving of mobile phones can be achieved without involving users' private information and improving the hit rate of users' next visit.

Key words: smartphones save energy, access data prediction, social network, interaction times

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