北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2012, Vol. 35 ›› Issue (2): 46-49.doi: 10.13190/jbupt.201202.46.122

• 论文 • 上一篇    下一篇

基于混合高斯分布的传感器网络主观信任模型

巩思亮1,2,邢涛1,熊永平3,马建3   

  1. 1 中国科学院 上海微系统与信息技术研究所, 上海 200050; 2 中国科学院 研究生院, 北京 100049; 3 北京邮电大学 网络技术研究院, 北京 100876
  • 收稿日期:2011-07-11 修回日期:2011-10-13 出版日期:2012-04-28 发布日期:2012-01-05
  • 通讯作者: 巩思亮 E-mail:gongsiliang@126.com
  • 作者简介:巩思亮(1985-),男,博士生,E-mail:gongsiliang@126.com 邢涛(1970-),男,研究员,博士生导师
  • 基金资助:

    国家重点基础研究发展计划项目(2011CB302901);国家重大科技专项项目(2009ZX03006-003)

Mixed Gaussian Based Subjective Trust Model for Sensor Networks

GONG Si-liang1,2,XING Tao1,XIONG Yong-ping3,MA Jian3   

  1. 1 Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Science, Shanghai 200050, China; 2 Graduate University of Chinese Academy of Science, Beijing 100049, China; 3 Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China  
  • Received:2011-07-11 Revised:2011-10-13 Online:2012-04-28 Published:2012-01-05
  • Contact: Si-Liang Gong E-mail:gongsiliang@126.com

摘要:

在传感器网络中,节点可信度常受到节点性能和环境的影响而呈现周期性变化,针对现有信任模型对这种情况的动态适应性不足,提出了一种基于混合高斯分布的传感器网络主观信任模型(MGSRM).该模型通过建立多个高斯分布函数对应被评价节点的多个可信度“状态”,提高了信任值计算的动态适应能力.使用当前信任值和综合信任值分别评测被评价节点的短期行为和长期行为,具有针对性和实用性.仿真分析表明,与已有信任模型相比,MGSRM模型在准确性、动态适应能力和学习记忆能力等方面具有优势.

关键词: 传感器网络, 主观信任模型, 混合高斯分布

Abstract:

The existing trust models for wireless sensor networks seems imprecise when the node credibility presents periodic changes which are affected by nodes’ performance and environment. For that, a subjective trust model based on mixed Gaussian distribution (MGSRM) is proposed. This model builds multiple Gaussian distributions corresponding to different “state”, improves dynamic adaptability of trust values. Current trust value and integrated trust value are separately used to reflect the node credibility under long-term and short-term behavior. A better focus and practicability is obtained. Simulation shows that, compared to existing trust models, MGSRM model has obvious advantages in accuracy, dynamic adaptability and learning ability.

Key words: sensor networks, subjective trust model, mixed Gaussian distribution

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