基于混合高斯分布的传感器网络主观信任模型  

Mixed Gaussian Based Subjective Trust Model for Sensor Networks

作　　者：巩思亮[1,2] 邢涛[1] 熊永平[3] 马建[3] 

机构地区：[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

出　　处：《北京邮电大学学报》2012年第2期46-49,共4页Journal of Beijing University of Posts and Telecommunications

基　　金：国家科技重大专项项目(2009ZX03006-003);国家重点基础研究发展计划项目(2011CB302901)

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.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.

关 键 词：传感器网络 主观信任模型 混合高斯分布 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]