计算机应用 ›› 2014, Vol. 34 ›› Issue (3): 700-703.DOI: 10.11772/j.issn.1001-9081.2014.03.0700

• 计算机安全 • 上一篇    下一篇

基于模糊预测的无线传感器网络信任模型

曹晓梅1,2,3,沈何阳4,朱海涛4   

  1. 1. Key Laboratory of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education, Nanjing Jiangsu 210003, China
    2. 江苏省无线传感网高技术研究重点实验室(南京邮电大学)，南京210003;
    3. 南京邮电大学 计算机学院，南京210003;
    4. 南京邮电大学 计算机学院，南京210003
  • 收稿日期:2013-09-25 修回日期:2013-11-22 出版日期:2014-03-01 发布日期:2014-04-01
  • 通讯作者: 沈何阳
  • 作者简介:曹晓梅（1974-），女，江苏无锡人，副教授，博士，主要研究方向：网络与信息安全；沈何阳（1989-），女，江苏盐城人，硕士研究生，主要研究方向：无线传感器网络安全数据融合；朱海涛（1989-），男，江苏镇江人，硕士研究生，主要研究方向：移动P2P网信誉管理。
  • 基金资助:

    国家973计划项目;国家自然科学基金资助项目;江苏高校优势学科建设工程资助项目

Reputation model based on fuzzy prediction in wireless sensor networks

CAO Xiaomei1,2,3,SHEN Heyang4,ZHU Haitao4   

  1. 1. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (Nanjing University of Posts and Telecommunications), Nanjing Jiangsu 210003, China;
    2. School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu 210003, China;
    3. 南京邮电大学 宽带无线通信与传感网技术教育部重点实验室，南京210003
    4. School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu 210003, China
  • Received:2013-09-25 Revised:2013-11-22 Online:2014-03-01 Published:2014-04-01
  • Contact: SHEN Heyang

PDF

摘要：

针对无线传感器网络(WSN)中的信任值更新问题，提出了一种基于模糊预测(FP)的无线传感器网络信任值更新的方法——RMFP。算法采用模糊数学理论方法，利用模糊隶属函数来全面地刻画节点的表现行为，并将其转换成节点的模糊隶属度，最后将模糊隶属度进行整合以实现节点的信任值更新。仿真实验表明，所提算法在整合节点信任值精确度方面提高了10.8%，在判断可疑节点的速度方面提高了两倍。这说明基于模糊预测的节点信任值更新算法在发现并摒除恶意节点的准确率和速度上均有显著的效果，尤其是针对前期取得高信任的恶意节点的判断具有很强的优势。

关键词: 无线传感器, 模糊预测, 信任模型, RMFP算法, 隶属度

Abstract:

In view of the update problem of the trust value in Wireless Sensor Network (WSN), a trust model based on Fuzzy Prediction (FP), called RMFP, was proposed. The behavior of nodes was described by using fuzzy mathematics theory method, and the fuzzy membership degree was converted by the fuzzy membership functions. Finally, the trust value was achieved by integrating the fuzzy membership degrees. The simulation results show that the accuracy of trust value is increased by 10.8%, and the judgment speed of suspected nodes is increased by two times. This shows that the effect on accuracy and speed of discovering, eliminating malicious node is more significant, especially for the judgment of the pre-made malicious nodes of high trust value.

Key words: Wireless sensor network, fuzzy prediction, trust model, RMFP algorithm, membership grade

中图分类号: 

版权所有 © 2021 四川计算机应用杂志社有限公司
蜀ICP备 05010208 号-3　新出网证(川)字026号
技术支持：北京玛格泰克科技发展有限公司
访问总数：
今日访问：