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基于信誉度集对分析的WSN安全数据融合

ISSN号：1000-1239
期刊名称：计算机研究与发展
时间：0
页码：1652-1658
分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]南京邮电大学计算机学院,南京210003, [2]南京人口管理干部学院信息科学系,南京210042
相关基金：基金项目：国家自然科学基金项目（60973139,60773041,61003039,61003236）;江苏省科技支撑计划（工业）基金项目（BE2010197,BE2010198）;江苏省高校自然科学基础研究基金项目（08KJB520006）;国家博士后基金项目（20090451240,20090451241）;江苏省博士后基金项目（0801019C）;江苏省六大高峰人才基金项目（2008118）;江苏省普通高校研究生科研创新计划基金项目（CX10B-197Z）
相关项目：基于Agent的无线多媒体传感器网络中间件技术研究

关键词：无线传感器网络, 分簇算法, 数据融合, 信誉度, 集对分析, wireless sensor networks, clustering algorithm, data aggregation, reputation, set pair analysis

中文摘要：

由于无线传感器网络存在资源约束问题，为了有效地减少无线传感器网络中的数据传输量以降低网络的总能耗，同时确保对感知数据进行融合操作的安全性，提出了一种基于传感器节点信誉度集对分析的安全数据融合方法．在节点分簇阶段，利用基于密度函数的减法聚类方法进行分簇，既获得了较快的分簇速度，又保证了簇头节点地理位置的合理分布，使得分簇规模更加符合节点的实际布设情况．在数据传输阶段，将簇头节点选择下一跳数据转发节点建模为多属性决策过程，综合考虑备选转发节点的信誉度、能量等属性信息，从中选择综合评价最优的簇头节点转发融合数据，不仅使网络中的数据流量分布更加均衡而且保证了数据的安全性．仿真结果表明，提出的数据融合算法在融合精度、安全性及簇头节点能耗方面与同类的LEACH算法和BTSR算法相比具有明显的优势．

英文摘要：

Wireless sensor networks is a kind of self-organizing networks which consist of large numbers of low-cost and low-power tiny sensor nodes that can communicate with each other to perform sensing, processing and storing sensed data cooperatively. In order to effectively reduce the amount of data transmission to cut down the overall energy consumption based on the strict energy limitations in wireless sensor networks, and simultaneously guarantee the security of sensed data aggregation, a secure data aggregation algorithm based on set pair analysis of sensor node reputations is presented. The employment of subtractive clustering method based on density function during the node clustering phase results in faster clustering speed, more reasonable cluster head distribution, and more preferable cluster size. In the phase of data transmission, the selection of next hop node is modeled as a multiple attribute decision making process. The networks data stream therefore obtains equilibrium and safety by virtue of the comprehensive evaluation of the multifold cluster head attributes （reputations, energy, etc） and the obtainment of optimal cluster head for relaying the aggregated data. Simulation results show that the proposed algorithm is superior to similar data aggregation algorithms such as LEACH algorithm and BTSR algorithm in aggregation precision, aggregation security, and cluster head energy consumption.