基于能量优化的无线传感器网络安全路由算法

针对无线传感器网络路由面临安全威胁和节点能量有限的不足,提出一种基于能量优化的安全路由算法(EOSR).该算法把优化能量、提高路由安全性和缩短传输时延同时作为设计目标,采用多目标决策,在保证安全性和快速传输的同时,让能量储备较多的节点承担较多的数据转发任务,可获得最优路由和延长网络生命期.通过预置公私密钥对,有效地提高了路由的安全性.给出了该算法中路由发现、路由选择和路由删除的具体步骤,通过仿真实验证明该算法的有效性.     

基于多维度网络安全行为的信任评估模型

提出了一种基于多维度网络安全行为的动态信任评估模型,该模型通过在现有的身份认证、网络安全、终端安全等各类系统中采集信任指标数据,并对指标数据进行综合的处理和评估,帮助应用系统从各种角度评估网络实体的可信度,并且根据信任度评定相应的信任等级,最终实现为用户分配动态的权限,保证用户访问应用业务系统的数据安全性。该模型在信任度评估方面具有动态、高效和稳定的特点,使用该多维动态评估方法的应用系统能够实现对用户的操作安全性和合法性进行持续动态的高效评估,并根据评估结果,为用户分配动态的数据访问权限,从而解决复杂网络环境数据安全的问题。     

基于IPSec的网络安全实现

因特网(Internet)是全球最大的、开放的、由众多网络互连而成的计算机网络，TCP／IP协议族是实现网络连接性和互操作性的关键。TCP／IP协议的开放性促进了Internet的发展和普及，但由此带来的网络安全问题正日益突出。为了保护国家公众信息网和企业内部网及外联网信息和数据的安全，必须大力发展网络安全技术。     

基于IPSec的网络安全实现

因特网(Internet)是全球最大的、开放的、由众多网络互连而成的计算机网络．TCP／IP协议旅是实现网络连接性和互操作性的关键。TCP/IP协议的开放性促进了Internet的发展和普及，但由此带来的网络安全问题正日益突出。为了保护国家公众信息网和企业内部网及外联网信息和数据的安全，必须大力发展网络安全技术。     

基于防火墙的网络安全实现

阐述了当前网络安全所面临的严峻问题，并就此提出了采用防火墙来实现网络安全的方法，叙述了防火墙的原理及体系结构，并介绍了几种防火墙的方案。     

基于零信任的网络安全架构研究与应用

随着网络攻击演变得更加复杂高端,电信运营商系统及数据安全的压力越来越大,传统边界安全理念先天能力存在不足,导致安全边界不断被迫重构。运用零信任安全理念,打破信任和网络位置的默认绑定关系,通过强身份验证技术保护数据,降低资源访问过程中的安全风险,防止数据泄露,限制网络内部横向移动,实现数据端到端安全访问的方法。     

基于加权信任向量的混合结构式P2P网络信任模型

为了解决非结构化P2P网络信任模型的全局信任度的迭代计算和结构化P2P网络信任模型的网络规模不易扩展性的缺点,设计了基于加权信任向量的混合结构式P2P网络信任模型(简称W-TPP),它采用信任向量来存储历史经验数据,利用结构化网络中的节点来分布式存取全局信任向量表.并且为了解决历史经验数据的时间有效性问题,提出一种新的节点信任值算法-加权信任向量算法.通过对W-TPP信任模型的性能分析和模拟仿真试验,验证了该信任模型具有可扩展性,降低了查询全局信任度所占的网络资源.     

基于低功耗及加权优化的BIST测试生成器设计实现

测试生成器TPG（Tesl Panern Generation）的构造是BIST（Built—In Self-Test）测试策略的重要组成部分。文章结合加权伪随机测试原理及低功耗设计技术，提出了一种基于低功耗及加权优化的BIST测试生成器设计方案。它根据被测电路CUT（Circuit Under Test）各主输入端口权值构造TPG，在对测试序列优化的同时达到降低功耗的目的。仿真结果验证了该方案的可行性。     

基于混沌加密的无线传感器网络安全技术

在当今社会中,计算机网络已经深入到社会的各个方面,无线传感器网络应用也越来越加普及,对人们的生活、国家安全以及其他各行各业都产生了极大影响,确保无线传感器网络安全是十分必要的,本文就针对以混沌加密为基础的无线传感器网络安全技术展开研究。     

无线传感器网络系统综述

介绍了无线传感器网络的概念、几种主要的无线传感器网络协议，尤其是MAC层协议的研究情况，给出了一些比较经典的无线传感器网络MAC协议的思想。     

基于模糊理论的无线传感器网络数据融合算法

针对无线传感器数据融合过程中,各节点观测值存在冗余以及监测区域重叠可能导致信息的精确性低、能量消耗大等问题,本文提出利用模糊理论中的相关性函数计算节点间相互支持程度,对支持程度高的传感器进行数据融合,并利用融合结果与服务质量期望筛选出冗余节点,使其进入休眠状态.仿真结果表明,该方法能够获得更高的精度和可靠性,并能有效延长网络生命周期.     

基于自适应按需加权的传感器网络分簇算法

基于LEACH算法的不确定特性将会导致某些节点过快耗尽电池能量而缩短系统寿命,提出了一种基于自适应按需加权的分簇算法.通过设定权值,将节点的节点度,与邻节点的平均距离以及节点的剩余能量考虑在内,从而保证综合性能最好的节点成为簇头.仿真结果表明,改进后的算法在网络寿命、负载平衡程度以及节点充当簇头的公平性指数上均比LEACH算法有了很大提高,有利于提高WSN的能量利用率.     

基于分区的无线传感网节点复制攻击检测方法

 无线传感器网络部署在敌方区域时,节点可能被俘获,其信息被复制并散布到网络中进行破坏活动.这种攻击隐蔽,破坏力较强.本文提出了基于分区的节点复制攻击检测方法,通过将部署区域分区,并建立基于跳数的坐标,可有效检测节点复制攻击.仿真实验表明:本方法耗能少,效率高且无需辅助节点.     

基于神经网络的自组织传感器网络设计

多传感器目标跟踪是信息融合的一个重要研究内容。尽管已经有许多的融合算法,但目前对跟踪传感器的配置问题研究还很少,而这对于设计一个成功的UGS网络系统是必要的。本文设计了一种神经元阈值可调的自适应Hopfield网络,可以自组织地从整个网络中选取合适数目的传感器组成跟踪器,使整个系统的精度足够高,而使用的传感器数目尽可能少。仿真显示了算法的有效性。     

基于局部联系的无线传感网络异常入侵检测

提出一种局部联系对比搜索算法.通过把节点刷新定位过程,与其相邻的小范围分布网络的均值特征节点做比较,利用局部无线网络节点最优信息,检测异常入侵节点信息,避免了传统集中式方法对全部节点搜索的耗时.实验证明,这种局部联系对比定位算法能够有效利用网络信息,对异常节点实现准确入侵检测,缩短了检测时间.     

基于局域世界的WSN拓扑加权演化模型

 无标度加权网络模型,反映了现实网络的存在形式和动力学特征,是无线传感网络建模和拓扑演化的有效研究工具.本文基于局域世界理论提出一种不均匀成簇的无线传感网络拓扑动态加权演化模型,考虑节点能量,通信流量和距离等因素,对边权重和节点强度进行了定义,同时研究了拓扑生长对边权重分布的影响.实验证明演化所得网络节点度,强度和边权重均服从幂律分布,结合已有理论成果可知,该拓扑不仅继承了无权网络较高的鲁棒性和抗毁性,同时降低了节点发生相继故障的几率,增强了无线传感网络的同步能力.     

加权选举的无线传感器网络协议

为了提高无线传感器网络的稳定期,提出了一种高效节能的加权选举协议。该协议使用集群策略结合链状路由算法,在异构的无线传感器网络环境下改善节能并且延长稳定期。仿真结果表明该协议在网络寿命和稳定期方面的性能都优于LEACH,SEP和HEARP。另外,实验表明在异构环境下稳定期依赖于节点的额外能量。     

无线传感网络布局的虚拟力导向微粒群优化策略

无线传感网络通常由固定传感节点和少量移动传感节点构成,动态无线传感网络布局优化有利于提高无线传感网络覆盖率和目标检测概率,是无线传感网络研究的关键问题之一.传统的虚拟力算法在优化过程中容易受固定传感节点的影响,无法实现全局优化.本文结合虚拟力算法和微粒群算法,提出一种面向无线传感网络布局的虚拟力导向微粒群优化策略.该策略通过无线传感节点间的虚拟力影响微粒群算法的速度更新过程,指导微粒进化,加快算法收敛.实验表明,虚拟力导向微粒群优化策略能快速有效地实现无线传感节点布局优化.与微粒群算法和虚拟力算法相比,虚拟力导向微粒群优化策略不仅网络覆盖率高,且收敛速度快,耗时少.     

Parametric Probabilistic Routing in Sensor Networks

Motivated by realistic sensor network scenarios that have mis-in-formed nodes and variable network topologies, we propose an approach to routing that combines the best features of limited-flooding and information-sensitive path-finding protocols into a reliable, low-power method that can make delivery guarantees independent of parameter values or information noise levels. We introduce Parametric Probabilistic Sensor Network Routing Protocols, a family of light-weight and robust multi-path routing protocols for sensor networks in which an intermediate sensor decides to forward a message with a probability that depends on various parameters, such as the distance of the sensor to the destination, the distance of the source sensor to the destination, or the number of hops a packet has already traveled. We propose two protocol variants of this family and compare the new methods to other probabilistic and deterministic protocols, namely constant-probability gossiping, uncontrolled flooding, random wandering, shortest path routing (and a variation), and a load-spreading shortest-path protocol inspired by (Servetto and Barrenechea, 2002). We consider sensor networks where a sensor’s knowledge of the local or global information is uncertain (parametrically noised) due to sensor mobility, and investigate the trade-off between robustness of the protocol as measured by quality of service (in particular, successful delivery rate and delivery lag) and use of resources (total network load). Our results for networks with randomly placed nodes and realistic urban networks with varying density show that the multi-path protocols are less sensitive to misinformation, and suggest that in the presence of noisy data, a limited flooding strategy will actually perform better and use fewer resources than an attempted single-path routing strategy, with the Parametric Probabilistic Sensor Network Routing Protocols outperforming other protocols. Our results also suggest that protocols using network information perform better than protocols that do not, even in the presence of strong noise. Christopher L. Barrett is leader of the Basic and Applied Simulation Science Group of the Computing and Computational Sciences Division at Los Alamos National Laboratory. His Group is a simulation science and technology (S&T) invention organization of 30 scientists devoted to providing large-scale, high performance methods for systems analysis and simulation-based assisted reasoning. His Group engages in fundamental mathematical, algorithmic, and complex systems analysis research. Current applied research is focused on interdependent simulation and analysis tools for complex, socio-technical systems like transportation, communications, public health and other critical infrastructure areas. His scientific experience is in simulation, scientific computation, algorithm theory and development, system science and control, engineering science, bio-systems analysis, decision science, cognitive human factors, testing and training. His applied science and engineering achievements include, for example, development of large-scale, high performance simulation systems (e.g., Transportation Analysis Simulation System, TRANSIMS) and development of a distributed computing approach for detailed simulation-based study of mobile, packet switched digital communications systems (Self Organizing Stochastic Rebroadcast Relay, SORSRER). He has a M.S. and Ph.D. in Bio-information Systems from California Institute of Technology. He is a decorated Navy veteran having served in both the submarine service and as a pilot. He has been awarded three Distinguished Service Awards from Los Alamos National Laboratory, one from the Alliance for Transportation Research, one from the Royal Institute of Technology, Stockholm, and one from Artificial Life and Robotics, Oita University, Japan. Stephan J. Eidenbenz is a technical staff member in the Basic and Applied Simulation Science group (CCS-5) at Los Alamos National Laboratory (LANL). He received an M.Sc. in Computer Science from the Swiss Federal Institute of Technology (ETH) in Zurich in 1997 and a Ph.D. in Computer Science from ETH in 2000; he also obtained a Bachelor’s degree in business administration from GSBA in Zurich in 1999. Stephan has worked for McKinsey & Co. in Switzerland, where he received training in business administration and microeconomics. He has held a postdoctoral position at ETH and he has been a postdoctoral fellow at LANL. Stephan’s more than 30 publications cover a wide range of subjects such as approximability and inapproximability properties of visibility problems in polygons and terrains, error modeling in sequencing problems for computation biology, and designing communication protocols robust against selfish behavior. His current research interests include selfish networking, algorithmic game theory, network modeling and simulation, network design, and network optimization. Lukas Kroc is a student of M.Sc. program in Computer Science at Charles University in Prague. In 2003, he was a Graduate Research Assistant at the Basic and Applied Simulation Science group (CCS-5) at Los Alamos National Laboratory. His research interests include simulation, wireless networking and artificial intelligence. Madhav V. Marathe is a Team Leader for Mathematics and Computer Science in the Basic and Applied Simulation Science group, Computer and Computational Sciences (CCS-5) at the Los Alamos National Laboratory. He obtained his B.Tech in 1989 in Computer Science and Engg. from IIT Madras, India and his Ph.D. in 1994 in Computer Science, from University at Albany. His team focuses on developing mathematical and computational tools for design and analysis of large scale simulations of socio-technical and critical infrastructure systems. His research interests are in modeling and simulations of large socio-technical systems, design and analysis of algorithms, computational complexity theory, theory of parallel, distributed and mobile computing and communication systems. He has published over 100 research articles in peer reviewed journals and conferences. He is an adjunct faculty in the Computer Science Department at the University of New Mexico. James P. Smith is a technical staff member in the Basic and Applied Simulation Science Group of the Computing and Computational Sciences Division at Los Alamos National Laboratory. His principal interest is in high performance computing applied to modeling, simulation and analysis of socio-technical systems. His current research applies to national infrastructure, especially telecommunication/computing, public health, and transportation. He has scientific experience in high performance computing and parallel processing applied to large-scale microscopic simulations, including original software design and debugging of very large, evolving systems of inter-operable computational systems, and efficient analysis and synthesis of massive data produced by multi-scale complex environments. Before attending graduate school he worked for a short time in nuclear theory, and had several publications in experimental biophysics from the Pennsylvania Muscle Institute and Bockus Research Institute. During graduate school he took a one year hiatus to start a company to work in analytic finance, and then spent time doing theoretical space physics at LANL. His graduate work eventually included theoretical and experimental fusion research, but concentrated on computational space plasma physics. He has publications in biophysics, analytic finance, education, space plasma physics and computer science, and is a co-inventor on the TRANSIMS patent. He has a Ph.D. in Theoretical Plasma Physics from the University of Texas at Austin.This revised version was published online in August 2005 with a corrected cover date.