计算机网络的发展现状及网络体系结构涵义分析

自身存在种种缺陷与不足的传统网络体系结构，已不再适宜用来指导现今互联网向下一代网络的演进。有必要在正确认识当前计算机网络发展现状和重新审视网络体系结构涵义的基础上，展开对满足下一代网络发展需求的新一代网络体系结构的研究。为此，本文专门对以互联网为代表的计算机网络所面临的发展现状以及近年来计算机网络演进与发展的主要趋势进行了总结和分析，并重点对网络体系结构的涵义进行了全方位、多角度的深入剖析。主张在综合考虑网络体系结构的普适性、特指性、抽象性和过程性等特征的基础上，运用发展的眼光和系统的观点来全面认识和把握其本质，并提出了合理的“网络体系结构认知框架”。本文研究结论对新一代网络体系结构的研究有重要的参考价值和指导意义。     

新一代网络体系结构：需求目标、设计原则及参考模型

本文按照网络体系结构认知框架，遵循“需求目标→设计原则→具体实现”的合理研究思路，对满足下一代网络发展需求的新一代网络体系结构进行了研究。首先对下一代网络的概念进行了比较全面的分析，在此基础上明确地归纳了新一代网络体系结构的10条主要需求目标；然后通过总结、吸收相关领域的最新成果，研究提出了与这些需求目标相适应的若干新的网络体系结构设计原则；最后，以这些新的设计原则为指导，研究并建立起了满足下一代网络发展需求的新一代网络体系结构INSA参考模型，为满足下一代网络发展需求的新一代网络体系结构的研究提供了较好的思路并奠定了良好的基础。     

网络认知——从认知无线电到认知网络

认知无线电(Cognitive Radio)和认知网络(Cognitive Network)的提出和快速发展源于日益增长的网络可靠性、可用性和适应性要求。本文从当前网络面临的问题出发,首先介绍了认知无线电的提出背景和技术特点,并说明了认知网络的概念和内涵。然后,归纳了当前有关认知网络研究的相关研究工作。在此基础上,给出了一种通用的认知网络体系结构并对其资源管理方式进行了阐述。最后,总结了全文并展望了今后工作。     

基于混沌粒子群优化算法的认知网络流量分类方法研究

针对网络的接入形式以及网络应用日益复杂、异构和泛在等特点,当前网络所提供的服务质量QoS难以满足用户的需要。认知网络被认为是提高网络整体及端到端系统的性能、简化网络管理的新途径,是下一代通信网络发展的必然趋势。提出一种混沌粒子群优化算法(CPSO-BP),并将该算法应用于认知网络环境中对网络流量进行精确分类,以实现对以业务为中心的认知网络的可管可控。实验结果表明该方法能够充分结合粒子群优化算法的全局搜索特性和BP算法的局部搜索特性,并利用混沌搜索在小空间具有较强的局部搜索能力来提高分类精度,从而具有分类精度高、稳定性强的特点,能够有效地提高网络资源利用率,保证认知网络端到端QoS效能。     

泛在网络的Multi-Agent系统模型

未来的泛在网络是一个开放的巨复杂系统,面临局部自治性、异构性、复杂性、混沌性等挑战性问题.研究了泛在网络特性、多Agent逻辑系统语法,语义模型,将泛在网络抽象为由具体特定信念、期望、义务和能力等认知成分的Agent组成的认知系统.同时,设计了一种基于Multi-Agent的泛在网络体系结构,并通过举例形式针对泛在网络中移动性管理问题进行了基于多Agent的形式逻辑描述.为设计未来泛在网络环境下的大规模、复杂的网络服务和应用开辟一条新颖的途径.     

基于认知无线电的QoS路由算法

一般无线网络的路由协议不适合直接应用于认知无线电网络,需要提出一个新的路由算法以满足认知无线电网络端到端的服务质量(QoS)性能。认知无线电网络中的节点可以自主地进行信道选择和频谱切换,从而最大限度地满足流量需求和避免流内竞争。结合按需路由的基本流程,在充分考虑信道容量和流内竞争所带来的干扰对路由选择的影响下,提出了适用于认知无线电网络的基于容量和干扰的路由算法。仿真结果表明,基于容量和干扰的路由算法比另两种路由算法具有更好的端到端QoS性能。     

一种多接口认知传感器网络体系结构

传统的单接口无线传感器网络由于同道干扰问题的存在,网络性能受到极大的限制。多接口无线网络技术是解决这一问题的有效方案。但是多接口无线传感器网络的频谱接入问题是多接口传感网大规模部署的关键一环。而认知无线电技术能够实现频谱的动态访问,即便是信道拥塞的情况仍能有效改善网络性能,并且在无线传感器网络中表现出较好的传输特性。本文提出一种基于连通支配集的多接口认知传感器网络体系结构。通过分析不难看出,提出的体系结构具有良好的扩展性和容错性能。     

深入理解网络体系结构

从对网络体系结构的几种不同理解入手，采用麻省理工学院计算机科学和人工智能实验室(CSAIL)高级网络体系结构小组对网络体系结构的定义，结合Internet的发展历史，分析了网络体系结构、需求和技术之间的关系，总结出网络体系结构的特点，并指出体系结构设计在网络建设中的重要性，最后归纳了进行网络体系结构设计的方法。     

一种面向认知网络的QoS路由协议

随着网络应用的快速发展,传统网络面临诸多挑战,难以满足新型应用的服务质量QoS(Quality of Service)需求,网络管理变得极其复杂,迫切要求网络具备认知能力.文中提出了一种面向认知网络的QoS路由协议.为了满足不同应用的QoS需求,路由请求对应不同业务类型和服务等级,映射到相应的QoS参数.文中赋予路由节点感觉、活动感、记忆、查找、决策、执行、推理、解释等认知行为,设计了与这些行为相对应的协议报文,支持节点之间通过这些报文进行交互,从而具备认知能力.节点只收集和维护局部邻居和链路状态信息,通过使用和维护经验路段信息提高路由效率.文中对所提出的路由协议在原型系统上进行了实现,对协议的多项性能指标进行了评价.实验结果表明,文中所提出的路由协议是可行和有效的.     

基于信息熵的IP网端到端行为分析与建模

具有开放、分布式、不协作、异构、无中心控制等特点的Internet复杂巨系统的管理、容量规划、新一代网络体系结构设计与分析和性能预测都离不开对网络行为的充分理解。而端到端行为作为网络行为的一个重要组成部分,具有一定的研究价值。该文利用信息熵原理建立用于分析端到端整体宏观行为的信息熵模型,该模型能很好地反映端到端整体宏观行为与链路上各节点的状态概率之间关系,根据该模型可以分析端到端链路上各节点之间的相互作用关系以及它们是如何引起端到端整体宏观行为的。最后,给出了该模型的有效性和稳定性定量分析的判别式。     

Vulnerabilities in cognitive radio networks: A survey

Cognitive radio networks are envisioned to drive the next generation wireless networks that can dynamically optimize spectrum use. However, the deployment of such networks is hindered by the vulnerabilities that these networks are exposed to. Securing communications while exploiting the flexibilities offered by cognitive radios still remains a daunting challenge. In this survey, we put forward the security concerns and the vulnerabilities that threaten to plague the deployment of cognitive radio networks. We classify various types of vulnerabilities and provide an overview of the research challenges. We also discuss the various techniques that have been devised and analyze the research developments accomplished in this area. Finally, we discuss the open research challenges that must be addressed if cognitive radio networks were to become a commercially viable technology.     

Fuzzy-logic-based channel selection in IEEE 802.22 WRAN

IEEE 802.22 WRAN standard does not specify algorithm for selecting operating channel from backup channel list and prioritizing the backup and candidate channels. Because all the channels in the backup and candidate channels list are not with the same properties, selecting channels randomly from the list may decrease the networks performance. We propose an approach of ranking channel according to behavior of primary users on the channel and RSSI value. It utilizes fuzzy-logic-based algorithm for prioritizing channels in the backup and candidate channels list. Performances of the networks with fuzzy-logic-based channel selection and random channel selection are compared.     

ML aided context feature extraction for cognitive radio

This paper addresses the estimation of different context features of a primary user network, such as transmitters’ positions, antenna patterns and directions, and propagation model characteristics. It is based on radio signal strength measurements obtained by a sensor network without any prior knowledge about the configuration of the primary transmitters in terms of antenna types or propagation model. A Maximum Likelihood Aided Context Feature Extraction (MLACFE) method is introduced based on applying image processing and a Maximum Likelihood estimation algorithm over the set of measurements to identify the existing transmitters in the scenario and their parameters. The proposed method can provide a quite similar performance than a classical ML method, in terms of average estimation errors while at the same time reducing the computation time in about three orders of magnitude, for the considered case study.     

Using online cognitive tasks to predict mathematics low school achievement

This study aimed to analyze the validity of an online cognitive screening battery to predict mathematic school achievement using artificial neural networks (ANNs). The tasks were designed to measure; selective attention, visuo-spatial working memory, mental rotation, and arithmetic ability in an online, game-like format. In the first study, we investigated the cognitive performance of students with low and typical achievement in mathematics and language. In the second study, we developed an ANN to classify mathematics school achievement. Finally, we tested the adequacy of this network to classify an unknown sample to the ANN. Most of the performance differences in the battery were related to mathematics achievement. The ANN was able to predict mathematics achievement with acceptable accuracy and presented equivalent results in a simulation involving a different sample. We suggest that this assessment model combining ANNs and online cognitive tasks may be a valuable tool to research low school achievement in school settings.     

Development of a computer game-based framework for cognitive behaviour identification by using Bayesian inference methods

In this work a novel technique for cognitive behavioural data acquisition via computer/console games is introduced by which the user feels more relax than s/he is in a formal environment (e.g., labs and clinics) and has less disruption as s/he provides cognitive data sequence by playing a game. The method can be adapted into any game and is based on the assumption that in this way more efficient analysis of mind can be made to unveil the cognitive or mental characteristics of an individual. In experiments of the proposed work a commercial console game was utilised by different users to complete the tasks in which each game player followed his/her own optional scenarios of the game for a certain period of time. The attributes were then extracted from the behavioural video data sequence by visual inspection where each one corresponds to user’s behavioural characteristics spotted throughout the game and then analysed by the Bayesian network utility. At the end of all the experiments, two types of results were obtained: semantic representation of behavioural attributes and classification of personal behavioural characteristics. The approach is proved to be a unique way and helped identify general and specific behavioural characteristics of the individuals and is likely to open new areas of applications.     

Learning and motivational couplings promote smarter behaviors of an animat in an unknown world

This paper reports on statistical results of an animat behavior in an unknown environment using a cognitive map. We study the coupling effect between the motivation for eating and the one for drinking. Some smart behaviors are not caused by a sophisticated “intelligent” algorithm, but only through coupling of the motivations. Adding a learning rule on the links of the cognitive map allows to reinforce particular paths, and to forget others. This also leads to reinforce the same “smart” behaviors.     

认知无线Mesh网络中基于概率的贪婪频谱决策技术研究

认知无线电网络传统的频谱决策方法中次用户根据不同的判据选择信道,会引起信道竞争和拥塞。针对认知无线Mesh网络中基于概率的频谱决策方法,提出一种贪婪信道选择算法,当发生频谱切换时,其结合改进的抢占优先(相似文献   

基于SC-FDMA的宽带认知无线电网络中最优功率分配的研究

研究了基于单载波频分多址(SC-FDMA)的宽带认知无线电网络中非授权用户(NU)的最优功率分配(OPA)问题.首先给出了基于SC-FDMA的宽带认知无线电网络中非授权用户对授权用户的干扰功率模型.在此基础上,分别在下述两种约束条件下利用凸优化理论推导出了两种使非授权用户的和速率最大的最优功率分配(OPA)算法.约束条件一为:任一非授权用户的任一子载波对授权用户的干扰功率受限(IPCs);约束条件二为:所有非授权用户的所有子载波对授权用户的干扰功率受限(IPC).仿真结果表明,与等功率分配(EPA)和传统最优功率分配(C-OPA)算法相比,所提出的算法显著提高了非授权用户的可达和速率.     

RAC: Range Adaptive Cognitive Radio Networks

In recent years, cognitive radio has received a great attention due to tremendous potential to improve the utilization of the radio spectrum by efficiently reusing and sharing the licensed spectrum bands, as long as the interference power inflicted on the primary users of the band remains below a predefined threshold level. Cognitive radio allows the secondary users in the cognitive radio network to access the licensed spectrum of the primary users opportunistically. In this paper, an autonomous distributed adaptive transmission range control scheme for cognitive radio networks which is called the RAC is proposed. The RAC considers the QoS requirements of both the primary and the secondary users simultaneously. The cognitive user's maximization of its achievable throughput without interfering the primary user by adapting transmission range of the secondary users dynamically is the key feature of the RAC. One of the advantages of using the proposed scheme is its implementation simplicity. The RAC is compared to other cognitive radio schemes in a simulation environment by using ns2. Simulations indicate that, the RAC can well fit into the mobile cognitive radio ad hoc networks and improve the network performance. Having compared to the other schemes utilizing contemporary cognitive radio technology, the RAC provides better adaptability to the environment and maximizes throughput and minimizes data delivery latency.     

基于POMDP的认知无线网络次用户多时隙信道选择算法

为了最大化认知无线网络次用户系统的吞吐量,同时减少信道选择过程中的计算量,在多条主用户信道的次用户多时隙系统中,用半马尔科夫链POMDP(Partially Observable Markov Decision Process)理论刻画次用户信道选择过程,并提出对应的认知无线网络信道选择算法。在每一个时隙开始时,次用户选择部分信道进行感知,之后根据感知结果选择不同的传输功率接入信道,对于没有被感知的信道,次用户可以直接接入。对POMDP问题求解给出最优策略、次优策略。通过在不同仿真环境下对最优策略、次优策略以及任意策略进行数值分析比较,论证了该设计算法的有效性。