基于多终端协同的业务流控制

文章基于IP多媒体子系统(IMS)分层网络架构模型,设计了一种基于多终端的业务流控制系统构架。根据该系统架构和多终端协同的特征,文章提炼出基于多终端协同的业务流控制所面临的关键技术需求。满足这些需求可充分发挥网络与终端融合协同所带来的个人化、智能化业务能力,改善信息传输服务质量,为用户提供智能化业务体验。文章通过阐述两种典型的多终端协同应用场景——多网协作传输和移动性管理,说明了基于多终端协同的业务流控制所具有的重要意义。     

泛在末梢环境下均衡多业务的终端聚合机制

为了对异构终端进行有效聚合,解决泛在末梢环境中网络资源和终端能力有限性带来的多用户效益冲突分配问题,该文提出一种均衡多业务的终端聚合机制(DCMME)。首先以相对熵为均衡性指标设计了均衡服务质量效益函数,建立了面向多业务的终端聚合问题模型(MODC);然后,该机制对问题模型进行降维简化,提出了基于均衡的终端聚合算法(EDC);最后,通过C++和MATLAB仿真表明,在该机制下,用户均衡性改善了0.5%~20%,由此证明该方法很好地权衡和保证了多泛在业务请求下各用户效益。     

基于协同学的异构无线网络选择算法

终端接入的网络需要满足各类业务需求,对此提出了一种基于协同学的异构无线网络选择算法.该算法将终端网络选择看作一个系统化的问题,可选网络是一个复合系统,终端上运行的每一种业务都是一个子系统,依据熵的概念得到复合系统的熵值,熵值的大小反映了子系统之间的协同度,进而体现了网络的整体性能.仿真结果表明,在终端多业务的情况下,基于所提算法选择的网络业务QoS满意率达到了95％,比基于TOPSIS算法选择的网络业务QoS满意率提升了近4倍,能够很好地解决多业务终端网络选择问题.     

基于移动终端业务的多接入网络选择算法

本文提出了一种基于移动终端业务的多接入网络选择算法,该算法将网络选择的单位从终端转向业务,并通过如下三个步骤为业务确定一个最优接入网络集合：首先,通过充分考虑业务的实际QoS需求以及终端速度的影响,确定各决策因素的权重;其次,通过效用函数模型确定各接入网络的效用函数矩阵;最后,通过最优接入网络集确定适合业务传输的最优接入网络组合。仿真结果表明：本文提出的新算法能根据终端移动速度及业务需求为业务选择最优网络组合,同时能大幅度提高终端网络的吞吐量。     

基于移动终端业务的多接入网络选择算法

提出了一种基于移动终端业务的多接入网络选择算法,该算法将网络选择的单位从终端转向业务,并通过如下3个步骤为业务确定一个最优接入网络集合:首先,通过充分考虑业务的实际QoS需求以及终端速度的影响,确定各决策因素的权重;其次,通过效用函数模型确定各接入网络的效用函数矩阵;最后,通过最优接入网络集确定适合业务传输的最优接入网络组合.仿真结果表明:提出的新算法能根据终端移动速度及业务需求为业务选择最优网络组合,同时能大幅度提高终端网络的吞吐量.     

基于终端协同的并行多流速率分配算法研究*

为充分利用用户周围的网络资源以及终端资源,为用户提供更优质的用户体验,终端协同成为研究热点.针对基于终端协同的多流并行传输问题,提出了一种多属性决策的并行多流速率分配算法.进行从终端选择时,通过多属性决策算法综合考虑终端用户主观喜好,以及各终端及其接入网之间的客观差异.在速率分配的过程中,以各网络资源为约束,不断地进行速率调整.此算法在实现从终端选择的同时完成了各协同终端之间的速率分配.通过仿真后的性能分析表明,所提算法不仅能够充分利用优势网络的资源,还可满足网络资源的约束以及减少能耗,同时也能满足用户业务体验.     

FMC环境下的多终端绑定机制

随着固定移动融合网络和智能终端的高速发展,用户已经不满足于只通过一个终端获取业务,同一用户多个终端间的协同成为业界研究的重点.本文首先提出了一种基于网络侧的多终端绑定机制,通过这种绑定机制可以将FMC环境下的多个终端绑定起来构成一个虚拟终端,使接入固定网的终端和接入移动网络的终端联合起来为用户提供服务,实现多终端协同.为了解决绑定机制中的终端选择问题,本文构建了一个多属性加权数学模型,以辅助终端选择,并通过原型系统验证了所提出的绑定机制的有效性.     

基于用户偏好的异构网络选择算法研究

针对当前大多数网络选择算法没有充分考虑用户基于业务的网络偏好这一问题,提出了一种基于用户偏好的异构网络选择算法。该算法充分考虑业务需求、用户偏好及网络性能差异,通过层次分析法和距离分析法分别计算用户对各候选网络的主观和客观偏好权重,根据用户策略动态确定综合权重,并由此结合各属性权重计算网络性能,选出最优网络。仿真结果表明该算法可以准确选择接入网络,并当用户在网络边缘做兵乓运动时有效减少切换次数。     

基于群决策的多业务无线接入选择方法

在异构无线环境中,为了给运行了多个业务(群呼叫)的移动终端选择一个合适的无线接入网络,提出了一种基于群决策的接入选择方法。该方法引入效用函数对网络QoS及功耗进行评价;根据业务特点分配各属性的模糊权重,并采用业务优先权来反映各业务在群决策中的相对重要性;基于模糊理论,设计合理的模糊TOPSIS数据聚合模型,并在效用评价基础上实现了群决策。仿真结果表明,该方法能有效地实现多业务的无线接入选择,并且业务优先权在很大程度上影响了群决策结果。     

物联网多终端协同的应用场景及网络控制需求

在物联网环境下,随着终端技术的发展、用户拥有智能终端数量的增加、越来越多终端对近距离通信技术的支持,同一用户或不同用户终端之间,可以通过相互协同,提升单个用户终端的功能和性能,为用户提供更加智能化的业务体验。本文分析和列举了多终端协同的7种应用场景,并提炼出了3种多终端协同下的网络控制需求,为物联网多终端协同的网络控制技术研究奠定了基础。     

Capability adaptation algorithm based on joint network and terminal selection in heterogeneous networks

Modern service industry has been the new point of economic growth, which is on the basis of information and communication technologies. Heterogeneous networks convergence is the trend for the development of information and communication technologies. This paper proposes a capacity adaptation algorithm to implement the network and terminal collaborative selection functions. The AHP and GRA algorithms are introduced for heterogeneous network selection, in which the AHP algorithm is aimed to calculate the weight parameters and the GRA algorithm is to select the best target network. The AHP and SAW algorithms are adopted to complete the terminal selection on basis of terminal attributes. Simulations and analysis show that capability adaptation can achieve network and terminal selection in heterogeneous networks for many services and applications.     

Network selection based on multiple attribute decision making and group decision making for heterogeneous wireless networks

One of the key issues for radio resources management is network selection strategy in heterogeneous scenarios.In order to provide ubiquitous service,the paper puts forward a network selection algorithm based on multiple attribute decision making(MADM) and group decision making(GDM).Firstly,the proposed algorithm acquires attribute weights’ vectors of the subjective and objective decision makers based on MADM,and then the two attribute weights’ vectors are synthesized to be a new attribute weights’ vector by using GDM.Considering that the results of GDM should be reasonable and convincible,the criterion of consistency is adopted for judging the compatibility of group judgments.More specifically,the algorithm takes into account not only objective attributes of networks but also the preference of subscribers and traffic class.Hence it guarantees that the subscribers can not select the networks with poor performance depending on their preference.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide subscribers with satisfactory quality of service(QoS).     

Adaptive resource allocation framework for user satisfaction maximization in multi-service wireless networks

In order to capture and maintain a representative share of the wireless communication market, effective ways to manage the scarce physical resources of cellular networks are fundamental for cellular network operators. In this context, this paper proposes an adaptive Radio Resource Allocation algorithm that targets the user satisfaction maximization in cellular networks with multiple services. The proposed algorithm is mathematically derived from a utility-based cross-layer optimization framework and employs user weights as well as an innovative service weight that is adapted to meet the satisfaction target of the most prioritized service. Furthermore, the proposed algorithm is scalable to several services classes and can be employed in the current and future generations of wireless systems that guarantee orthogonality among the allocable resources. The performance evaluation is conducted in realistic scenarios of the downlink of an Orthogonal Frequency Division Multiple Access based cellular network serving video and Constant Bit Rate flows, where we assume imperfect Channel State Information at the transmitter. Significant gains in the joint system capacity were obtained, demonstrating that the adaptability and service prioritization allow the accomplishment of simultaneously maximizing the user satisfaction for distinct services.     

泛在网络下的群组移动模型研究现状及展望

群组移动模型的研究对泛在网络下通信协议的设计、算法性能的评价等问题具有重要意义。泛在网络涉及多种终端同时为移动用户服务的场景,有效地整合终端能力需要有效的群组移动模型进行指导。总结了以往Ad Hoc网络下各种群组移动模型的特点和适用场景,结合泛在网络下多终端多网络的环境特点,以用户和业务为中心,对个人服务场景中的群组移动模型的构建方案进行了探讨和展望,提出了关键的技术要求。     

An End-to-End Bandwidth Allocation Algorithm for Ad Hoc Networks

Bandwidth-guaranteed QoS service in ad hoc networks is a challenging task due to several factors such as the absence of the central control, the dynamic network topology, the hidden terminal problem and the multihop routing property. An end-to-end bandwidth allocation algorithm was proposed in [Lin and Liu, 15] to support the QoS service in ad hoc networks. However, without exploring the global resource information along the route, the performance of that algorithm is quite limited. In addition, it also incurs significant control overhead. We develop a new algorithm for end-to-end bandwidth calculation and assignment in ad hoc networks which utilizes the global resource information along the route to determine the available end-to-end bandwidth. Our method also employs the topology-transparent scheduling technology to reduce the control overhead. The proposed algorithm can be efficiently utilized in a distributed manner. Both theoretical analysis and simulation results show that our end-to-end bandwidth allocation scheme can significantly improve the network capacity compared with the existing method.     

Service community construction method of internet of things based on semantic similarity

Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.     

基于动态复合优先级的海上节点网络选择算法

针对海上移动节点完成业务时出现高优先级业务完成率低、业务拥堵率大、网络接入算法对波动环境适应性不足的问题,提出一种基于动态复合优先级的网络接入算法.首先,移动节点收集周围机动站点的网络属性参数,计算产生业务的执行紧迫性和剩余价值,再将不同类型业务的参数权重加入VIKOR法,形成最终的动态复合优先级,针对环境网络参数提供...     

基于SED的自适应负载均衡研究

负载均衡是提高集群系统性能的重要技术,负载均衡技术可以在当前的网络应用结构上有效提高服务器处理能力,使系统在现有的结构下能够满足更多的用户同时访问所提供的服务,负载均衡算法是集群任务分配的核心。分析了影响服务节点性能的主要因素,基于SED算法提出了一种负载均衡的自适应机制,根据服务节点的性能状况自动调整权值。在Linux平台下进行了实验,测试结果显示该算法优于静态算法。     

异构无线网络的自适应垂直切换判决算法

在未来的异构环境中,网络间的垂直切换将对QoS保证产生重要影响。针对移动终端在异构网络间切换不理想的问题,提出了一种自适应的垂直切换判决算法。采用基于用户多应用的代价函数对接入网络进行评估与选择,综合考虑移动终端当前的电池电量,判断当前业务是否需要进行网络切换,使移动终端能自适应地进行切换判决。仿真结果表明,该算法可以有效地延长移动终端的工作时间,减少乒乓效应,提高系统的切换性能,改善业务的QoS。     

基于语言值模糊关系的关联规则挖掘算法

针对模糊关联规则挖掘时隶属函数的确定困难以及区间划分边界过硬等问题,提出了模糊关系关联规则挖掘算法,确定了关系等级数目和相邻等级相似度,将语言表达式(事务的属性值)根据模糊运算规则映射到标签集的各个等级上得到等级权值。在这些权值的基础上定义了模糊关系支持度和置信度,阐述了算法的详细步骤,最后给出了算法在服务信任领域挖掘关联规则的应用过程。