车辆异构网络中预测垂直切换算法

在车辆异构网络中,针对垂直切换决策时刻之后网络状态的动态变化,提高切换性能问题,提出一种基于马尔可夫过程的预测垂直切换(M-VHO)算法。算法考虑了切换决策后网络状态的动态变化对车辆终端服务质量(QoS)的影响。其基本思路是：在需要垂直切换时,利用马尔可夫过程的转移概率预测未来网络状态的变化;另外,采用模糊逻辑方法确定评价属性参数权重;最后,比较切换决策、切换执行和切换之后时刻的总收益来优化选择最佳切换网络。仿真结果证明,该算法在确保较高负载均衡的情况下,可有效改进车辆终端的平均阻塞率及丢包率,降低乒乓效应,确保了车辆终端的QoS。     

衰落信道下基于中继选择与功率控制的节能协作通信

In order to save energy and make more efficient use of wireless channel, this article puts forward an energy saving cooperative relaying scheme which actuates the cooperative transmission only when the feedback from the destination indicates failure of the direct transmission. The proposed scheme selects the optimal relay and its corresponding transmission power in each time slot based on channel condition and residual energy with the objective of minimizing energy consumption and extending network lifetime. In the study, the finitestate Markov channel model is used to characterize the correlation structure of channel fading in wireless networks, and the procedure of relay selection and transmission power decision is formulated as a Markov decision process. Numerical and simulation results show that the proposed scheme consumes less energy and prolongs the network lifetime.     

Heterogeneous Network Selection Optimization Algorithm Based on a Markov Decision Model

A network selection optimization algorithm based on the Markov decision process(MDP)is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment.Considering the different types of service requirements,the MDP model and its reward function are constructed based on the quality of service(QoS)attribute parameters of the mobile users,and the network attribute weights are calculated by using the analytic hierarchy process(AHP).The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network,and the MDP model is solved by using the genetic algorithm and simulated annealing(GA-SA),thus,users can seamlessly switch to the network with the best long-term expected reward value.Simulation results show that the proposed algorithm has good convergence performance,and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.     

基于S4框架的并行复杂事件处理系统

针对复杂事件处理技术在单机的吞吐量瓶颈,而现有通用并行框架不适合复杂事件处理系统的问题,在分析现有并行技术的基础上,设计了14种复杂事件处理操作符,提出了以操作符为单位并行的复杂事件处理系统框架,给出了基于操作符的事件流负载分流方法。并基于S4系统实现了并行复杂事件处理系统,提供比单机运行更高的可靠性和吞吐率。通过集群上的实验证明,在一定范围内,该系统的吞吐量可随集群节点数增加而线性增长。     

基于变电站的入侵防御网络安全评估模型

网络攻击是最重要的变电站安全问题之一,为了更好地识别易受攻击的变电站,首先需要对侦察活动进行建模。在此基础上研究相应的网络攻击和防御策略。利用马尔可夫决策过程建立了变电站控制权竞争中的入侵防御模型。在模型中考虑了目标变电站、入侵者和防御者的关键特征。因此,通过求解模型,可以得到入侵者和防御者的最优策略。通过这些策略可以对变电站的网络安全状态进行评估。仿真实验结果验证了所提出的模型以及相应策略的可行性。     

一种无线传感器网络中目标跟踪的自适应节点调度算法

在无线传感器网络目标跟踪的过程中进行节点调度,可以综合考虑跟踪误差和能量消耗,延长传感器网络的使用寿命。为了综合考虑节点调度的短期和长远损失,该文将问题建模为部分可观测马尔科夫决策过程(POMDP)以得到更优的调度策略,并提出一种近似求解算法C-QMDP。该算法利用马尔科夫链蒙特卡洛方法(MCMC)推导连续状态空间的置信状态的转移,并计算瞬时代价。使用状态离散化方法,基于马尔科夫决策过程(MDP)值迭代求解未来代价的近似值。仿真结果表明,相比现有POMDP近似算法,该文算法既可以降低跟踪过程中的累积损失,又可以将大量运算进行离线计算,减小了在线决策时的计算量。     

Speech recognition algorithm based on neural network and hidden Markov model

This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model (HMM) and artificial neural network (ANN). First, the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second, with the probability score as input to the neural network, the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally, Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of neural network, the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover, the hybrid model enhances the individual flaws of the HMM and the neural network and greatly improves the speed and performance of speech recognition.     

Lightweight opportunistic routing forwarding strategy based on Markov chain

A lightweight opportunistic routing forwarding strategy (MOR) was proposed based on Markov chain.In the scheme,the execute process of network was divided into a plurality of equal time period,and the random encounter state of node in each time period was represented by activity degree.The state sequence of a plurality of continuous time period constitutes a discrete Markov chain.The activity degree of encounter node was estimated by Markov model to predict its state of future time period,which can enhance the accuracy of activity degree estimation.Then,the method of comprehensive evaluating forwarding utility was designed based on the activity degree of node and the average encounter interval.MOR used the utility of node for making a routing forwarding decision.Each node only maintained a state of last time period and a state transition probability matrix,and a vector recording the average encounter interval of nodes.So,the routing forwarding decision algorithm was simple and efficient,low time and space complexity.Furthermore,the method was proposed to set optimal number of the message copy based on multiple factors,which can effectively balance the utilization of network resources.Results show that compared with existing algorithms,MOR algorithm can effectively increase the delivery ratio and reduce the delivery delay,and lower routing overhead ratio.     

分布式环境下基于马尔科夫链的图流三角近似计算

图三角数量的计算是计算网络聚集系数和传递性的重要步骤.大数据背景下,以采样为策略的近似计算成为图三角计算的主要方法,然而此类方法面临时空消耗和计算错误性两大难题.本文提出了一种针对图流的基于马尔科夫链的图三角近似计算算法,该算法以窗口作为图流处理单位,将马尔科夫链与采样相结合,保证降低错误率的同时实现动态适应内存空间的变化.实验显示,相较其他三角形近似计算算法,该算法在错误率上降低2~4倍,时间消耗上也有很大改进.     

基于分时MDP的出租车载客预测推荐技术研究

针对出租车盲目寻客导致空载率高的问题,提出了一种出租车载客热点推荐策略,以最大程度优化匹配乘客过程,提高寻客效率.基于出租车历史轨迹数据,结合热点乘客信息的时间序列特性,提出基于循环神经网络的分段预测(SPBR)算法,以及基于分时马尔可夫决策过程(TMDP)的载客推荐模型.实验表明,SPBR算法预测结果的RMSE比SV...     

Power allocation and transmission scheduling for a network with bidirectional relaying links

In this paper, we study packet transmission scheduling for a network with bidirectional relaying links, where the relay station can use network coding to combine packets to multiple receivers and opportunistically decide the number of packets to be combined in each transmission. Two cases are considered, depending on whether nodes are allowed to overhear transmissions of each other. A constrained Markov decision process is first formulated with an objective to minimize the average delay of packet transmissions, subject to the maximum and average transmission power limits of the relay node. The complexity for solving the constrained Markov decision process (MDP) is prohibitively high, although the computational complexity for the no‐overhearing case can be greatly reduced. Heuristic schemes are then proposed, one applies to the general case, and another applies to only the no‐overhearing case. Numerical results demonstrate that the heuristic schemes can achieve close‐to‐optimum average packet transmission delay, and furthermore, the second scheme achieves lower maximum delay while keeping the same average packet transmission delay and relay node power consumption as the first one. Copyright © 2015 John Wiley & Sons, Ltd.     

基于深度Q网络的在线服务功能链部署方法

针对5G网络资源状态动态变化和网络模型高维度下服务功能链部署的复杂性问题,该文提出一种基于深度Q网络的在线服务功能链部署方法(DeePSCD)。首先,为描述网络资源动态变化的特征,将服务功能链部署建模成马尔可夫决策过程,然后,针对系统资源模型的高维度问题采用深度Q网络的方法进行在线服务功能链部署策略求解。该方法可以有效描述网络资源状态的动态变化,特别是深度Q网络能有效克服求解复杂度,优化服务功能链的部署开销。仿真结果表明,所提方法在满足服务时延约束条件下降低了服务功能链的部署开销,提高了运营商网络的服务请求接受率。     

自适应模糊决策树算法在数据流挖掘中的应用

在网络的许多应用中数据是以流的形式存在的,例如网络流、传感器数据,以及网页点击流等,分析和挖掘这类数据,可以发现某中有价值的信息。在此,针对数据流挖掘算法中出现的一些问题（如概念漂移问题）,提出了一种自适应模糊决策树的优化算法。该算法对于解决处理数据流概念中的漂移问题有较好的效果。     

基于活跃度的分级映射解析系统

针对当前映射解析系统存在的映射解析时延过高的问题,该文依据终端的活跃程度,提出一种基于活跃度的分级映射解析系统。该系统将通信对端的身份位置映射信息划分为活跃级、中性级和稳定级3个等级,并据此建立了一种3层的映射解析存储架构,映射副本可根据自身活跃度的变化在3层之间动态调整存储位置。为最小化映射解析时延,在系统构建过程中,针对传统DHT构建方式存在的非位置感知问题,将系统构建过程建模为马尔科夫决策过程,并提出一种马尔科夫决策构建算法用于求解该模型。仿真结果表明,该系统能够显著降低映射解析时延和提升路由性能,对网络结构的动态变化具有良好的适应性。     

基于多核MIPS64芯片的综合安全网关设计与实现

针对当前安全设备在面对日益复杂的多形态网络行为和攻击应用上存在的低效率和低功能,提出一种面向下一代网络的高性能、分层次、并行处理的多功能综合安全网关体系结构。该体系结构以多核MIPS64安全处理芯片为基本核心模块,采用X86作为资源调度管理,以MIPS64cavium芯片内嵌的针对TCP数据包深度检查和模式识别的硬件完成网络数据流的高速检测,以PCI express X16总线实现线速处理网络数据流量,以双通道DDR2和Ultra320SCSI完成海量数据流的内部处理和存储。实际测试表明新型安全网关具有极好的效果。     

Heuristic RAT selection policy to minimize call blocking probability in next generation wireless networks

In next generation wireless network (NGWN) where multiple radio access technologies (RAT) co‐exist, a joint call admission control (JCAC) algorithm is needed to make a RAT selection decision for each arriving call. RAT selection policy has a significant effect on the overall new call blocking probability in the network. We propose a heuristic RAT selection policy to minimize new call blocking probability in NGWN. The proposed JCAC scheme measures the arrival rate of each class of calls in the heterogeneous wireless network. Based of the measured values of the arrival rates and using linear programming technique, the JCAC scheme determines the RAT selection policy that minimizes overall call blocking probability in the heterogeneous wireless network. Using Markov decision process, we develop an analytical model for the JCAC scheme, and derive new call blocking probability, handoff call dropping probability (HCDP), and call incompletion probability (CIP). Performance of the proposed scheme is compared with the performance of other JCAC scheme. Simulation results show that the proposed scheme reduces new call blocking probability, HCDP, and CIP in the heterogeneous wireless network. Copyright © 2009 John Wiley & Sons, Ltd.     

基于马尔可夫决策的理性秘密共享方案

基于马尔可夫决策理论研究理性密码共享系统模型和秘密重构方法。首先利用马尔可夫决策方法,提出适合于理性秘密共享的系统模型,该模型包括参与者集合、状态集合、风险偏好函数、状态转移函数、回报函数等。在模型中,引入秘密重构中的参与者的风险偏好函数刻画秘密共享模型的状态集合和状态转移函数。其次,基于所提出的系统模型构造相应的理性秘密共享方案,基于马尔可夫策略解决各理性参与者在秘密共享方案中的秘密重构问题。最后对方案进行理论分析证明,给出理性秘密重构方案中折扣因子、回报函数、参与者风险偏好函数间的函数关系,其结果表明所提系统模型方法的合理性和有效性。     

Energy cooperation in communication of energy harvesting tags

Energy harvesting wireless devices are recently emerging as a viable infrastructure for internet of things (IoT) applications. In this paper, an energy cooperative transmission strategy is proposed for a network energy harvesting tags, that is adapted to the available energy resource and identification request. We develop an optimal transmission policy to maximize the long-term average throughput performance via a Markov decision process (MDP) formulation. Numerical results are provided to show the performance of the energy cooperative transmission policy under various scenarios.     

Polynomial cost approximations in Markov decision theory based calladmission control

The problem of call admission control and routing in a multiservice circuit-switched loss network can be solved optimally under certain assumptions by the tools of Markov decision theory. However, in networks of practical size a number of simplifying approximations are needed to make the solution feasible. Assuming link independence, we propose a new method for approximating the state-dependent link costs accurately and relatively efficiently, even on links with extremely large state spaces. The proposed polynomial approximations are optimal in the sense of minimizing the residual in the continuous-time Howard equations of the Markov decision processes associated with the links. Numerical results are presented, and the proposed approximations are found superior to some earlier link-cost approximation methods     

基于神经网络密码的图像加密

利用离散Hopfrield神经网络生成的序列密码,对图像信号进行加密和解密的实验研究表明,该网络可模拟复杂非线性函数,使输出序列的周期性、0与1平衡性和序列相关性等均满足序列密码设计要求,而且并行运算速度快,有利于数字电路实现.