Uniqueness of the solution for optimal static routing in open BCMP queueing networks

Optimal static routing problems in open BCMP queueing networks with state-independent arrival and service rates are studied. They include static routing problems in communication networks and optimal static load balancing problems in distributed computer systems. We consider an overall optimal policy that is the routing policy whereby the overall mean response (or sojourn) time of a job is minimized. We obtain the routing decisions of the overall optimal policy and show that they may not be unique, but that the utilization of each service center is uniquely determined by the overall optimal policy. We also consider an individually optimal policy whereby jobs are routed so that each job may feel that its own expected response time is minimized if it knows the mean delay time for each path.     

Modeling and optimization of energy efficient routing in wireless sensor networks

Wireless sensor networks (WSNs) have important applications in remote environmental monitoring and target tracking. The development of WSNs in recent years has been facilitated by the availability of sensors that are smaller, less expensive, and more intelligent. The design of a WSN depends significantly on its desired applications and must take into account factors such as the environment, the design objectives of the application, the associated costs, the necessary hardware, and any applicable system constraints. In this study, we propose mathematical models for a routing protocol (network design) under particular resource restrictions within a wireless sensor network. We consider two types of constraints: the distance between the linking sensors and the energy used by the sensors. The proposed models aim to identify energy-efficient paths that minimize the energy consumption of the network from the source sensor to the base station. The computational results show that the presented models can be used efficiently and applied to other network design contexts with resource restrictions (e.g., to multi-level supply chain networks).     

Exact and heuristic methods to maximize network lifetime in wireless sensor networks with adjustable sensing ranges

Wireless sensor networks involve many different real-world contexts, such as monitoring and control tasks for traffic, surveillance, military and environmental applications, among others. Usually, these applications consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. We want to individuate a set of covers (that is, subsets of sensors that can cover the whole set of targets) and appropriate activation times for each of them in order to maximize the total amount of time in which the monitoring activity can be performed (network lifetime), under the constraint given by the limited power of the battery contained in each sensor. A variant of this problem considers that each sensor can be activated in a certain number of alternative power levels, which determine different sensing ranges and power consumptions. We present some heuristic approaches and an exact approach based on the column generation technique. An extensive experimental phase proves the advantage in terms of solution quality of using adjustable sensing ranges with respect to the classical single range scheme.     

Mathematical model on the transmission of worms in wireless sensor network

Wireless sensor networks (WSNs) have received extensive attention due to their great potential in civil and military applications. The sensor nodes have limited power and radio communication capabilities. As sensor nodes are resource constrained, they generally have weak defense capabilities and are attractive targets for software attacks. Cyber attack by worm presents one of the most dangerous threats to the security and integrity of the computer and WSN. In this paper, we study the attacking behavior of possible worms in WSN. Using compartmental epidemic model, we propose susceptible – exposed – infectious – recovered – susceptible with a vaccination compartment (SEIRS-V) to describe the dynamics of worm propagation with respect to time in WSN. The proposed model captures both the spatial and temporal dynamics of worms spread process. Reproduction number, equilibria, and their stability are also found. If reproduction number is less than one, the infected fraction of the sensor nodes disappears and if the reproduction number is greater than one, the infected fraction persists and the feasible region is asymptotically stable region for the endemic equilibrium state. Numerical methods are employed to solve and simulate the systems of equations developed and also to validate our model. A critical analysis of vaccination class with respect to susceptible class and infectious class has been made for a positive impact of increasing security measures on worm propagation in WSN.     

The N-policy of a discrete time Geo/G/1 queue with disasters and its application to wireless sensor networks

We analyze an N-policy of a discrete time Geo/G/1 queue with disasters. We obtain the probability generating functions of the queue length, the sojourn time, and regeneration cycles such as the idle period and the busy period. We apply the queue to a power saving scheme in wireless sensor networks under unreliable network connections where data packets are lost by external attacks or shocks. We present various numerical experiments for application to power consumption control in wireless sensor networks. We investigate the characteristics of the optimal N-policy that minimizes power consumption and derive practical insights on the operation of the N-policy in wireless sensor networks.     

Detecting and Preventing Routing Problems in the Planning Process of CCSS #7 Networks

Common Channel Signalling System #7 (CCSS#7) has become nowadays widely used by most of the operators which are strongly dependant on its performance. This paper develops next issues. First, usual problems with routing in CCSS#7 networks that can not be solved by the protocol are described, and a checking algorithm based on modified dynamic programming techniques is proposed. And secondly, the problem of updating routing tables of nodes and several alternatives to solve it, are presented and compared. Two kind of solutions to solve this second problem are proposed and tested. Conclusions are presented.     

Dynamic routing in open queueing networks: Brownian models,cut constraints and resource pooling

We present an introductory review of recent work on the control of open queueing networks. We assume that customers of different types arrive at a network and pass through the system via one of several possible routes; the set of routes available to a customer depends on its type. A route through the network is an ordered set of service stations: a customer queues for service at each station on its route and then leaves the system. The two methods of control we consider are the routing of customers through the network, and the sequencing of service at the stations, and our aim is to minimize the number of customers in the system. We concentrate especially on the insights which can be obtained from heavy traffic analysis, and in particular from Harrison's Brownian network models. Our main conclusion is that in many respects dynamic routingsimplifies the behaviour of networks, and that under good control policies it may well be possible to model the aggregate behaviour of a network quite straightforwardly.Supported by SERC grant GR/F 94194.     

The evolution of convention: Conformity and innovation in task-oriented networks

The individual choice between conformity and innovation within task-oriented collectivities is presented as a social dilemma. Adaptive, network-embedded actors are seen to modify their propensities to conform or innovate retrospectively, based on performance differences between the individual and task group levels. A computational framework, based on simulation techniques and algorithmic complexity theory, is advanced to investigate the impact of antecedent structural conditions on innovative behavior and the effect that such behavior has on the evolution of patterns of interaction (conventions) and efficiency. Findings indicate that the dynamics of innovation differ dramatically based on the complexity of tasks faced by actors. When simple tasks are addressed, innovative behaviors have a destabilizing effect on social conventions and are clearly linked to contextual factors. When complex tasks are involved, commitment to innovation may actually help reinforce conventions and is not tied to structural antecedents.Department of Sociology, Stanford University     

Queue length and occupancy in discrete-time cyclic networks with several types of nodes

For a discrete-time, closed, cyclic queueing network, where the nodes have independent, geometric service times, the equilibrium rate of local progress is determined. Faster nodes are shown to have a capacity depending only on the service probabilities. A family of such networks, each with the same number of types of nodes, is analyzed. If the number of nodes approaches infinity, and if the ratio of jobs to nodes has a positive limit and each node type has an asymptotic density, then for a given node type, the limits of the proportion of occupied nodes and the expected queue length are calculated. These values depend on the service parameter and on the asymptotic rate of local progress. The faster nodes can attain their capacity only when the limiting density of nodes of slowest type is zero. This revised version was published online in June 2006 with corrections to the Cover Date.     

Measurement and optimization of robust stability of multiclass queueing networks: Applications in dynamic supply chains

Multiclass queueing networks are an essential tool for modeling and analyzing complex supply chains. Roughly speaking, stability of these networks implies that the total number of customers/jobs in the network remains bounded over time. In this context robustness characterizes the ability of a multiclass queueing network to remain stable, if the expected values of the interarrival and service times distributions are subject to uncertain shifts. A powerful starting point for the stability analysis of multiclass queueing networks is the associated fluid network. Based on the fluid network analysis we present a measure to quantify the robustness, which is indicated by a single number. This number will be called the stability radius. It represents the magnitude of the smallest shift of the expected value of the interarrival and/or service times distributions so that the associated fluid network looses the property of stability. The stability radius is a worst case measure and is a conceptual adaptation from the dynamical systems literature. Moreover, we provide a characterization of the shifts that destabilize the network. Based on these results, we formulate a mathematical program that minimizes the required network capacity, while ensuring a desired level of robustness towards shifts of the expected values of the interarrival times distributions. This approach provides a new view on long-term robust production capacity allocation in supply chains. The capabilities of our method are demonstrated using a real world supply chain.     

国内数学学科论著产出的结构以及与国际数学热门研究领域的比较

本文以统计数据为依据对国内数学学科近五年的研究状况、发展趋势以及国内学者数学论著的产出结构做了分析，并与国际数学领域的研究动向和发展态势进行了比较．     

New Concepts of Feasibility and Efficiency of Solutions in Fuzzy Mathematical Programming Problems

Some new concepts in regards to $\bar{\alpha }$-feasibility and $\bar{\alpha }$-efficiency of solutions in fuzzy mathematical programming problems are introduced in this paper, where $\bar{\alpha }$ is a vector of distinct satisfaction degrees. Based on the defined concepts, a new method is suggested to solve fuzzy mathematical programming problems. In this sense, the proposed approach enables decision makers to take into account more flexible solutions by allowing desired distinct satisfactions in constraints. In the case of linear problems with fuzzy constraints, multi-parametric programming is employed to obtain the optimal solution as an affine function of distinct satisfaction degrees. In particular, it proves that the obtained solution is convex and continuous. Therefore, the different optimal solutions can be obtained by a simple substituting the new values of satisfaction parameters into the parametric profiles without any further optimization calculations, which is desirable for online optimization and sensitivity analysis of the profit to satisfaction parameters.     

基于粗糙集理论的超稠油油藏水平井吞吐效果评价及其影响因素分析

影响超稠油油藏水平井蒸汽吞吐开发效果的因素包括地质参数、流体参数、开发参数、水平井设计参数等众多因素,寻找主要因素对于超稠油油藏水平井吞吐效果评价及开发调整设计具有重要意义.基于粗糙集理论对影响超稠油油藏水平井吞吐效果的10个因素进行数据属性约简,同时利用灰色关联分析,对这些因素进行敏感性分析;应用Elman神经网络比较上述两种结果,证明了粗糙集理论进行数据属性约简的方法更准确.最终得到注汽干度、注汽强度、注汽速度、水平段长度和注汽压力对水平井的开发效果影响最大.实例计算表明,方法实用有效,精度较高,可用于超稠油油藏水平井吞吐效果评价及开发、调整设计.     

A problem in enumerating extreme points, and an efficient algorithm for one class of polytopes

We consider the problem of developing an efficient algorithm for enumerating the extreme points of a convex polytope specified by linear constraints. Murty and Chung (Math Program 70:27–45, 1995) introduced the concept of a segment of a polytope, and used it to develop some steps for carrying out the enumeration efficiently until the convex hull of the set of known extreme points becomes a segment. That effort stops with a segment, other steps outlined in Murty and Chung (Math Program 70:27–45, 1995) for carrying out the enumeration after reaching a segment, or for checking whether the segment is equal to the original polytope, do not constitute an efficient algorithm. Here we describe the central problem in carrying out the enumeration efficiently after reaching a segment. We then discuss two procedures for enumerating extreme points, the mukkadvayam checking procedure, and the nearest point procedure. We divide polytopes into two classes: Class 1 polytopes have at least one extreme point satisfying the property that there is a hyperplane H through that extreme point such that every facet of the polytope incident at that extreme point has relative interior point intersections with both sides of H; Class 2 polytopes have the property that every hyperplane through any extreme point has at least one facet incident at that extreme point completely contained on one of its sides. We then prove that the procedures developed solve the problem efficiently when the polytope belongs to Class 2.     

承压水漏斗动态研究的BP网络模型及其研制中的若干问题

结合BP网络在承压水漏斗动态规律研究中的应用,对BP网络模型研制中监控样本的设置、样本数据的规范化处理、网络权重和阈值的初始值的确定、训练函数的选择等问题进行了研究与实践,所提出的方法及有关结论具有普适性,为BP网络的广泛应用提供了可借鉴的方法.     

通脑活络针刺法治疗脑梗死患者的BI与NIHSS指数的协方差分析

利用协方差分析将那些认为很难控制的因素,即患者入院时的BI得分或NIHSS得分作为协变量x,在排除协变量x对观测变量y,即治疗90天后患者的BI、NIHSS得分影响的条件下,分析治疗方法对治疗90天后患者的BI、NIHSS得分的影响,从而更加准确地对治疗方法进行评价.     

基于主成分分析的体制机制对辽宁省两化融合贡献度研究

在我国全力推进工业化和信息化融合的趋势下,针对辽宁省的具体情况,从体制机制对两化融合的影响程度进行分析,建立指标体系,选取代表性的指标,综合运用主成分进行分析,得出了各种指标的权重,从量化的角度了解体制机制对两化融合的影响程度,并结合实际情况做出了体制机制创新的具体建议.     

北京市工业经济宏观监测预警系统中的数理统计方法

本文介绍了门限自回归模型及调控模拟分析在建立北京市工业经济宏观监测预警系统中的成功应用，并对用Ｋ－Ｌ信息量判别指标分类的局限性进行了分析讨论，最后对宏观经济周期规律进行了频谱分析。     

A selective view of stochastic inference and modeling problems in nanoscale biophysics

Advances in nanotechnology enable scientists for the first time to study biological processes on a nanoscale molecule-by-molecule basis. They also raise challenges and opportunities for statisticians and applied probabilists. To exemplify the stochastic inference and modeling problems in the field, this paper discusses a few selected cases, ranging from likelihood inference, Bayesian data augmentation, and semi- and non-parametric inference of nanometric biochemical systems to the utilization of stochastic integro-differential equations and stochastic networks to model single-molecule biophysical processes. We discuss the statistical and probabilistic issues as well as the biophysical motivation and physical meaning behind the problems, emphasizing the analysis and modeling of real experimental data. This work was supported by the United States National Science Fundation Career Award (Grant No. DMS-0449204)