A Markov Renewal Based Model for Wireless Networks

We propose a queueing network model which can be used for the integration of the mobility and teletraffic aspects that are characteristic of wireless networks. In the general case, the model is an open network of infinite server queues where customers arrive according to a non-homogeneous Poisson process. The movement of a customer in the network is described by a Markov renewal process. Moreover, customers have attributes, such as a teletraffic state, that are driven by continuous time Markov chains and, therefore, change as they move through the network. We investigate the transient and limit number of customers in disjoint sets of nodes and attributes. These turn out to be independent Poisson random variables. We also calculate the covariances of the number of customers in two sets of nodes and attributes at different time epochs. Moreover, we conclude that the arrival process per attribute to a node is the sum of independent Poisson cluster processes and derive its univariate probability generating function. In addition, the arrival process to an outside node of the network is a non-homogeneous Poisson process. We illustrate the applications of the queueing network model and the results derived in a particular wireless network.     

Parametric Ghost Image Processes for Fixed-Charge Problems: A Study of Transportation Networks

We present a parametric approach for solving fixed-charge problems first sketched in Glover (1994). Our implementation is specialized to handle the most prominently occurring types of fixed-charge problems, which arise in the area of network applications. The network models treated by our method include the most general members of the network flow class, consisting of generalized networks that accommodate flows with gains and losses. Our new parametric method is evaluated by reference to transportation networks, which are the network structures most extensively examined, and for which the most thorough comparative testing has been performed. The test set of fixed-charge transportation problems used in our study constitutes the most comprehensive randomly generated collection available in the literature. Computational comparisons reveal that our approach performs exceedingly well. On a set of a dozen small problems we obtain ten solutions that match or beat solutions found by CPLEX 9.0 and that beat the solutions found by the previously best heuristic on 11 out of 12 problems. On a more challenging set of 120 larger problems we uniformly obtain solutions superior to those found by CPLEX 9.0 and, in 114 out of 120 instances, superior to those found by the previously best approach. At the same time, our method finds these solutions while on average consuming 100 to 250 times less CPU time than CPLEX 9.0 and a roughly equivalent amount of CPU time as taken by the previously best method.     

Stochastic processes adapted by neural networks with application to climate, energy, and finance

Local climate parameters may naturally effect the price of many commodities and their derivatives. Therefore we propose a joint framework for stochastic modeling of climate and commodity prices. In our setting, a stable Levy process is drift augmented to a generalized SDE. The related nonlinear function on the state space typically exhibits deterministic chaos. Additionally, a neural network adapts the parameters of the stable process such that the latter produces increasingly optimal differences between simulated output and observed data. Thus we propose a novel method of “intelligent” calibration of the stochastic process, using learning neural networks in order to dynamically adapt the parameters of the stochastic model.     

On the Existence of Invariant Measures for Networks with String Transitions

Recently a new class of Markov network processes was introduced, characterized by so-called string transitions. These are continuous-time Markov processes on a discrete state space. It is known that they possess an invariant measure of a special form, called a product-form, provided that a certain system of so-called traffic equations possesses a solution. Little is known about the existence of solutions of the traffic equations. The present paper deals with this question, focussing on the most important special case of unit vector string transitions. It is shown for open networks with unit vector string transitions of bounded lengths that the traffic equations possess a solution. Furthermore, it is shown for a prominent example of a network featuring signals and batch services that the traffic equations possess a solution.     

Contagion processes on urban bus networks in Indian cities

Bus transportation is the most convenient and cheapest way of public transportation in Indian cities. Due to cost‐effectiveness and wide reachability, buses bring people to their destinations every day. Although the bus transportation has numerous advantages over other ways of public transportation, this mode of transportation also poses a serious threat of spreading contagious diseases throughout the city. It is extremely difficult to predict the extent and spread of such an epidemic. Earlier studies have focused on the contagion processes on scale‐free network topologies; whereas, real‐world networks such as bus networks exhibit a wide‐spectrum of network topology. Therefore, we aim in this study to understand this complex dynamical process of epidemic outbreak and information diffusion on the bus networks for six different Indian cities using SI and SIR models. We identify epidemic thresholds for these networks which help us in controlling outbreaks by developing node‐based immunization techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 451–458, 2016     

Activation clustering in neural and social networks

Questions related to the evolution of the structure of networks have received recently a lot of attention in the literature. But what is the state of the network given its structure? For example, there is the question of how the structures of neural networks make them behave? Or, in the case of a network of humans, the question could be related to the states of humans in general, given the structure of the social network. The models based on stochastic processes developed in this article, do not attempt to capture the fine details of social or neural dynamics. Rather they aim to describe the general relationship between the variables describing the network and the aggregate behavior of the network. A number of nontrivial results are obtained using computer simulations. © 2005 Wiley Periodicals, Inc. Complexity 10: 42–50, 2005     

On non-stationary solutions to MSDDEs: Representations and the cointegration space

In this paper we study solutions to multivariate stochastic delay differential equations (MSDDEs) and their relation to the discrete-time cointegrated VAR model. In particular, we observe that an MSDDE can always be written in an error correction form and, under suitable conditions, we argue that a process with stationary increments is a solution to the MSDDE if and only if it admits a certain Granger type representation. A direct implication of these results is a complete characterization of the cointegration space. Finally, the relation between MSDDEs and invertible multivariate CARMA equations is used to introduce the cointegrated MCARMA processes.     

Branching/queueing networks: Their introduction and near-decomposability asymptotics

A new class of models, which combines closed queueing networks with branching processes, is introduced. The motivation comes from MIMD computers and other service systems in which the arrival of new work is always triggered by the completion of former work, and the amount of arriving work is variable. In the variant of branching/queueing networks studied here, a customer branches into a random and state-independent number of offspring upon completing its service. The process regenerates whenever the population becomes extinct. Implications for less rudimentary variants are discussed. The ergodicity of the network and several other aspects are related to the expected total number of progeny of an associated multitype Galton-Watson process. We give a formula for that expected number of progeny. The objects of main interest are the stationary state distribution and the throughputs. Closed-form solutions are available for the multi-server single-node model, and for homogeneous networks of infinite-servers. Generally, branching/queueing networks do not seem to have a product-form state distribution. We propose a conditional product-form approximation, and show that it is approached as a limit by branching/queueing networks with a slowly varying population size. The proof demonstrates an application of the nearly complete decomposability paradigm to an infinite state space. This revised version was published online in June 2006 with corrections to the Cover Date.     

Time behavior of stressed nonhierarchical networks

In this paper it is shown, by means of simulation, that interesting time behavior is observed for damaged and overloaded networks, and that adaptive, decentralized intelligence can have a dramatic influence on the overall network performance. The time behavior of nonhierarchical networks is modeled by a system of nonlinear difference equations among global variables, and bistability is shown to exist. Using a simple adaptive control mechanism, it is shown that, depending upon the value of a certain network variable, either limit cycle or steady state behavior results. A few ideas are discussed for applications of nonhierarchical communications to cellular automata and to decision-making systems.     

Learning continuous time Bayesian network classifiers

Streaming data are relevant to finance, computer science, and engineering while they are becoming increasingly important to medicine and biology. Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of event matters. Structural and parametric learning for the class of continuous time Bayesian network classifiers are considered in the case where complete data is available. Conditional log-likelihood scoring is developed for structural learning on continuous time Bayesian network classifiers. Performance of continuous time Bayesian network classifiers learned when combining conditional log-likelihood scoring and Bayesian parameter estimation are compared with that achieved by continuous time Bayesian network classifiers when learning is based on marginal log-likelihood scoring and to that achieved by dynamic Bayesian network classifiers. Classifiers are compared in terms of accuracy and computation time. Comparison is based on numerical experiments where synthetic and real data are used. Results show that conditional log-likelihood scoring combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring. Conditional log-likelihood scoring becomes even more effective when the amount of available data is limited. Continuous time Bayesian network classifiers outperform in terms of computation time and accuracy dynamic Bayesian network on synthetic and real data sets.     

Large deviations ordering of point processes in some queueing networks

Given a stochastic ordering between point processes, say that a p.p. N is smooth if it is less than the Poisson process with the same average intensity for this ordering. In this article we investigate whether initially smooth processes retain their smoothness as they cross a network of FIFO ·/D/1 queues along fixed routes. For the so-called strong variability ordering we show that point processes remain smooth as they proceed through a tandem of quasi-saturated (i.e., loaded to 1) M+·/D/1 queues. We then introduce the Large Deviations ordering, which involves comparison of the rate functions associated with Large Deviations Principles satisfied by the point processes. For this ordering, we show that smoothness is retained when the processes cross a feed-forward network of unsaturated ·/D/1 queues. We also examine the LD characteristics of a deterministic p.p. at the output of an M+·/D/1 queue. This revised version was published online in June 2006 with corrections to the Cover Date.     

几何随机图大连通分支覆盖面积及其在传感器网络中的应用

随机网络中的大连通分支能体现一个网络的连通情况,是几何随机图研究的-个热点,具有重要的理论意义和应用价值.本文利用渗流理论,研究了几何随机图大连通分支覆盖面积所具有的性质,并将理论结果应用到大型无线传感器网络中,研究了无线传感器网络覆盖的性质.研究结果表明,对于节点服从泊松分布的大型无线传感器网络,其大连通分支覆盖区域大小与总区域大小的比值趋于-个常数,且并估计出了2维空间中没有被大连通分支所覆盖的连通区域(本文称为空洞)的大小.这些结果为衡量无线传感器网络性能提供了理论基础,对实际布网和网络优化等具有一定的指导意义.     

Decomposition approximation of queueing-network control models with tree structures

We devise a decomposition approximation method for a general branching queueing network with service-rate controls. The decomposition method, which reduces and simplifies computation routines considerably, results from the conditions for monotone optimal control policies in induction arguments. We first isolate each branch of connected queues as a subnetwork and then link the subnetworks through branching nodes to approximate the optimal control policies for the global network. The numerical results for a wide class of cost models show that the approximated optimal policies from the decomposed problems are sufficiently close to the optimal policies of the global problem.     

Coevolutionary-based Mechanisms for Network Anomaly Detection

The paper presents an approach based on the principles of immune systems applied to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by building a model of the network behavior based on the self–nonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. The structures corresponding to self-space are built using a training set from this space. The hyperrectangular detectors covering nonself space are created using a niching genetic algorithm. A coevolutionary algorithm is proposed to enhance this process. The results of experiments show a high quality of intrusion detection, which outperform the quality of the recently proposed approach based on a hypersphere representation of the self-space.      

点星网与度量空间的映像

拓扑空间X的覆盖列{P_i}_(i∈N)被称为空间X的点星网,若x∈X,则{st(x,P_i)}_(i∈N)是x在X中的网.本文刻画具有cs有限cs覆盖列的点星网的空间,并将其表示为度量空间在确定映射下的像.在假设集族性质β满足适当的条件下,证明对拓扑空间X下述条件相互等价:(1) X具有β且cs覆盖列的点星网.(2)X具有β且sn覆盖列的点星网.(3)X是Cauchy sn对称空间且具有σ-β的cs网.(4) X是Cauchy sn对称空间且具有σ-β的sn网.(5) X是度量空间的序列覆盖、π且σ-β映像.(6) X是度量空间的1序列覆盖、紧且σ-β映像.这些工作以局部有限集族与点有限集族为特例,拓展了从基到cs网的研究,丰富了映射与空间的相互分类思想.     

Foraging society organization: A simple model of a complex transition

The evolutionary development of the hominids that culminated in the appearance of Homo sapiens included the subdivision of the species into societies on the basis of culturally, instead of biologically, constructed differentiation. It is argued that this change must have occurred after the mental ability to formulate and culturally express conceptual structures of extended relationships had been been biologically introduced, and that intergroup competition within a species provided the selective impetus for this more complex form of organization. The combination of conceptual structures for organization at a more extensive scale and the effects of intergroup competition would lead to a restructuring of the whole species into society like groups.     

Poisson flows in single class open networks of quasireversible queues

This paper analyzes open networks of quasireversible nodes with a single class of customers and in equilibrium. A simple argument shows, under a stability conditions, that a flow on a link of such a network is Poisson if and only if the link is not part of a loop. This loop criterion is shown to apply to the usual quasireversible networks with bounded service rates.     

Inferring social structure from continuous‐time interaction data

Relational event data, which consist of events involving pairs of actors over time, are now commonly available at the finest of temporal resolutions. Existing continuous‐time methods for modeling such data are based on point processes and directly model interaction “contagion,” whereby one interaction increases the propensity of future interactions among actors, often as dictated by some latent variable structure. In this article, we present an alternative approach to using temporal‐relational point process models for continuous‐time event data. We characterize interactions between a pair of actors as either spurious or as resulting from an underlying, persistent connection in a latent social network. We argue that consistent deviations from expected behavior, rather than solely high frequency counts, are crucial for identifying well‐established underlying social relationships. This study aims to explore these latent network structures in two contexts: one comprising of college students and another involving barn swallows.     

MARKOV SKELETON PROCESS IN PERT NETWORKS

In this article, we investigate Programming Evaluation and Review Technique networks with independently and generally distributed activity durations. For any path in this network, we select all the activities related to this path such that the completion time of the sub-network （only consisting of all the related activities） is equal to the completion time of this path. We use the elapsed time as the supplementary variables and model this sub-network as a Markov skeleton process, the state space is related to the subnetwork structure. Then use the backward equation to compute the distribution of the sub-network＇s completion time, which is an important rule in project management and scheduling.     

Statistical methods for network surveillance

The term network surveillance is defined in general terms and illustrated with many examples. Statistical methodologies that can be used as tools for network surveillance are discussed. Details for 3 illustrative examples that address network security, surveillance for data network failures, and surveillance of email traffic flows are presented. Some open areas of research are identified.