Coalitional game theory for communication networks

In this tutorial, we provided a comprehensive overview of coalitional game theory, and its usage in wireless and communication networks. For this purpose, we introduced a novel classification of coalitional games by grouping the sparse literature into three distinct classes of games: canonical coalitional games, coalition formation games, and coalitional graph games. For each class, we explained in details the fundamental properties, discussed the main solution concepts, and provided an in-depth analysis of the methodologies and approaches for using these games in both game theory and communication applications. The presented applications have been carefully selected from a broad range of areas spanning a diverse number of research problems. The tutorial also sheds light on future opportunities for using the strong analytical tool of coalitional games in a number of applications. In a nutshell, this article fills a void in existing communications literature, by providing a novel tutorial on applying coalitional game theory in communication networks through comprehensive theory and technical details as well as through practical examples drawn from both game theory and communication application.     

Hybrid protection algorithms based on game theory in multi-domain optical networks

With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user’s utility, the network operator’s utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator’s utility, while IEP has better user’s utility.     

Detection of evoked potentials with the use of the stochastic filtering theory

For detecting evoked potentials (EPs) in electroencephalogram (EEG) signals, a mathematical model of EEG without EP is constructed and equations of stochastic filtering are synthesized. From the analysis of updating sequences, an EP detection criterion is formulated. An EP detection algorithm is developed and studied, and its efficiency is confirmed by means of simulation.     

On the use of fractional Brownian motion in the theory ofconnectionless networks

An abstract model for aggregated connectionless traffic, based on the fractional Brownian motion, is presented. Insight into the parameters is obtained by relating the model to an equivalent burst model. Results on a corresponding storage process are presented. The buffer occupancy distribution is approximated by a Weibull distribution. The model is compared with publicly available samples of real Ethernet traffic. The degree of the short-term predictability of the traffic model is studied through an exact formula for the conditional variance of a future value given the past. The applicability and interpretation of the self-similar model are discussed extensively, and the notion of ideal free traffic is introduced     

A stochastic game framework for joint frequency and power allocation in dynamic decentralized cognitive radio networks

Cognitive radio networks (CRNs) have been recognized as a promising solution to improve the radio spectrum utilization. This article investigates a novel issue of joint frequency and power allocation in decentralized CRNs with dynamic or time-varying spectrum resources. We firstly model the interactions between decentralized cognitive radio links as a stochastic game and then proposed a strategy learning algorithm which effectively integrates multi-agent frequency strategy learning and power pricing. The convergence of the proposed algorithm to Nash equilibrium is proofed theoretically. Simulation results demonstrate that the throughput performance of the proposed algorithm is very close to that of the centralized optimal learning algorithm, while the proposed algorithm could be implemented distributively and reduce information exchanges significantly.     

Toward an integrated dynamic defense system for strategic detecting attacks in cloud networks using stochastic game

Telecommunication Systems - In a complex network as a cloud computing environment, security is becoming increasingly based on deception techniques. To date, the static nature of cyber networks...     

The theory of functionals of stochastic backscattered fields

The theory of wave scattering by anisotropic statistically rough surfaces, which is an important part of statistical radiophysics, is considered. A new analytic method is developed and generalized for solving problems of radar imaging. The method involves analytic determination of the functionals of stochastic backscattered fields and can be applied to solve a wide class of physical problems with allowance for the finite width of an antenna’s pattern. The unified approach based on this method is used to analyze the generalized frequency response of a scattering radio channel, a generalized correlator of scattered fields, spatial correlation functions of stochastic backscattered fields, frequency coherence functions of stochastic backscattered fields, the coherence band of a spatial-temporal scattering radar channel, the kernel of the generalized uncertainty function, and the measure of noise immunity characterizing radar probing of the Earth’s surface or extended targets. The introduced frequency coherence functions are applied for thorough and consistent study of techniques for measuring the characteristics of a rough surface, aircraft altitude, and distortions observed when radar signals are scattered by statistically rough, including fractal, surfaces. To exemplify urgent applications, radiophysical synthesis of detailed digital reference radar terrain maps and microwave radar images that was proposed earlier is considered and improved with the use of the theory of fractals.     

On measures of vulnerability of interconnection networks

With the advent of distributed and multiple processing systems, the study of interconnection networks has developed into a field of its own. In most cases, the performance measures used to characterize the reliability of such networks have been primitive.It is our purpose, in this brief paper, to survey the many available measures of the vulnerability of interconnection networks. The most widely known is classical connectivity; however, measures more recently introduced in the graph theory literature are probably a better practical metric of reliability. Our objective is to make these measures more widely known, and to spur the development of good algorithms to calculate them in practical instances.     

A hybrid,game theory based,and distributed clustering protocol for wireless sensor networks

Clustering has been well known as an effective way to reduce energy dissipation and prolong network lifetime in wireless sensor networks. Recently, game theory has been used to model clustering problem. Each node is modeled as a player which can selfishly choose its own strategies to be a cluster head (CH) or not. And by playing a localized clustering game, it gets an equilibrium probability to be a CH that makes its payoff keep equilibrium. In this paper, based on game theory, we present a clustering protocol named Hybrid, Game Theory based and Distributed clustering. In our protocol, we specifically define the payoff for each node when choosing different strategies, where both node degree and distance to base station are considered. Under this definition, each node gets its equilibrium probability by playing the game. And it decides whether to be a CH based on this equilibrium probability that can achieve a good trade-off between minimizing energy dissipation and providing the required services effectively. Moreover, an iterative algorithm is proposed to select the final CHs from the potential CHs according to a hybrid of residual energy and the number of neighboring potential CHs. Our iterative algorithm can balance the energy consumption among nodes and avoid the case that more than one CH occurs in a close proximity. And we prove it terminates in finite iterations. Simulation results show that our protocol outperforms LEACH, CROSS and LGCA in terms of network lifetime.     

Optimal resource allocation scheme for cognitive radio networks with relay selection based on game theory

In this paper,we present a non-transferable utility coalition graph game(NTU-CGG) based resource allocation scheme with relay selection for a downlink orthogonal frequency division multiplexing(OFDMA) based cognitive radio networks to maximize both system throughput and system fairness.In this algorithm,with the assistance of others SUs,SUs with less available channels to improve their throughput and fairness by forming a directed tree graph according to spectrum availability and traffic demands of SUs.So this scheme can effectively exploit both space and frequency diversity of the system.Performance results show that,NTU-CGG significantly improves system fairness level while not reducing the throughput comparing with other existing algorithms.     

Dynamic subcarrier and power allocation based on cooperative game theory in symmetric cooperative OFDMA networks

In order to improve the efficiency and fairness of radio resource utilization,a scheme of dynamic cooperative subcarrier and power allocation based on Nash bargaining solution(NBS-DCSPA) is proposed in the uplink of a three-node symmetric cooperative orthogonal frequency division multiple access(OFDMA) system.In the proposed NBS-DCSPA scheme,resource allocation problem is formulated as a two-person subcarrier and power allocation bargaining game(SPABG) to maximize the system utility,under the constraints of each user’s maximal power and minimal rate,while considering the fairness between the two users.Firstly,the equivalent direct channel gain of the relay link is introduced to decide the transmission mode of each subcarrier.Then,all subcarriers can be dynamically allocated to the two users in terms of their selected transmission mode.After that,the adaptive power allocation scheme combined with dynamic subcarrier allocation is optimized according to NBS.Finally,computer simulation is conducted to show the efficiency and fairness performance of the proposed NBS-DCSPA scheme.     

A game theory approach to constrained minimax state estimation

This paper presents a game theory approach to the constrained state estimation of linear discrete time dynamic systems. In the application of state estimators, there is often known model or signal information that is either ignored or dealt with heuristically. For example, constraints on the state values (which may be based on physical considerations) are often neglected because they do not easily fit into the structure of the state estimator. This paper develops a method for incorporating state equality constraints into a minimax state estimator. The algorithm is demonstrated on a simple vehicle tracking simulation.     

A game theoretic approach to video streaming over peer-to-peer networks

We consider the problem of foresighted multimedia resource reciprocation in peer-to-peer (P2P) networks, which consist of rational peers aiming at maximizing their individual utilities. We introduce an artificial currency (credit) to take into account the characteristics of different parts of the video signal. The resource reciprocation with the proposed credit metric can be formulated as a stochastic game, in which the peers determine their optimal strategies using Markov Decision Process (MDP) framework. The introduced framework can be applied to the general video coding, and in particular, is suitable for the scalable video where various parts of the encoded bit stream have significantly different importance for the video quality.     

High throughput relay policy in wireless cooperative relaying networks based on stochastic control theory

This paper proposes a distributed relay and modulation and coding scheme (MCS) selection in wireless cooperative relaying networks where the adaptive modulation and coding (AMC) scheme is applied. First-order finite-state Markov channels (FSMCs) are used to model the wireless channels and make prediction. The objective of the relay policy is to select one relay and MCS among different alternatives in each time-slot according to their channel state information (CSI) with the goal of maximizing the throughput of the whole transmission period. The procedure of relay and MCS selection can be formulated as a discounted Markov decision chain, and the relay policy can be obtained with recent advances in stochastic control algorithms. Simulation results are presented to show the effectiveness of the proposed scheme.     

A game theoretical approach to clustering of ad-hoc and sensor networks

Game theory has been used for decades in fields of science such as economics and biology, but recently it was used to model routing and packet forwarding in wireless ad-hoc and sensor networks. However, the clustering problem, related to self-organization of nodes into large groups, has not been studied under this framework. In this work our objective is to provide a game theoretical modeling of clustering for ad-hoc and sensor networks. The analysis is based on a non-cooperative game approach where each sensor behaves selfishly in order to conserve its energy and thus maximize its lifespan. We prove the Nash Equilibria of the game for pure and mixed strategies, the expected payoffs and the price of anarchy corresponding to these equilibria. Then, we use this analysis to formulate a clustering mechanism (which we called Clustered Routing for Selfish Sensors??CROSS), that can be applied to sensor networks in practice. Comparing this mechanism to a popular clustering technique, we show via simulations that CROSS achieves a performance similar to that of a very popular clustering algorithm.     

复杂网络上的演化博弈研究

介绍了一些典型的复杂网络建模的演化博弈模型,主要研究动态网络上的群体行为,它们都体现出网络中引入一定的空间拓扑结构会对主体策略行为产生显著影响,论述了规则格子和无标度网络结构分别对囚徒困境博弈和雪堆博弈的影响,给出了网络结构形成机制,提升了主体策略行为.     

基于博弈论的无线自组网路由协议研究

Ad hoc网络,是无线自组织网络的简称.它没有固定的基础设施,网路中的每个节点具有双重身份,即通信终端和路由器.路由协议是Ad hoc网络的关键技术,但传统的协议在设计时并未考虑效率因素.本文基于博弈论提出一种平均场均衡的方法,能有效的减少信息泛洪,以满足节点移动性对自组网的性能要求.     

Reducing vulnerability of shared-path protection subject to double-link failures in WDM mesh networks

Shared-path protection (SPP) is a capacity- efficient mechanism to provide 100% single-link failure protection in WDM mesh networks. After the first link failure, however, if reconfiguration is unavailable, a second link failure may lead to tremendous service interruption. Vulnerability of a link is the percentage of links that are vulnerable to the arbitrary second link failure after the link failed and network vulnerability is defined as the average vulnerability of links in the network. In this article, we present three policies that can reduce network vulnerability by reducing backup sharing in shared-path protection. The first policy tries to restrict the times of sharing a wavelength-link by connections. The second policy tries to avoid backup sharing on highly vulnerable links. The third policy tries to avoid backup sharing on highly vulnerable wavelength-links. Algorithms to implement these policies based on a similar framework are presented. Numerical results suggest that there is a trade-off between capacity efficiency and network vulnerability in all the three policies. The third policy can deliver a better performance in terms of blocking probability and network vulnerability.

A game theory approach to robust discrete-timeH∞-estimation

The deterministic robust estimation problem is formulated in the discrete-time case as a zero-sum two person difference game, in which a statistician plays against nature. The statistician tries to estimate a linear combination of the states of an uncertain linear system, which is driven by nature. Saddle-point strategies for the statistician in the above game are difficult to find. A suboptimal strategy is, therefore, considered which guarantees an H_∞ performance bound on the estimation error. Different patterns of the information that is available to the statistician are treated, and the corresponding fixed-point, fixed-lag, and fixed-interval smoothing strategies are derived