基于融合的无线传感器网络k-集覆盖的分布式算法

当节点采用概率感知模型且融合多个节点的数据进行联合感知的情况下,提出了一个新的无线传感器网络的覆盖优化问题:基于融合的k-集覆盖优化问题.首先,将优化问题建模为融合覆盖博弈,证明该博弈是势博弈,且势函数与优化目标函数一致,因此,最优解是一个纯策略Nash均衡解.其次,给出了节点间融合覆盖效用独立的判定条件,进而分别提出同步、异步控制的、基于局部信息的、分布式的覆盖优化算法,证明了算法收敛到纯策略Nash均衡.最后,仿真实验结果表明,当算法收敛时,网络能达到高的覆盖率且具有好的覆盖稳定性.     

A Price Based Decentralized Rate Selection in IEEE 802.11 Based WLANS

We consider the problem of decentralized rate selection in IEEE 802.11 wireless local area networks (WLANs). Owing to the decentralized nature of WLANs, we formulate the current problem of rate selection as a non-cooperative game where individual users (players) of a WLAN can pick their actions from a finite set of physical layer modulation rates. The utility of each user is the difference of throughput and a cost incurred due to the price imposed by the access point. We prove the resulting non-cooperative game to be supermodular, and hence has at least one pure strategy Nash equilibrium, that is contained in a set bounded by the smallest and largest Nash equilibria. We also prove the smallest and largest Nash equilibria to be non-decreasing functions of the price and the smallest Nash equilibrium to be Pareto-dominant. We present an algorithm to compute the best response of each user asynchronously, that converges almost surely to the smallest Nash equilibrium of the game. Next we extend our price based approach to the multi-channel case and prove the resulting game to be supermodular in the special case of two channels. Our simulation results demonstrate the improvement in overall network throughput with appropriate tuning of the price.     

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.     

Coverage control algorithm for wireless sensor networks based on non-cooperative game

For the redundancy coverage of nodes leads to the phenomenon of low energy efficiency,Non-cooperative game theory was used to solve it.A revenue function was proposed,which considering the coverage of nodes and the residual energy.The lifetime of the node and network path gain were applied to revenue function.The network topology was built by nodes with the appropriate work strategy.Control algorithm coverage in wireless sensor network was proposed based on Non-cooperative game theory.A Nash equilibrium between the coverage rate and the residual energy was proved,and the return function converged to the Pareto optimal.Experiments show that the algorithm can provide reasonable coverage of network nodes and ensure energy efficiency.     

Bottleneck Routing Games in Communication Networks

We consider routing games where the performance of each user is dictated by the worst (bottleneck) element it employs. We are given a network, finitely many (selfish) users, each associated with a positive flow demand, and a load-dependent performance function for each network element; the social (i.e., system) objective is to optimize the performance of the worst element in the network (i.e., the network bottleneck). Although we show that such "bottleneck" routing games appear in a variety of practical scenarios, they have not been considered yet. Accordingly, we study their properties, considering two routing scenarios, namely when a user can split its traffic over more than one path (splittable bottleneck game) and when it cannot (unsplittable bottleneck game). First, we prove that, for both splittable and unsplittable bottleneck games, there is a (not necessarily unique) Nash equilibrium. Then, we consider the rate of convergence to a Nash equilibrium in each game. Finally, we investigate the efficiency of the Nash equilibria in both games with respect to the social optimum; specifically, while for both games we show that the price of anarchy is unbounded, we identify for each game conditions under which Nash equilibria are socially optimal.     

Multiradio Channel Allocation in Multihop Wireless Networks

Channel allocation was extensively investigated in the framework of cellular networks, but it was rarely studied in the wireless ad hoc networks, especially in the multihop networks. In this paper, we study the competitive multiradio multichannel allocation problem in multihop wireless networks in detail. We first analyze that the static noncooperative game and Nash equilibrium (NE) channel allocation scheme are not suitable for the multihop wireless networks. Thus, we model the channel allocation problem as a hybrid game involving both cooperative game and noncooperative game. Within a communication session, it is cooperative; and among sessions, it is noncooperative. We propose the min-max coalition-proof Nash equilibrium (MMCPNE) channel allocation scheme in the game, which aims to maximize the achieved data rates of communication sessions. We analyze the existence of MMCPNE and prove the necessary conditions for MMCPNE. Furthermore, we propose several algorithms that enable the selfish players to converge to MMCPNE. Simulation results show that MMCPNE outperforms NE and coalition-proof Nash equilibrium (CPNE) schemes in terms of the achieved data rates of multihop sessions and the throughput of whole networks due to cooperation gain.     

Joint Channel Allocation and Power Control Optimal Algorithm Based on Non-cooperative Game in Wireless Sensor Networks

In order to reduce the wireless sensor network interference, and balance the network energy consumption, we have established a joint channel allocation and power control optimal game model. The model takes the network interference, and the residual energy of nodes as the parameters. What’s more, the model considers the independent and influencing relation between channel allocation and power control. In addition, we design a joint channel allocation and power control optimal algorithm based on non-cooperative game (JCAPGA), and then prove that JCAPGA converges to Nash equilibrium. Simulation results show that, JCAPGA has a good network performance of lower interference and uniform energy consumption.     

A Game-Theoretical Study of Robust Networked Systems

This paper analyses the robustness of networked systems from a game-theoretical perspective. Networked systems often consist of several subsystems sharing resources interdependently based on local preferences. These systems can be modelled by a dependence game, which is a generalisation of stable paths problem. A unique pure Nash equilibrium in a dependence game can characterise the robustness of the represented networked system, precluding oscillations and nondeterminism. We show that the absence of a structure termed a generalised dispute wheel is useful to ensure the existence of a unique pure Nash equilibrium. Furthermore, we consider more sophisticated settings: tie-breaking over non-strict preferences and asynchronous communications among subsystems. We also obtain stronger results that the absence of a generalised dispute wheel can be useful to ensure the consistency of tie-breaking and asynchronous convergence to a pure Nash equilibrium.     

Asynchronous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels

This paper considers the maximization of information rates for the Gaussian frequency-selective interference channel, subject to power and spectral mask constraints on each link. To derive decentralized solutions that do not require any cooperation among the users, the optimization problem is formulated as a static noncooperative game of complete information. To achieve the so-called Nash equilibria of the game, we propose a new distributed algorithm called asynchronous iterative water-filling algorithm. In this algorithm, the users update their power spectral density (PSD) in a completely distributed and asynchronous way: some users may update their power allocation more frequently than others and they may even use outdated measurements of the received interference. The proposed algorithm represents a unified framework that encompasses and generalizes all known iterative water-filling algorithms, e.g., sequential and simultaneous versions. The main result of the paper consists of a unified set of conditions that guarantee the global converge of the proposed algorithm to the (unique) Nash equilibrium of the game.     

未知环境中基于图型博弈和Multi-Q学习的动态信道选择算法

研究了分布式无线网络中,没有任何信息交换、也没有环境变化先验知识情况下的动态信道接入算法。运用图型博弈模型对用户的实际拓扑进行建模分析,证明了此博弈模型存在纯策略纳什均衡并且此纳什均衡是全局最优解。同时,采用multi-Q学习求解模型的纯策略纳什均衡解。仿真实验验证了multi-Q学习能获得较高的系统容量以及在图型博弈模型中用户的效用主要由节点的度决定,而与用户数量无直接关系。     

Dense femtocell networks power self‐optimization: an exact potential game approach

Femtocell is regarded as a promising technology to enhance indoor coverage and improve network capacity. However, highly dense and self‐organized femtocells in urban environment will result in serious inter‐femtocell interference. To solve this problem, this paper proposes a distributed power self‐optimization scheme for the downlink operation of dense femtocell networks. First, a novel convex pricing mechanism is presented to price the transmit power of femtocells and construct the utility function of femtocells. Then, a noncooperative game framework for power self‐optimization of femtocells in dense femtocell networks is established on the basis of the exact potential game theory, which is demonstrated to converge to a pure and unique Nash equilibrium. Finally, combined with firefly algorithm, an effective power self‐optimization algorithm with guaranteed convergence is proposed to achieve the Nash equilibrium of the proposed game. With practical LTE parameters and a 3GPP dual‐strip femtocell model, simulation results show that the proposed game with convex pricing mechanism increases the femtocell network throughput by 7% and reduces the average transmit power of femtocells by 50% in dense femtocell networks, with respect to the compared schemes. Copyright © 2014 John Wiley & Sons, Ltd.     

Effect of Selfish Node Behavior on Efficient Topology Design

The problem of topology control is to assign per-node transmission power such that the resulting topology is energy-efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a non-cooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed and the network performance is comparable to that obtained from centralized algorithms.     

Internet拥塞控制和资源分配中的对策论分析框架

本文从一个简单的 ,适用性很强的对策论模型出发 ,首先证明了当前Internet资源分配低效率的原因 :存在拥塞的外部效应 .进而提出了一个统一的对策论框架 ,以目前最具代表性的三个支持多业务的资源分配方案 :综合服务、区分服务和基于使用的计费为例 ,推导出这些方案在集中化控制和非集中化控制之下 ,Nash均衡的存在性及其性质 .得到了每种方案中各方参与者的优化问题的解 ,并给出了相应的物理解释 .     

Non-Cooperative Multicast and Facility Location Games

We consider a multicast game with selfish non- cooperative players. There is a special source node and each player is interested in connecting to the source by making a routing decision that minimizes its payment. The mutual influence of the players is determined by a cost sharing mechanism, which in our case evenly splits the cost of an edge among the players using it. We consider two different models: an integral model, where each player connects to the source by choosing a single path, and a fractional model, where a player is allowed to split the flow it receives from the source between several paths. In both models we explore the overhead incurred in network cost due to the selfish behavior of the users, as well as the computational complexity of finding a Nash equilibrium. The existence of a Nash equilibrium for the integral model was previously established by the means of a potential function. We prove that finding a Nash equilibrium that minimizes the potential function is NP-hard. We focus on the price of anarchy of a Nash equilibrium resulting from the best-response dynamics of a game course, where the players join the game sequentially. For a game with in players, we establish an upper bound of O(radicnlog² n) on the price of anarchy, and a lower bound of Omega(log n/log log n). For the fractional model, we prove the existence of a Nash equilibrium via a potential function and give a polynomial time algorithm for computing an equilibrium that minimizes the potential function. Finally, we consider a weighted extension of the multicast game, and prove that in the fractional model, the game always has a Nash equilibrium.     

Incentive and Service Differentiation in P2P Networks: A Game Theoretic Approach

Conventional peer-to-peer (P2P) networks do not provide service differentiation and incentive for users. Therefore, users can easily obtain information without themselves contributing any information or service to a P2P community. This leads to the well known free-riding problem. Consequently, most of the information requests are directed towards a small number of P2P nodes which are willing to share information or provide service, causing the “tragedy of the commons.” The aim of this paper is to provide service differentiation in a P2P network based on the amount of services each node has provided to the network community. Since the differentiation is based on nodes' prior contributions, the nodes are encouraged to share information/services with each other. We first introduce a resource distribution mechanism for all the information sharing nodes. The mechanism is distributed in nature, has linear time complexity, and guarantees Pareto-optimal resource allocation. Second, we model the whole resource request/distribution process as a competition game between the competing nodes. We show that this game has a Nash equilibrium. To realize the game, we propose a protocol in which the competing nodes can interact with the information providing node to reach Nash equilibrium efficiently and dynamically. We also present a generalized incentive mechanism for nodes having heterogeneous utility functions. Convergence analysis of the competition game is carried out. Examples are used to illustrate that the incentive protocol provides service differentiation and can induce productive resource sharing by rational network nodes. Lastly, the incentive protocol is adaptive to node arrival and departure events, and to different forms of network congestion.     

Game of energy consumption balancing in heterogeneous sensor networks

In a multi‐hop sensor network, sensors largely rely on other nodes as a traffic relay to communicate with targets that are not reachable by one hop. Depending on the topology and position of nodes, some sensors receive more relaying traffic and lose their energy faster. Such imbalanced energy consumption may lead to server problems like network partitioning. In this paper, we study the problem of energy consumption balancing (ECB) in heterogeneous sensor networks by assuming general any‐to‐any traffic pattern. We consider both factors of transmission power and forwarding load in measuring energy consumption. To find a solution, we formulate the problem as a strategic network formation game with a new utility function. We show that this game is guaranteed to converge to strongly connected topologies which have better ECB and bounded inefficiency. We propose a localized algorithm in which every node knows only about its k‐hop neighbourhood. Through simulations on uniform and clustered networks with various densities, we show that the performance of our algorithm is comparable with global and centralized algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

博弈模型在传感器网络安全中的应用

入侵检测是传感器网络安全的重要研究内容,论文基于博弈论中的非合作模型提出了一种新型传感器网络入侵检测方案。该方案用一种只有两个参与者(攻击者和传感器网络)的非零非合作博弈模型来描述传感器网络中入侵检测问题,并证明了这个博弈模型可以达到纳什均衡,据此可以制定一个防御策略有效地提高入侵被检测到的概率。模拟试验证明这一模型是有效可行的。     

The Analysis of Nash Equilibria of the One-Shot Random-Access Game for Wireless Networks and the Behavior of Selfish Nodes

We address the fundamental question of whether or not there exist stable operating points in a network in which selfish nodes share a common channel, and if they exist, how the nodes behave at these stable operating points. We begin with a wireless communication network in which $n$ nodes (agents), which might have different utility functions, contend for access on a common, wireless communication channel. We characterize this distributed multiple-access problem in terms of a one-shot random-access game, and then analyze the behavior of the nodes using the tools of game theory. We give necessary and sufficient conditions on nodes for the complete characterization of the Nash equilibria of this game for all $ngeq 2$. We show that all centrally controlled optimal solutions are a subset of this game theoretic solution, and almost all (w.r.t. Lebesgue measure) transmission probability assignments chosen by a central authority are supported by the game theoretic solution. We analyze the behavior of the network throughput at Nash equilibria as a function of the costs of the transmitters incurred by failed transmissions. Finally, we conclude the paper with the asymptotic analysis of the system as the number of transmitters goes to infinity. We show that the asymptotic distribution of the packet arrivals converges in distribution to a Poisson random variable, and the channel throughput converges to $-(c/(1+c))ln(c/(1+c))$ with $c>0$ being the cost of failed transmissions. We also give the best possible bounds on the rates of convergence of the packet arrival distribution and the channel throughput.      

Competitive Resource Sharing Based on Game Theory in Cooperative Relay Networks

This letter considers the problem of resource sharing among a relay and multiple user nodes in cooperative transmission networks. We formulate this problem as a sellers’ market competition and use a noncooperative game to jointly consider the benefits of the relay and the users. We also develop a distributed algorithm to search the Nash equilibrium, the solution of the game. The convergence of the proposed algorithm is analyzed. Simulation results demonstrate that the proposed game can stimulate cooperative diversity among the selfish user nodes and coordinate resource allocation among the user nodes effectively.     

The delay-constrained information coverage problem in mobile sensor networks: single hop case

In this paper, we study the delay-constrained information coverage problem in mobile sensor networks. Motivated by real application needs, our formulation takes advantage of the sensor mobility for sensing information collection, which takes place when a sensor moves into the proximity (single hop) of stationary sink nodes. To the best of our knowledge, we present the first formulation for the delay-constrained information coverage problem, which targets at optimal sink nodes placement with the objective of maximizing sensing information collection within a constrained time. We prove that this problem is NP-hard even under finite search space approximation and we develop theoretical analysis to derive its upper and lower performance bounds. We then develop approximation techniques and use simulations to verify their effectiveness.