一种基于马尔可夫博弈的能量均衡路由算法

针对无线传感器网络中耗能不均问题,引入马尔可夫博弈理论,构建了无线传感器网络的马尔可夫博弈模型.在能量均衡路由分析的基础上,给出了一种基于马尔可夫博弈的能量均衡路由算法,该算法从无线传感器网络整体耗能出发,兼顾节点之间的合作.定义了能量和信誉值的二元收益函数,给出了节点转发的状态转移概率,根据收益函数进行能量调节,求解出能量和收益之间的均衡系数——纳什均衡,实现了节点能量的均衡消耗,延长了网络的生命周期.使用PRISM概率仿真工具进行仿真,验证了该博弈模型存在纳什均衡点,同时表明该模型能促进节点之间合作,最大化无线传感器网络的生命周期.     

无线传感器网络路由中合作性重复博弈模型的研究

无线传感器网络中,节点能耗、路径可靠度以及节点的死亡时间是传感器网络路由需要考虑的关键因素.为了提高能量利用率和传感器网络收益,在节点理性且自私的条件下,运用博弈论方法提出了一种基于节点合作的数据包发送/转发的重复博弈模型,设计了一个与路径连通度和节点能量消耗有关的收益函数,采用惩罚机制使重复博弈模型存在子博弈精炼纳什均衡,降低了自私节点背叛的可能性.实验结果表明:采用惩罚机制的重复博弈能够提高网络的收益,同时也提高了网络吞吐量,任何自私节点的不合作行为都导致节点的能量浪费和节点的整体收益下降.     

基于博弈论的无线传感器网络簇间路由选择算法

针对无线传感器网络(WSN)中,网络覆盖范围大,但传感器节点通信范围有限,长距离传输容易造成数据丢失的问题,提出了一种基于博弈论的无线传感器网络簇间路由算法,通过建立以网络服务质量(QoS)和节点剩余能量为效用函数的博弈模型,并求解其纳什均衡来解决以上问题。仿真结果表明：所提出的博弈模型在优化网络服务质量、降低节点能耗的同时,延长了整个网络的生存时间。     

认知无线传感器网络中基于流量的多路径路由

人们对无线传感器网络多路径路由问题的讨论很多,但从流量分配的角度来讨论路由问题相对较少。主要讨论无线传感器网络通过认知无线电租赁有限的频谱资源,从网络流量分配的角度,利用微分博弈模型来解决无线传感器网络多路径路由问题,同时也考虑了多路径路由的稳定性及经济效益问题。通过求微分博弈反馈纳什均衡的解对n条路径进行了动态流量分配,给出了可以提高认知无线传感器网络有效性、稳定性、可靠性及经济效益的多路径路由算法。     

WSN中基于完全信息动态博弈的可信路由研究

如何将信任管理运用于无线传感器网络(WSN)的路由选择成为近年来研究的一个热点.考虑无线传感器网络的节点安全度、能量约束以及传输可靠度等三个基本因素,完成节点可信度以及最优可信路径(MTP)的度量,引入博弈机制对节点参与路由进行建模,基于可信度设计了一个payoff函数,通过相应的奖惩机制抑制WSN路由中普遍存在的恶意节点、自私节点以及激励措施问题.实验结果表明与WSN中传统的典型路由算法相比,该最优可信路径算法在网络生存时间,路径安全度,传输可靠性等因素方面综合性能显著改善.     

无线多跳网络中基于博弈论的协作激励机制研究

无线多跳网络中网络传输性能容易受到自私节点的影响。本文首先对目前的节点协作激励机制进行了总结,然后,针对分簇路由中簇间路由场景,运用博弈论中非合作博弈的思想,建立博弈模型以激励簇内节点通过协作通信的方式帮助簇头进行数据包的转发,最后给出了基于非合作博弈的激励机制中纳什均衡解的求解过程。     

基于蚁群优化的无线传感器网络能量均衡路由算法

如何有效使用无线传感器节点有限的能量来最大化网络的寿命是无线传感器网络研究的重要问题.网络能量是否均衡消耗对网络寿命有着决定性的影响.本文将蚁群优化算法应用于无线传感器网络的路径选择,提出一种基于蚁群优化的无线传感器网络能量均衡路由算法.该算法利用蚁群的动态适应性和寻优能力在网络最短路径和能量均衡消耗之间进行平衡,以达到网络能量的优化均衡消耗,进而延长整个网络的寿命.仿真实验表明,该算法在延长网络寿命方面效果较显著,与最短路径路由算法相比网络寿命延长超过33%.     

基于演化博弈的无线传感器网络节能分簇路由算法

考虑到无线传感器网络中节点在冲突环境下决策时具有有限理性,近年来学者引入博弈论解决传感网分簇路由中自私节点的簇头选举问题。以往经典博弈分簇路由算法要求知道所有参与者行动的完全信息,并假设节点完全理性,这对于资源有限的传感器节点不切实际。本文提出了一种基于演化博弈论的无线传感器网络节能分簇路由算法(EECEG),通过演化博弈复制动态方程证明存在演化稳定策略(ESS)。算法将所有节点模拟为自私的博弈参与者,参与者可决策宣称自己成为簇头候选者(D)或不成为候选者(ND)。所有参与者根据自身剩余能量、邻居节点个数等因素自私决策,通过观察和模仿对手进行演化,直到收益均衡。实验结果表明,EECEG协议可有效延长网络生命周期,均衡节点间能耗,同时使数据传输更高效。     

基于信任的无线传感器网络时隙分配博弈分析

为了帮助无线传感器网络作出既有利于自身收益又能抑制恶意节点的决策,提出了一种信任激励的时隙分配博弈模型。根据收益矩阵对恶意节点和簇头之间的非零和博弈关系进行了认知和分解,指出了破坏行为可以抑制的原因是纳什均衡可以作为惩罚阻止节点偏离收益更高的策略组合。在此基础上建立了博弈模型,然后证明了无限重复博弈中的纳什回归策略成为子博弈完美均衡的充分必要条件。采用单轮纳什均衡惩罚合作性策略的偏离者,从而使恶意节点与簇头的无限重复博弈能够产生合作效应。仿真实验结果表明,根据该模型作出的决策可以增加网络收益并抑制恶意节点的破坏行为。     

无线传感器网络中能量有效自适应路由算法研究

针对无线传感器网络寿命最大化问题,基于无线传感器节点能耗分布特点和数据传输能耗模型,建立无线传感器网络生存周期的数学优化模型,并针对最小能耗路由的能耗不均衡问题和能量均衡路由的能耗开销问题,综合考虑网络中节点的剩余能量和节点间发送数据的能耗,提出一个适合无线多跳传感器网络的自适应路由算法。仿真结果表明,提出的路由算法能充分地利用有限的能量资源,较大地延长网络生存周期。     

基于最优反应均衡的传感网恶意程序传播抑制方法

为抑制传感网恶意程序传播,在考虑传感网恶意程序传播参与者"有限理性"的基础上,提出一种基于最优反应均衡的方法.根据传感网恶意程序传播过程中的博弈分析,建立传感网恶意程序传播阶段博弈模型以反应传感网恶意程序和传感网入侵检测系统之间的博弈交互过程.由参与者之间博弈交互持续进行的事实,建立传感网恶意程序传播重复博弈模型.使用最优反应均衡预测传感网恶意程序的行为以解决重复博弈纳什均衡解求解困难的问题,给出抑制传感网恶意程序传播的算法.实验分析了参与者基于最优反应均衡的策略,对所提出方法的有效性进行了验证.     

基于博弈论能耗均衡的WSN非均匀分簇路由协议

在无线传感器网络(WSN)的分簇路由算法中,节点间能耗不均容易引发 “能量空洞”现象,影响整个网络的性能。针对这个问题,提出了一种基于博弈论能耗均衡的非均匀分簇路由(GBUC)算法。该算法在分簇阶段,采用非均匀分簇结构,簇的半径由簇头到汇聚节点的距离和剩余能量共同决定,通过调节簇头在簇内通信的能耗和转发数据的能耗来达到能耗的均衡;在簇间通信阶段,通过建立一个以节点剩余能量和链路可靠度为效益函数的博弈模型,利用其纳什均衡的解来寻找联合能耗均衡、链路可靠性的最优传输路径,从而提高网络性能。仿真结果表明:与能量高效的非均匀分簇(EEUC)算法和非均匀分簇节能路由(UCEER)算法相比,GBUC算法在均衡节点能耗、延长网络生命周期等性能方面有显著的提高。     

Sensor-centric energy-constrained reliable query routing for wireless sensor networks

Standard wireless sensor network models emphasize energy efficiency and distributed decision-making by considering untethered and unattended sensors. To this we add two constraints—the possibility of sensor failure and the fact that each sensor must tradeoff its own resource consumption with overall network objectives. In this paper, we develop an analytical model of energy-constrained, reliable, data-centric information routing in sensor networks under all the above constraints. Unlike existing techniques, we use game theory to model intelligent sensors thereby making our approach sensor-centric. Sensors behave as rational players in an N-player routing game, where they tradeoff individual communication and other costs with network wide benefits. The outcome of the sensor behavior is a sequence of communication link establishments, resulting in routing paths from reporting to querying sensors. We show that the optimal routing architecture is the Nash equilibrium of the N-player routing game and that computing the optimal paths (which maximizes payoffs of the individual sensors) is NP-Hard with and without data-aggregation. We develop a game-theoretic metric called path weakness to measure the qualitative performance of different routing mechanisms. This sensor-centric concept which is based on the contribution of individual sensors to the overall routing objective is used to define the quality of routing (QoR) paths. Analytical results on computing paths of bounded weakness are derived and game-theoretic heuristics for finding approximately optimal paths are presented. Simulation results are used to compare the QoR of different routing paths derived using various energy-constrained routing algorithms.     

针对窃听问题的马尔可夫博弈路由模型的研究

在随机路由的基础上,给出一种针对窃听问题的马尔可夫博弈路由模型(Markov Game Theory-based Routing , MUBR)。给出的模型以发送者和窃听者为马尔可夫博弈双方,发送者通过概率进行数据传输,增加了窃听者窃听信息的难度。模型通过收益函数计算纳什均衡点,找出最优路径。使用PRISM工具进行仿真,结果表明MGBR中存在纳什均衡点,在纳什均衡点处信息被窃听的概率最小;给出信息在纳什均衡点处被窃听的概率变化趋势,与基于最小跳数算法的路由协议相比,它降低了信息被窃听的概率。     

无线传感器网络多径路由协议综述

由于无线传感器网络不同于传统的自组织网络,已有自组织网络路由协议不能有效应用于传感器网络中.文献中已提出不少无线传感器网络路由协议,但这些协议大多针对单路径情况.当链路失效时,单径路由协议需要重新发现新的路由,从而会对传输延时、能耗和可靠性带来较大影响.采用多径路由协议可弥补单径路由协议的不足,有利于提高数据传输的可靠性和实现负载平衡.通过对目前文献中几种典型的多径路由协议的分析和比较,指出进一步研究中值得关注的问题.     

A Learning Invader for the “Guarding a Territory” Game

This paper explores the use of a learning algorithm in the “guarding a territory” game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case the guard is superior (faster) to the invader. We will also consider using models with non-holonomic constraints. A control system is designed and optimized for the invader to play the game and reach Nash Equilibrium. The paper shows how the learning system is able to adapt itself. The system’s performance is evaluated through different simulations and compared to the Nash Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.     

Random Walk Routing in WSNs with Regular Topologies

Topology is one of the most important characteristics for any type of networks because it represents the network's inherent properties and has great impact on the performance of the network. For wireless sensor networks (WSN), a well-deployed regular topology can help save more energy than what a random topology can do. WSNs with regular topologies can prolong network lifetime as studied in many previous work. However, little work has been done in developing effective routing algorithms for WSNs with regular topologies, except routing along a shortest path with the knowledge of global location information of sensor nodes. In this paper, a new routing protocol based on random walk is proposed. It does not require global location information. It also achieves load balancing property inherently for WSNs which is difficult to achieve by other routing protocols. In the scenarios where the message required to be sent to the base station is in comparatively small size with the inquiry message among neighboring nodes, it is proved that the random walk routing protocol can guarantee high probability of successful transmission from the source to the base station with the same amount of energy consumption as the shortest path routing. Since in many applications of WSNs, sensor nodes often send only beep-like small messages to the base station to report their status, our proposed random walk routing is thus a viable scheme and can work very efficiently especially in these application scenarios. The random walk routing provides load balancing in the WSN as mentioned, however, the nodes near to the base station are inevitably under heavier burden than those far away from the base station. Therefore, a density-aware deployment scheme is further proposed to guarantee that the heavy-load nodes do not affect the network lifetime even if their energy is exhausted. The main idea is deploying sensors with different densities according to their distance to the base station. It will be shown in this paper that incorporating the random walk routing protocol with the density-aware deployment scheme can effectively prolong the network lifetime.     

Reaching pareto-optimality in prisoner’s dilemma using conditional joint action learning

We consider the learning problem faced by two self-interested agents repeatedly playing a general-sum stage game. We assume that the players can observe each other’s actions but not the payoffs received by the other player. The concept of Nash Equilibrium in repeated games provides an individually rational solution for playing such games and can be achieved by playing the Nash Equilibrium strategy for the single-shot game in every iteration. Such a strategy, however can sometimes lead to a Pareto-Dominated outcome for games like Prisoner’s Dilemma. So we prefer learning strategies that converge to a Pareto-Optimal outcome that also produces a Nash Equilibrium payoff for repeated two-player, n-action general-sum games. The Folk Theorem enable us to identify such outcomes. In this paper, we introduce the Conditional Joint Action Learner (CJAL) which learns the conditional probability of an action taken by the opponent given its own actions and uses it to decide its next course of action. We empirically show that under self-play and if the payoff structure of the Prisoner’s Dilemma game satisfies certain conditions, a CJAL learner, using a random exploration strategy followed by a completely greedy exploitation technique, will learn to converge to a Pareto-Optimal solution. We also show that such learning will generate Pareto-Optimal payoffs in a large majority of other two-player general sum games. We compare the performance of CJAL with that of existing algorithms such as WOLF-PHC and JAL on all structurally distinct two-player conflict games with ordinal payoffs.     

An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.     

WSN中利用贝叶斯博弈的数据传递策略

在无线传感器网络中,数据的传递策略对网络的能量损耗具有重要的影响,为此,提出了一个基于贝叶斯博弈的数据传递模型。在该模型中网络节点为了获取最大的收益,在考虑自身能量水平的基础上,适当的调整发送/转发的数据量。当节点发送/转发的数据满足一定条件时,网络存在均衡状态。仿真结果表明,该基于博弈论的数据传递策略在均衡状态下能够明显降低能量损耗,延长网络的使用寿命。