Enhancing Routing Robustness of Unstructured Peer-to-Peer Networks Using Mobile Agents

The growing popularity of Peer-to-Peer (P2P) technology in real applications in recent years has sparked increasing interest from both academia and industry in designing resilient P2P networks for these applications. Routing is a crucial technique to support successful P2P applications, and designing robust routing mechanisms is still a technical challenge for P2P systems and applications due to their inherent dynamics. This paper addresses the issue of resilient routing in unstructured P2P networks. To this end, we first propose a novel query routing approach to improving query performance in unstructured P2P networks, and then develop a mobile agent based network reconfiguration mechanism that provides a combined node and link failure diagnosis and restoration to maintain routing performance even in dynamic network environments. Simulated results show that the proposed query routing approach can achieve a good tradeoff between query hit rates and space costs in scale-free networks and that the mobile agent based network reconfiguration mechanism outperforms the existing methods in different types of network topologies.     

一种能量有效的WSN目标跟踪动态协同自组织算法

针对无线传感器网络目标跟踪应用中跟踪精度与网络能耗的权衡问题,提出一种能量有效的动态协同自组织算法(E-DCS)。根据目标预测位置和节点的位置、能量信息,建立了信息效用、通信开销和节点剩余能量的综合性能指标,并利用层次分析法确定了性能指标中各要素的权值系数。通过自适应动态成簇策略,分别设定簇首切换精度阈值和节点选择精度阈值判断是否切换簇首和选择任务节点。簇首节点根据簇内节点提供的测量信息采用序贯EKF进行状态估计。仿真结果表明,与信息驱动传感器查询(IDSQ)和自适应动态协同自组织算法(A-DCS)相比,该算法在保证跟踪精度的基础上,降低了网络能耗,有效延长了网络的生命周期。     

动态自组织P2P僵尸网络的构建及其防御

僵尸网络作为大规模攻击活动的基础平台,严重威胁网络空间安全,从预测的角度对其开展研究具有重要的现实意义。针对现有研究在终端感知、身份识别和动态对抗中存在的不足,本文概括僵尸网络生命周期,总结P2P结构僵尸网络的脆弱点,建立P2P僵尸网络动态对抗模型,分析节点真实性判断和网络拓扑优化重构的重要性。在此基础上,从攻击者视角提出一种新颖的动态自组织P2P僵尸网络模型DSBot。该模型在架构设计上可扩展至各类目标设备,通过基于可信度矩阵和真实性验证的节点安全性评估机制增强终端对抗性,并提出分阶段感染策略。借鉴无线自组网和多智能体的思路和方法,刻画节点属性多维表示和基于状态标识的动态网络框架,以此为基础设计O(N_i)更新算法、均匀连接算法和节点主动移除算法,并结合相应的初始化和调整机制提出网络自组织重构策略,从而进一步提升网络的健壮性。其中,O(N_i)更新算法确保节点的可信度,均匀连接算法降低网络暴露风险,节点主动移除算法实时移除可疑节点。从平均等待时间、命令可达率、网络连接度和重构稳定时间等方面对DSBot模型进行评估。实验结果表明,DSBot模型在效率和韧性上可满足僵尸网络命令控制机制的基本需求。最后,从终端清除、命令控制服务器打击和命令控制过程等方面讨论了可能的防御策略。本文旨在通过预测新型僵尸网络模型来完善防御解决方案。     

Ant-based survivable routing in dynamic WDM networks with shared backup paths

In this paper, we consider the problem of survivable routing in dynamic WDM networks with single link failure model. Our work mainly concerns in how to dynamically determine a protection cycle (i.e., two link-disjoint paths between a node pair) to establish a dependable lightpath with backup paths sharing. This problem is identified as NP-complete, thus a heuristic for finding near optimal solution with reasonable computation time is usually preferred. Inspired from the principle of ant colony optimization, we propose in this paper an ant-based mobile agents algorithm for this problem with improved blocking performance. To enable the new ant-based algorithm, we propose to use on each network node both a routing table that contains a set of feasible protection cycles between source destination nodes and also a pheromone table for mobile agents. By keeping a suitable number of mobile agents in a network to continually and proactively update the routing tables based on the current network congestion state, the routing solution of a connection request can be obtained with a reasonable computation time. Extensive simulation results upon the ns-2 network simulator and two typical network topologies show that our new algorithm can achieve a significantly lower blocking probability than the promising algorithm for dynamic lightpath protection proposed in [11] with a comparable computation complexity.     

Consensus of second-order multi-agent systems in the presence of locally bounded faults

We propose an algorithm for consensus of second-order sampled-data multi-agent systems in the presence of misbehaving agents. Each normal agent updates its states following a predetermined control law based on local information while some malicious agents make updates arbitrarily. The normal agents do not know the global topology of the network, but have prior knowledge on the maximum number of malicious agents in their neighborhood. Under the assumption that the network has sufficient connectivity in terms of robustness, we develop a resilient algorithm where each agent ignores the neighbors which have large and small position values to avoid being influenced by malicious agents.     

DTN中基于节点质量的自适应散发和等待路由

针对节点活动性的差异,给出了节点质量的定义,提出了基于节点质量的自适应散发和等待路由协议。该协议通过引入节点质量的观点来动态地转发报文拷贝数,从而避免了二分散发和等待路由协议在转发报文拷贝数时的盲目性。仿真结果显示,该协议更能够适应自组织状况下动态的网络拓扑,在改善报文递交率和延迟的同时能显著提高报文的递交效用、减小网络开销率。     

Adaptive learning algorithm of self-organizing teams

In order to improve the ability of achieving good performance in self-organizing teams, this paper presents a self-adaptive learning algorithm for team members. Members of the self-organizing teams are simulated by agents. In the virtual self-organizing team, agents adapt their knowledge according to cooperative principles. The self-adaptive learning algorithm is approached to learn from other agents with minimal costs and improve the performance of the self-organizing team. In the algorithm, agents learn how to behave (choose different game strategies) and how much to think about how to behave (choose the learning radius). The virtual team is self-adaptively improved according to the strategies’ ability of generating better quality solutions in the past generations. Six basic experiments are manipulated to prove the validity of the adaptive learning algorithm. It is found that the adaptive learning algorithm often causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence the performance of the virtual self-organizing team. This paper considered the influence of relationships in self-organizing teams over existing works. It is illustrated that the adaptive learning algorithm is beneficial to both the development of self-organizing teams and the performance of the individual agent.     

Survival in cyberspace

The immune system response of the vertebrates demonstrates an extremely adaptive and resilient defensive capability against a broad spectrum of pathogenic attacks. The field of artificial immune systems (AISs) aims to replicate this capability in the digital environment. In particular, we would like to understand adaptive survivability and defence in large-scale computing networks. In this paper we discuss some of the background concepts to AIS and focus on one specific aspect required to achieve a digital immune system, i.e. the social dynamics of competitive and co-operative defence. In particular, the ability of an information network to maintain itself in the face of continuous perturbation raises more complex issues related to system metabolism and network topology.In order to investigate these processes a multi-agent simulation model has been developed that demonstrates a self-organising group formation capability and a collective immune response. In this model each agent is susceptible to viral infections passed between the agents and has local sensors and a complex metabolic state that reflects its current health. We then introduced an artificial immune system to each agent that allowed learned ‘antibody’ solutions to be exchanged between the agents within a social group. The health of a co-operative group was observed to improve by over 90%, relative to isolated agents or non-cooperative groups. The specific solutions advocated are therefore to utilise distributed defence mechanisms and the monitoring of metabolic processes to detect intrusions. Finally, the paper considers the problem of how we might utilise such knowledge to develop greater security and robustness in real-world networks using distributed agent systems.     

Ad Hoc网络环境中避免虫洞攻击的路由算法

根据Ad Hoc网络环境中虫洞攻击的特点,提出了一种基于移动Agent的路由算法。移动Agent在网络中根据一定的运行策略进行移动,并不断地和所经历的节点进行数据交换,由此了解网络中所有节点的路由连接信息,在此基础上形成一个节点信息矩阵表,并根据所形成的矩阵表,采用图论中的广度优先搜索算法来决定数据报文的最佳传输路径,同时产生若干条备用路径,快速地发送数据报文。仿真实验结果表明,由于各节点通过矩阵表可以充分了解网络中每个节点的地理位置,因此可以很好地抵御虫洞的攻击;同时由于仅使用很少的Agent便获得较多的全局信息,因此可以大大地减少维持节点信息而产生的开销,具有很高的效率和鲁棒性。     

基于奖励高速路网络的多智能体强化学习中的全局信用分配算法

针对多智能体系统中联合动作空间随智能体数量的增加而产生的指数爆炸的问题,采用"中心训练-分散执行"的框架来避免联合动作空间的维数灾难并降低算法的优化代价.针对在众多的多智能体强化学习场景下,环境仅给出所有智能体的联合行为所对应的全局奖励这一问题,提出一种新的全局信用分配机制——奖励高速路网络(RHWNet).通过在原有...     

Multi-sector multi-range control for self-organizing wireless networks

In this paper, we propose a distributed multi-sector multi-range (MSMR) control algorithm for supporting self-organizing wireless networks. The algorithm enables us to reduce the unnecessary coverage with fine-tuned range control and also to increase the network-wide capacity with enhanced spatial reusability. The proposed algorithm discovers neighboring nodes within the maximum transmission range at every node, divides its transmission area into multiple non-overlapping angular sectors of a given degree, chooses the home sector for each neighboring node according to its relative position, and constructs a spanning subgraph per sector by determining appropriate transmission range to maintain connectivity. Since the range control influences on network connectivity directly, we prove in the first place that the proposed algorithm preserves both network-wide and local connectivity as far as both connectivity exist in the network that uses the maximum transmission range. In order to investigate the performance of the proposed algorithm, we implemented it in the ns-2 simulator, and performed an extensive set of simulation study in comparison with other transmission range control schemes. The simulation results indicate that the proposed scheme is superior to other schemes with respect to the network-wide throughput and its normalized value per energy in various simulation configurations. In specific, the algorithm achieves minimally one order and maximally two orders of magnitude improvement in those performance evaluations. The improvement becomes more salient as the number of nodes increases and is immune to traffic type, network size, node distribution, or node density.     

A solution for the cooperative formation-tracking problem in a network of completely unknown nonlinear dynamic systems without relative position information

In this paper, a solution of the formation-tracking problem is provided for a network that contains nonlinear agents with completely unknown dynamics and working under unknown disturbances. By the combination of a cooperative observer and an adaptive model-free controller, the requirement of inter-agent relative position information in the network is eliminated. Here, a cooperative observer is designed to estimate the time-varying reference trajectory and the time-varying parameters of the desired formation topology at each agent in the network. The stability of the proposed cooperative observer is analysed using Lyapunov analysis. Utilising the cooperative observer, the formation-tracking problem in the network of dynamic agents is transformed to a tracking problem in a single agent system. Moreover, an adaptive model-free control policy is applied to each agent for providing the tracking objective. Utilising the algebraic connectivity originating from graph theory, this model-free control algorithm is formulated to scale-up for a network of multi-agents. The proposed decentralised controller includes two model-free adaptive laws for online estimating of the completely unknown dynamics at each agent in the network. The application of the proposed solution is simulated for a network of four quadrotors with unknown internal dynamics and unknown external disturbances.     

基于压缩感知的异构网络数据动态重构算法

针对异构网络数据资源存在节点及链路均衡度过低且数据重构资源分配路径误差较大的问题,设计一种基于节点拓扑感知的异构网络数据动态重构算法。利用模糊核聚类算法将数据样本集映射至高维空间内,聚类目标数据,根据节点在网络中对应的坐标构建异构网络拓扑模型及数据权矩阵模型;设置路由器物理链路通信图,根据链路带宽约束优化数据重构资源的分配路径,将节点核心区数据重构,并分配至聚类节点来感知数据的动态变化,为每一个核心聚类节点拓扑感知一个路由器数据的动态变化,来实现全局的数据动态重构,至此完成基于节点拓扑感知的异构网络数据动态重构算法的设计。设计仿真实验,测试算法的节点均衡度与链路均衡度。实验结果表明,设计的算法的节点均衡度为0.93,链路均衡度为0.90,均高于对比方法,因此可以得出,该算法的资源均衡度更好。     

Evaluation of a selective distributed discovery strategy in a fully decentralized biologically inspired environment

The increased demand and complexity of services operating within open distributed environments has emphasized the need for systems that are adaptive, self-organizing and more robust. In order to address these issues some agent oriented approaches have adopted ideas from natural systems as possible solutions. The introduction of biological properties, especially birth and death of agents as expected events, generates an extremely dynamic environment where it is difficult to maintain the overall connectivity of the overlay network and facilitate efficient discovery processes. In this paper we evaluate the performance of a selective discovery mechanism in a distributed bio-inspired multi-agent community through a simulation study. The primary focus of the study is on the impacts which death and (sexual/asexual) reproduction events have on the effectiveness of the discovery process in different overlay networks.     

基于自组织模糊RBF网络的连续空间Q学习

针对连续空间下的强化学习控制问题,提出了一种基于自组织模糊RBF网络的Q学习方法．网络的输入为状态,输出为连续动作及其Q值,从而实现了“连续状态—连续动作”的映射关系．首先将连续动作空间离散化为确定数目的离散动作,采用完全贪婪策略选取具有最大Q值的离散动作作为每条模糊规则的局部获胜动作．然后采用命令融合机制对获胜的离散动作按其效用值进行加权,得到实际作用于系统的连续动作．另外,为简化网络结构和提高学习速度,采用改进的RAN算法和梯度下降法分别对网络的结构和参数进行在线自适应调整．倒立摆平衡控制的仿真结果验证了所提Q学习方法的有效性．     

基于自适应PSO和类别分解的多任务串行联盟生成*

现有的联盟生成方案多针对一个agent只能加入一个联盟,不利于联盟总效用的最大化以及联盟中agent能力的充分利用。提出了基于能力类别的agent分解策略,通过定义子agent使得agent可以同时加入多个联盟,在此基础上设计了基于二维离散粒子群的多任务串行联盟生成算法,并对粒子的惯性权重进行动态自适应调整;最后通过算例验证了该方法的有效性。     

A randomized algorithm for the joining protocol in dynamic distributed networks

We describe a randomized algorithm for assigning neighbours to vertices joining a dynamic distributed network. The aim of the algorithm is to maintain connectivity, low diameter and constant vertex degree. On joining each vertex donates a constant number of tokens to the network. These tokens contain the address of the donor vertex. The tokens make independent random walks in the network. A token can be used by any vertex it is visiting to establish a connection to the donor vertex. This allows joining vertices to be allocated a random set of neighbours although the overall vertex membership of the network is unknown. The network we obtain in this way is robust under adversarial deletion of vertices and edges and actively reconnects itself.     

Engineering self-organizing referral networks for trustworthy service selection

Developing, maintaining, and disseminating trust in open, dynamic environments is crucial. We propose self-organizing referral networks as a means for establishing trust in such environments. A referral network consists of autonomous agents that model others in terms of their trustworthiness and disseminate information on others' trustworthiness. An agent may request a service from another; a requested agent may provide the requested service or give a referral to someone else. Possibly with its user's help, each agent can judge the quality of service obtained. Importantly, the agents autonomously and adaptively decide with whom to interact and choose what referrals to issue, if any. The choices of the agents lead to the evolution of the referral network, whereby the agents move closer to those that they trust. This paper studies the guidelines for engineering self-organizing referral networks. To do so, it investigates properties of referral networks via simulation. By controlling the actions of the agents appropriately, different referral networks can be generated. This paper first shows how the exchange of referrals affects service selection. It identifies interesting network topologies and shows under which conditions these topologies emerge. Based on the link structure of the network, some agents can be identified as authorities. Finally, the paper shows how and when such authorities emerge. The observations of these simulations are then formulated into design recommendations that can be used to develop robust, self-organizing referral networks.     

Interactive Navigation of Heterogeneous Agents Using Adaptive Roadmaps

We present a novel algorithm for collision-free navigation of a large number of independent agents in complex and dynamic environments. We introduce adaptive roadmaps to perform global path planning for each agent simultaneously. Our algorithm takes into account dynamic obstacles and interagents interaction forces to continuously update the roadmap based on a physically-based dynamics simulator. In order to efficiently update the links, we perform adaptive particle-based sampling along the links. We also introduce the notion of 'link bands' to resolve collisions among multiple agents. In practice, our algorithm can perform real-time navigation of hundreds and thousands of human agents in indoor and outdoor scenes.     

基于自组织聚类及自决定聚首的路由算法

针对结构化对等网系统的拓扑失配问题,提出一种基于自组织聚类及自决定聚首的路由算法。该算法为每个节点设置不同的聚类邻居集,每个节点根据自己的能力决定是否成为自己邻居集的聚首。节点资源的查找按照自身邻居集查找、聚首邻居集查找及常规算法查找3步进行。实验结果表明,改进后算法具有较高的路由查找成功率,可较好地解决拓扑失配问题。