基于ISP分布的网络蠕虫检测系统

传统的通过追踪TCPSYN包来发现蠕虫扫描源的方法需要耗费探测点大量的存储和计算资源。基于ISP分布,提出了一种通过检测TCPRESET包来发现扫描源的网络蠕虫早期检测系统。它能够检测疑似网络蠕虫,防御伪造攻击,在正常和模拟蠕虫扫描情况下分别能够减轻探测点59.4%和28.9%的负荷。     

CONRO: Towards Deployable Robots with Inter-Robots Metamorphic Capabilities

Metamorphic robots are modular robots that can reconfigure their shape. Such capability is desirable in tasks such as earthquake search and rescue and battlefield surveillance and scouting, where robots must go through unexpected situations and obstacles and perform tasks that are difficult for fixed-shape robots. The capabilities of the robots are determined by the design specification of their modules. In this paper, we present the design specification of a CONRO module, a small, self-sufficient and relatively homogeneous module that can be connected to other modules to form complex robots. These robots have not only the capability of changing their shape (intra-robot metamorphing) but also can split into smaller robots or merge with other robots to create a single larger robot (inter-robot metamorphing), i.e., CONRO robots can alter their shape and their size. Thus, heterogeneous robot teams can be built with homogeneous components. Furthermore, the CONRO robots can separate the reconfiguration stage from the locomotion stage, allowing the selection of configuration-dependent gaits. The locomotion and automatic inter-module docking capabilities of such robots were tested using tethered prototypes that can be reconfigured manually. We conclude the paper discussing the future work needed to fully realize the construction of these robots.     

空间连接系统分布式并行滤波

给出了空间连接系统的一种分布式并行滤波算法．该算法的特点是利用多个计算单元对全部子系统的 状态同时进行估计．每个计算单元仅利用当前子系统的输出和相邻计算单元的估计结果,对当前子系统的状态进行 估计,并将结果传递给相邻计算单元．在线递推计算时,该算法在计算复杂度方面仅相当于单个子系统的卡尔曼滤 波．仿真结果表明,该算法的滤波精度十分接近最优的集总式卡尔曼滤波精度,二者的稳态滤波误差仅差5%．     

A Hybrid Deployable Dynamic Traffic Assignment Framework for Robust Online Route Guidance

Randomness in time-dependent origin-destination (O-D) demands and/or network supply conditions, and the computational tractability of potential solution methodologies are two major concerns for the online deployment of dynamic traffic assignment (DTA) under real-time traffic management systems. Most existing DTA models ignore these concerns and/or make unrealistic assumptions, precluding their online applicability. In this paper, a hybrid approach consisting of offline and online strategies is proposed to address the online stochastic dynamic traffic assignment problem. The basic idea is to address the computationally intensive components offline, while efficiently and effectively reacting to the unfolding conditions online. The offline component seeks a robust initial solution vis-à-vis randomness in O-D demands using historical O-D demand data. Termed the offline a priori solution, it is updated dynamically online based on unfolding O-D demands and incidents. The framework circumvents the need for accurate O-D demand and incident likelihood prediction models online, while exploiting historical O-D demand and incident data offline. Results of simulation experiments highlight the robustness of the hybrid approach with respect to online variations in O-D demand, its ability to address incident situations effectively, and its online efficiency.     

Distributed Filtering for Multi-sensor Systems with Missing Data

This paper studies distributed estimation problems for multi-sensor systems with missing data. Missing data may occur during sensor measuring or data exchanging among sensor nodes due to unreliability of communication links or external disturbances. Missing data include random missing measurements of sensor itself and random missing estimates of neighbor nodes. Three distributed Kalman filter (DKF) algorithms with the Kalman-like form are designed for each sensor node. When it is available whether a datum is missing or not at each time, an optimal DKF (ODKF) dependent on the knowledge of missing data is presented, where filter gains and covariance matrices require online computing. To reduce online computational cost, a suboptimal DKF (SDKF) is presented, where filter gains and covariance matrices dependent on missing probabilities can be computed offline. When it is unavailable whether a datum is missing or not, a probability-based DKF (PDKF) dependent on missing probabilities is presented. For each DKF algorithm, an optimal Kalman filter gain for measurements of sensor itself and different optimal consensus filter gains for state estimates of its neighbor nodes are designed in the linear unbiased minimum variance (LUMV) sense, respectively. Mean boundedness of covariance matrix of the proposed ODKF is analyzed. Stability and steady-state properties of the proposed SDKF and PDKF are analyzed. Also, the performance of three DKF algorithms is compared. Simulation examples demonstrate effectiveness of the proposed algorithms.     

Towards a Mobile Code Management Environment for Complex,Real-Time,Distributed Systems

We present a novel mobile code management environment,currently under design and development. Our design employs anopen architecture, suitable for ``plug-and-play' with COTS andother groups' tools. While we have studied new algorithms, costand objective functions, and other fundamental issues, the maincontribution of this experimental research work is in the environmentitself. It should be noted that networked platforms, such asthe World Wide Web, are inherently not suitable for traditional,predictable real-time applications. Thus, real-time concernsnecessarily need to be blended with others concerns, and thetarget applications, making use of our environment, will toobe a blend of partially hard real-time and partially (or mostly)soft-real- time ones. The prototype environment will thereforesupport performance-based analysis and management focusing notonly on predictability but also on compilation, efficiency, safetyand other tradeoffs. We have selected the Java language and itsbytecode format as a representation for mobile code as well asa language for our implementation.     

基子自适应泸波器理论的自相似流量预测

根据流量特徵预测其到达速率是基于测量的网络控制机制的关键问题。本文研究了基于最小均方（Least Mean-Square，LMS）自适应滤波器对自相似流量进行速率预测的方法。通过分析对不同实际流量记录和仿真流量的预测结果，发现该方法不但减小了采用指数加权平均估计带来的计算复杂度，而且其滤波器系数自适应特性可以有效地跟踪流量的高度变化，从而更加准确地估计流量速率。此外，实验还得出了用LMS自适应滤波器进行流量预测的几个基本参数。     

Towards A Role-Based Framework for Distributed Systems Management

Roles have been widely used for modeling theauthority, responsibility, functions, and interactions,associated with manager positions within organizations.In this paper, we discuss the issues related tospecifying roles for both human and automated managersof distributed computer systems. The starting point isthat a role can be defined in terms of the authorizationand obligation policies, for a particular managerposition, which specify what actions the manager ispermitted or is obliged to do on a set of targetobjects. This permits individuals to be assigned orremoved from positions without respecifying the policiesfor the role. However these policies are insufficientfor fully specifying relationships between managers andthe targets they manage or between different managerroles. There is a need to specify the interactionprotocols and how managers coordinate and synchronizetheir activities. The role-based framework consists ofa set of tools enabling the creation of roles frompolicies, the specification of the concurrencyconstraints for role activities and the specification ofprotocols for role interaction. In addition, the issuesrelated to conflicts which can occur between policieswithin a role or between interacting roles are brieflydiscussed.     

基于上下文的分布式协同过滤推荐技术

针对传统协同过滤推荐技术应用于大规模动态数据集时难以兼顾准确度和效率的问题,提出一种基于上下文的分布式协同过滤推荐技术,引入推荐上下文的概念,并在此基础上充分考虑用户的即时兴趣以提高推荐的准确度,采用评分矩阵的分布式存储和计算以提高推荐的效率。实验结果表明,该分布式协同过滤技术能同时保证推荐的准确度和效率,使其在大规模动态数据集上的应用更具优势。     

Distributed Consensus Filtering in Sensor Networks

In this paper, a new filtering problem for sensor networks is investigated. A new type of distributed consensus filters is designed, where each sensor can communicate with the neighboring sensors, and filtering can be performed in a distributed way. In the pinning control approach, only a small fraction of sensors need to measure the target information, with which the whole network can be controlled. Furthermore, pinning observers are designed in the case that the sensor can only observe partial target information. Simulation results are given to verify the designed distributed consensus filters.     

一种基于点路结合的骨干网P2P缓存部署方法

P2P应用的广泛流行给ISP骨干网络带来了前所未有的流量压力,P2P缓存(peer-to-peer caching)技术是目前缓解这种流量压力的最有效手段之一,缓存部署方法对P2P缓存系统的运行效率有重要影响.已有缓存部署方法分为两类：基于骨干节点的部署方法(node-based cache deployment, NCD)和基于骨干链路的部署方法(link-based cache deployment, LCD).在不同的P2P流量分布情形下,NCD与LCD各有优劣,但是,这两类方法未能充分发挥缓存的性能.提出一种基于点路结合的骨干网P2P缓存部署方法(node-Link based cache deployment, NLCD),根据缓存部署过程中P2P流量分布和缓存存储状态的动态变化,灵活选择骨干节点或骨干链路作为部署位置.建立了以网络负载最小化为目标的缓存部署模型,基于该模型将P2P缓存部署问题建模为一个最优化问题,由于流量分布和缓存状态会在部署过程中不断变化,不具有最优子结构性质.证明了该最优化问题为NP完全问题,并设计了一种启发式贪婪算法进行求解.实验结果表明,针对典型的H&S型、Ladder型骨干网络拓扑,使用NLCD的平均链路使用率比使用LCD低5%~15%,比使用NCD低7%~30%.     

治安卡口网络监控系统软硬件技术研究与实现治安卡口网络监控系统软硬件技术研究与实现

探讨了基于计算机网络的分布式治安卡口监控系统的设计思路和实现方法。给出了系统的软件、硬件结构及工作原理,并讨论了线程之间的调度关系和同步方法。     

A Distributed Approach for Coordination of Traffic Signal Agents

Innovative control strategies are needed to cope with the increasing urban traffic chaos. In most cases, the currently used strategies are based on a central traffic-responsive control system which can be demanding to implement and maintain. Therefore, a functional and spatial decentralization is desired. For this purpose, distributed artificial intelligence and multi-agent systems have come out with a series of techniques which allow coordination and cooperation. However, in many cases these are reached by means of communication and centrally controlled coordination processes, giving little room for decentralized management. Consequently, there is a lack of decision-support tools at managerial level (traffic control centers) capable of dealing with decentralized policies of control and actually profiting from them. In the present work a coordination concept is used, which overcomes some disadvantages of the existing methods. This concept makes use of techniques of evolutionary game theory: intersections in an arterial are modeled as individually-motivated agents or players taking part in a dynamic process in which not only their own local goals but also a global one has to be taken into account. The role of the traffic manager is facilitated since s/he has to deal only with tactical ones, leaving the operational issues to the agents. Thus the system ultimately provides support for the traffic manager to decide on traffic control policies. Some application in traffic scenarios are discussed in order to evaluate the feasibility of transferring the responsibility of traffic signal coordination to agents. The results show different performances of the decentralized coordination process in different scenarios (e.g. the flow of vehicles is nearly equal in both opposing directions, one direction has a clearly higher flow, etc.). Therefore, the task of the manager is facilitate once s/he recognizes the scenario and acts accordingly.This revised version was published online in August 2005 with a corrected cover date.     

A Distributed Approach for Coordination of Traffic Signal Agents

Innovative control strategies are needed to cope with the increasing urban traffic chaos. In most cases, the currently used strategies are based on a central traffic-responsive control system which can be demanding to implement and maintain. Therefore, a functional and spatial decentralization is desired. For this purpose, distributed artificial intelligence and multi-agent systems have come out with a series of techniques which allow coordination and cooperation. However, in many cases these are reached by means of communication and centrally controlled coordination processes, giving little room for decentralized management. Consequently, there is a lack of decision-support tools at managerial level (traffic control centers) capable of dealing with decentralized policies of control and actually profiting from them. In the present work a coordination concept is used, which overcomes some disadvantages of the existing methods. This concept makes use of techniques of evolutionary game theory: intersections in an arterial are modeled as individually-motivated agents or players taking part in a dynamic process in which not only their own local goals but also a global one has to be taken into account. The role of the traffic manager is facilitated since s/he has to deal only with tactical ones, leaving the operational issues to the agents. Thus the system ultimately provides support for the traffic manager to decide on traffic control policies. Some application in traffic scenarios are discussed in order to evaluate the feasibility of transferring the responsibility of traffic signal coordination to agents. The results show different performances of the decentralized coordination process in different scenarios (e.g. the flow of vehicles is nearly equal in both opposing directions, one direction has a clearly higher flow, etc.). Therefore, the task of the manager is facilitate once s/he recognizes the scenario and acts accordingly.     

城市路网交通分散协调控制

针对城市路网交通系统规模大和非线性、不确定性强等特点，利用模糊神经网络设计了一种新的实时分散协调控制算法．把城市区域和市内快速公路作为一个路网大系统，子系统为路网中的各个交叉口；每个子系统都有一个模糊神经网络控制器，该控制器根据它自己和相邻子系统的交通流信息来动态管理相序及绿灯时间．控制器由3个模块组成：相序选择模块、绿灯判断模块和相位切换模块．控制器的控制目标是保持快速公路主线密度均衡和区域内各车辆平均延误时间最短．仿真研究表明，该算法能有效处理各种路网交通环境．     

能量高效的无线传感器网络分布式分簇一致性滤波算法

针对无线传感器网络中节点能量有限的特点,利用分簇模型提出了一种新的能量高效的分布式卡尔曼一致性滤波算法.并结合图论、矩阵论对该算法进行了收敛分析,得出了分簇处理能加快系统的收敛速度,且能有效地减少节点间信息的传输量、缩短节点间的通信距离的结论.为进一步降低能量消耗,引入Gossip 算法用于处理簇头级网络信息的一致性问题.仿真分析表明,所提出的算法不仅具有优越的估计性能,而且能有效地减少节点能量消耗,延长无线传感器网络的寿命.     

分布交互仿真中基于Server的层次过滤机制

大规模分布交互仿真对网络带宽、处理资源等提出了很高的要求,采用数据过滤机制可大大降低对网络带宽和计算资源的压力,有利于系统可扩性.然而对于高效过滤机制,仿真结点机要接收和处理足够的过滤信息.当系统规模增大时,大量过滤信息交换和处理占用了过多的计算资源,影响了正常的仿真任务.该文提出了一种基于Server的层次化过滤机制,解决了仿真节点计算能力受限的问题.同时,新的机制利用仿真的内在局部性,开发了两级过滤信息,从而减少了Server内部过滤信息计算量.     

基于粒子滤波的分布式目标跟踪算法

根据无线传感器网络分布式目标跟踪的特性,提出一种改进粒子滤波算法。将簇内各节点最新的观测数据用极大似然估计法得到目标的状态信息,该信息作为参考分布更换粒子滤波部分粒子,引入模糊推理的数据融合方法为各个节点滤波结果分配不同权值,通过加权平均法得到目标的状态信息。仿真实验表明该算法能有效提高目标跟踪的精度。