Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

Scheduling video streams in a large-scale video-on-demand server

This paper addresses the design problems concerning a large-scale, parallel video-on-demand server that consists of multiple clusters of nodes connected by a high performance interconnection network. In order to efficiently control the flow of video streams, we propose two scheduling algorithms for data retrieval and communication. First, we present a disk scheduling algorithm called round scheduling which fully utilizes disk bandwidth, minimizing the disk idle time while the server retrieves data blocks. Second, a communication scheduling algorithm is developed to guarantee conflict-free communication over the multistage interconnection network that is topologically equivalent to the Omega network. We also show some simulation results on the server configuration. Analysis of tradeoffs between the server utilization and the start-up latency helps to determine the proper number and size of server clusters for a set of given nodes.     

基于Gossip协议的拜占庭共识算法

区块链是一种对等网络的分布式账本系统,具备去中心化、不可篡改、安全可信等特点,因此受到了广泛关注。在区块链系统中,典型的拜占庭错误包括操作错误、网络延迟、系统崩溃、恶意攻击等。现有共识算法不仅对区块链中拜占庭节点的容错能力低,而且对区块链系统的可扩展性差。针对这一问题,文中提出了基于Gossip协议的拜占庭共识算法,使系统可以容忍小于一半的节点为拜占庭节点,能够达到XFT共识算法的容错能力。同时,因为采用了统一的数据结构,所以系统具有更好的可扩展性,并且有利于正确节点识别区块链系统中的恶意节点。在该算法中,提案节点随着区块链长度的变化而转移,系统中所有节点都处于对等的地位,从而避免了单点故障问题,进而使得系统具有更好的动态负载均衡的性能。     

一种有效的时延约束组播路由算法

在多媒体通信网络中，组播问题提出了新的要求，除了最小化组播通信的代价，同时要求保证每一个目的的节点在固定的延时之内接收信息，在这篇论文中，我们提出了一个边路选择函数用于解决时延约束组播问题，我们的实验结果揭示了该函数能提供满足时约束且代价较小的组播路由问题近似解。     

多智体系统中约简状态空间的限界模型检测算法

为了形式化描述多智体系统中与概率、实时、知识相关的性质,提出了一种概率实时认知逻辑PTCTLK.模型检测是验证多智体系统是否满足PTCTLK公式的主要技术,状态空间爆炸是该技术实用化的主要瓶颈,为此提出一种PTCTLK的限界模型检测算法.其基本思想是,在有限的局部可达空间中逐步搜索属性成立的证据,从而达到约简状态空间的目的.首先,将PTCTLK的模型检测问题转换为无实时算子的PBTLK的模型检测问题;其次,定义PBTLK的限界语义,并证明其正确性;然后,设计基于线性方程组求解的限界模型检测算法;最后,依据概率度量的演化规律,探索检测过程终止的判别准则.实例研究结果表明,与无界模型检测相比,在属性为真的证据较短的情况下,限界模型检测完成验证所需空间更小.     

Partial syndrome-based system-level fault diagnosis using game theory

This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identification problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identifies the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identification algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we first show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoffs of all players (nodes). We then demonstrate, using results from a thorough simulation, the effectiveness of this approach in solving the fault identification problem using partial syndromes from randomly generated diagnosable systems of different sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.     

Control and optimization in a collision avoidance problem in oscillating systems

The development of control algorithms, including optimal control ones, in the collision avoidance problem for a system of two pendulums with a controllable common base is considered. Two problems are solved. The first one searches for the law of variation of the bounded control force that makes the system move from its initial state of rest to the given final state of rest during a finite time and ensures the pendulums do not collide in the process of oscillatory motions. The second problem searches for the performance-optimal law of variation of acceleration of the base and the bounded force that generates the acceleration. The algorithms for constructing the sought controls that use Kalman controllability conditions and Pontryagin’s maximum principle method are presented. The dynamics of the system involved is simulated for the constructed control laws. The numerical results of both problems are compared to find that implementation of the developed performance-optimal control algorithm can help significantly decrease the releasing time of the pendulums while preventing a possible collision.     

LABS: Latency aware broadcast scheduling in uncoordinated Duty-Cycled Wireless Sensor Networks

Broadcast is a fundamental operation in Wireless Sensor Networks (WSNs) and plays an important role in a communication protocol design. In duty-cycled scenarios, a sensor node can receive a message only in its active time slot, which makes it more difficult to design collision-free scheduling for broadcast operations. Recent studies in this area have focused on minimizing broadcast latency and guaranteeing that all nodes receive a broadcast message. This paper investigates the problem of Minimum Latency Broadcast Scheduling in Duty-Cycled (MLBSDC) WSNs. By using special geometric properties of independent sets of a broadcast tree, we reduce the number of transmissions, consequently reducing the possibility of collision. Allowing multiple transmissions in one working period, our proposed Latency Aware Broadcast Scheduling (LABS) scheme provides a latency-efficient broadcast schedule. Theoretical analysis proves that the scheme has the same approximation ratio and complexity as the previous best algorithm for the MLBSDC problem. Moreover, simulation shows that the new scheme achieves up to 34%, 37%, and 21% performance improvement over previous schemes, in terms of latency, number of transmissions, and energy consumption, respectively.     

Robust estimation for discrete time‐varying systems with limited communication capacity

In this paper, we consider the state estimation problem for linear discrete time‐varying systems subject to limited communication capacity which includes measurement quantization, random transmission delay and data‐packet dropouts. Based on transforming the three communication limitations into the system with norm‐bounded uncertainties and stochastic matrices, we design a robust filter such that, for all the communication limitations, the error state of the filtering process is mean square bounded. An upper bound on the variance of the state estimation error is first found, and then, a robust filter is derived by minimizing the prescribed upper bound in the sense of the matrix norm. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati‐like difference equations which also provide a recursive algorithm suitable for online computation. A simulation example is presented to demonstrate the effectiveness and applicability of the proposed algorithm. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

无线传感器网络延迟约束的MDC分布式轨道规划算法

针对引入移动元素后无线传感器网络数据面临的收集延时问题,提出了一种分布式的移动数据收集器(MDC)轨道规划算法.首先给出基于k跳支配集的MDC最小时延规划问题定义,并证明它是NP-hard.在基于集结的数据收集模式汇总,k跳支配节点作为集结点缓存传感节点收集的数据并在MDC到达时上传.然后,提出了一种高效的基于k跳支配集的MDC轨迹构建算法.算法通过分布式的k-跳支配集算法找出网络中的支配节点,进而通过Prim算法和Christofides近似算法对MDC的移动轨道进行规划.算法的正确性、k-跳支配集的界、时间复杂性和消息交换复杂性通过理论进行分析.最后,通过仿真实验验证了算法的有效性.仿真结论表明,与同类算法相比,所提出算法能够显著缩短MDC的移动轨迹,因而可以降低网络延迟.     

Robust model predictive control with time-varying tubes

This paper focuses on the problem of robustly stabilizing uncertain discrete-time systems subject to bounded disturbances. The proposed tube-based model predictive controller ensures that all possible realizations of the state trajectory lie in the time-varying tubes so robust stability and satisfaction of the state and input constraints are guaranteed. The time-varying tubes are computed off-line so the on-line computational time is tractable. At each sampling time, the precomputed time-varying tubes are included in the optimal control problem as the constraints in the prediction horizon and only a quadratic programming problem is solved. In comparison to the algorithm that calculates the time-varying tubes on-line, the proposed algorithm can achieve the same level of control performance while the on-line computational time is greatly reduced.     

An efficient TDMA start-up and restart synchronization approach for distributed embedded systems

A desired attribute in safety-critical embedded real-time systems is a system time and event synchronization capability on which predictable communication can be established. Focusing on bus-based communication protocols, we present a novel, efficient, and low-cost start-up and restart synchronization approach for TDMA environments. This approach utilizes information about a node's message length that forms a unique sequence to achieve synchronization such that communication overhead can be avoided. We present a fault-tolerant initial synchronization protocol with a bounded start-up time. The protocol avoids start-up collisions by deterministically postponing retries after a collision. We also present a resynchronization strategy that incorporates recovering nodes into synchronization.     

轮询通信协议下信息物理系统分布式状态估计

本文关注的是一类信息物理系统的分布式状态估计问题. 由于传感器网络通信带宽有限, 当大量节点同时发送数据时, 可能造成数据冲突. 因此, 通过引入轮询协议减轻传感器网络通信负担, 在该协议下每个节点的测量分量将依次且周期性访问网络. 考虑滤波网络拓扑切换概率矩阵是时变的, 因此采用非齐次Markov链描述随机拓扑切换行为. 证明了估计误差以指数衰减的形式收敛,确保了滤波误差系统在均方意义下最终有界. 进一步地, 通过解决特定拓扑依赖的凸优化问题, 获得期望的分布式滤波器参数. 最后, 通过两个例子证明了所设计的分布式状态估计方法的可行性.     

具有可控冗余度的虚拟机容错分配算法

在现代基于虚拟化的数据中心中,虚拟机分配是实现云中资源有效调度的首要考虑。已经证明对数据结点分配虚拟机并考虑虚拟机之间的通信延迟,使得最大通信延迟最小的问题是NP-hard问题。目前鲜有在数据中心网络虚拟机分配问题上考虑其安全性和可靠性的研究。针对虚拟机分配中的容错技术,提出了一种具有可控虚拟机冗余度的启发式分配算法。算法以最大通信延迟最小化为优化目标,在可利用的虚拟机集合中通过构造可控冗余度的团来分配处理数据结点。实验结果表明,在Tree、VL2、Fat-tree和BCube四种常用的网络结构中,提出的启发式算法能提供0-200%之间任意冗余度。同时,在冗余度介于0~40%时,虚拟机与数据结点的匹配时间平均降低了67.1%,并且算法运行时间平均降低了12.8%。     

Assignment and scheduling communicating periodic tasks indistributed real-time systems

Presents an optimal solution to the problem of allocating communicating periodic tasks to heterogeneous processing nodes (PNs) in a distributed real-time system. The solution is optimal in the sense of minimizing the maximum normalized task response time, called the system hazard, subject to the precedence constraints resulting from intercommunication among the tasks to be allocated. Minimization of the system hazard ensures that the solution algorithm allocates tasks so as to meet all task deadlines under an optimal schedule, whenever such an allocation exists. The task system is modeled with a task graph (TG), in which computation and communication modules, communication delays and intertask precedence constraints are clearly described. Tasks described by this TG are assigned to PNs by using a branch-and-bound (B&B) search algorithm. The algorithm traverses a search tree whose leaves correspond to potential solutions to the task allocation problem. We use a bounding method that prunes, in polynomial time, nonleaf vertices that cannot lead to an optimal solution, while ensuring that the search path leading to an optimal solution will never be pruned. For each generated leaf vertex, we compute the exact cost using the algorithm developed by Peng and Shin (1993). The lowest-cost leaf vertex (one with the least system hazard) represents an optimal task allocation. Computational experiences and examples are provided to demonstrate the concept, utility and power of the proposed approach     

基于离线利用率分析的混合关键级任务调度优化研究

现主流的混合关键级调度算法在系统高关键级状态下时主要通过抛弃低关键级任务来保证高关键级任务的执行,进而保证系统的正确性。此方法常常导致低关键级任务无法执行但系统资源却过剩的问题发生,故基于该问题提出复合型SDU（schedule depend on utilization）调度算法。该方法根据任务集对系统资源需求情况的不同进行利用率区间的划分,通过对各个区间实际使用情况的分析,设计相应的子算法进行调度,并提出了SDU算法对应的可调度性判据。仿真实验结果表明,相较于混合关键级任务调度领域主流的EDF-VD（earliest deadline first-virtual deadline）算法,所提SDU算法可将系统对任务集的调度率提升30%,并在相同情况下将系统对低关键级任务的执行率提升165%,证明了该算法可以极大地提高系统资源使用率,并保证系统服务完整性。     

Replica Consistency in Lazy Master Replicated Databases

In a lazy master replicated database, a transaction can commit after updating one replica copy (primary copy) at some master node. After the transaction commits, the updates are propagated towards the other replicas (secondary copies), which are updated in separate refresh transactions. A central problem is the design of algorithms that maintain replica's consistency while at the same time minimizing the performance degradation due to the synchronization of refresh transactions. In this paper, we propose a simple and general refreshment algorithm that solves this problem and we prove its correctness. The principle of the algorithm is to let refresh transactions wait for a certain deliver time before being executed at a node having secondary copies. We then present two main optimizations to this algorithm. One is based on specific properties of the topology of replica distribution across nodes. In particular, we characterize the nodes for which the deliver time can be null. The other improves the refreshment algorithm by using an immediate update propagation strategy.     

基于遗传算法的可扩展应用层组播树构建

在应用层组播中,为降低节点的路径延时,通常采用遗传算法和启发式算法来减小组播树直径的方法,但在组播树具有大规模节点数时,遗传算法收敛时间长,而采用启发式算法难以在有约束条件下达到全局最优.本文在具有超节点的双层应用层组播模型基础上,提出了利用遗传算法构建出度受限最小带权路径延时生成树（MWPL-DC-ST）的生成算法GA-MWPL-DC-ST,利用该算法可在超节点上对双层组播树进行分布式构建,从而将求最优解问题的巨大计算量分担到多个超节点上.算法中的初始化、杂交和变异阶段采用启发式算法,对变异参数进行适应性调整,加快了算法的收敛速度.仿真试验表明,本文提出的双层应用层组播模型和GA-MWPL-DC-ST算法能得到比启发式算法更优的解,与采用单层模型的遗传算法相比较,显著降低了算法收敛时间,解决了遗传算法构建有大规模节点数的应用层组播树的可扩展性问题.     

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities. To eliminate nonlinearities, neural networks are applied to approximate the inherent dynamics of the system. In addition, due to the limitations of the actual working conditions, each follower agent can only obtain the locally measurable partial state information of the leader agent.To a...     

一种处于任意时延非线性动态网络中的时钟振荡器快速平均同步算法

研究了处于任意时延非线性动态网络中的时钟振荡器的同步问题. 首先, 提出了一种基于一致性控制策略的动态同步算法, 即快速平均同步算法(FASA)来找到同步解. 该算法能够补偿时钟节点间的时钟偏移和时钟偏差, 使得和以前的同步方法比较后在较短的时间内实现时钟节点的同步. 其次, 由于FASA的动态特性, 我们从具有任意时延的分割动态系统角度来刻画这个算法. 该算法保证在动态网络中的所有时钟节点收敛到李亚普诺夫稳定平衡点. 最后, 数值仿真和实验结果证明了FASA的正确性和有效性, 这意味着时钟节点能达到全局一致, 并且使同步误差精度达到纳秒级别.