15897713
A Novel Multiple Time Scale Congestion Control Scheme for Multimedia Traffic
A novel two time scale congestion control scheme is proposed in this paper, which is called Adaptive Wavelet and Probability-based scheme (AWP). On normal time scales of 20-200 ms, it is a smooth and TCP-friendly congestion control; on large time scales of 1-5 second, it detects persistent shifts in overall network contention and modulates the rate control exhibited on normal scale. AWP exploits self-similar property of network traffic and predicts future network available bandwidth to enhance performance, and it is adaptive to network environment changes through an auto-correction algorithm. With comparable experiments among TFRC, TCP and AWP over NS platform, AWP has been proved to be able to greatly improve the quality of service(QoS) of multimedia data transmission while avoiding congestion collapse on the network.
37744315
Research on Network Architecture with Trustworthiness and Controllability
In this paper, the architecture of trustworthy and controllable networks is discussed to meet arising application requirements. After reviewing the lessons and experiences of success and failure in the Internet and summarizing related work, we analyze the basic targets of providing trustworthiness and controllability. Then, the anticipant architecture is introduced. Based on the resulting design, several trustworthy and controllable mechanisms are also discussed.
18052662
Probabilistic analysis on mesh network fault tolerance
Mesh networks are among the most important interconnection network topologies for large multicomputer systems. Mesh networks perform poorly in tolerating faults in the view of worst-case analysis. On the other hand, such worst cases occur very rarely in realistic situations. In this paper, we study the fault tolerance of 2-D and 3-D mesh networks under a more realistic model in which each network node has an independent failure probability. We first observe that if the node failure probability is fixed, then the connectivity probability of these mesh networks can be arbitrarily small when the network size is sufficiently large. Thus, it is practically important for multicomputer system manufacture to determine the upper bound for node failure probability when the probability of network connectivity and the network size are given. We develop a novel technique to formally derive lower bounds on the connectivity probability for 2-D and 3-D mesh networks. Our study shows that these mesh networks of practical size can tolerate a large number of faulty nodes thus are reliable enough for multicomputer systems. For example, it is formally proved that as long as the node failure probability is bounded by 0.5%, a 3-D mesh network of up to a million nodes remains connected with a probability larger than 99%.
15949067
Security authentication of 3G-WLAN interworking
With the development of wireless Internet, the interworking of the third generation (3G) wide area wireless networks and wireless local area networks (WLAN) has become a focus of research. 3G systems have their advantages in charging management, roaming, and security facilities, and WLAN systems have the advantages of high bandwidth and low investment cost. Interworking of 3G-WLAN offers characteristics that complement each other perfectly. Several authentication protocols have been proposed to improve security. In this paper, we firstly introduce the architectures of 3G-WLAN interworking and discuss existing problems related to authentication and key agreement procedures. Then, we present two schemes by applying authentication based on EAP-TLS (extensible authentication protocol-transport layer security) and EAP-TTLS (tunneled TLS) into integrated 3G and WLAN to achieve high end-to-end security and authentication for the users. Finally, we conduct a series of performance evaluation on the energy consumption and latency of standard authentication protocols.
12254119
Wireless network security
28815316
Stability analysis of buffer priority scheduling policies using Petri nets
A Petri net approach to determining the conditions for stability of a re-entrant system with buffer priority scheduling policy is described in this paper. The concept of buffer boundedness based on the dynamic behavior of the marking in the system model is emphasized. The method is used to demonstrate the stability of the first buffer first served (FBFS) and the last buffer first served (LBFS) scheduling policies. finally a sufficient condition for instability of systems with a positive feedback loop (PFL) is established, and an example is given.
38422944
Neural-Network-Based Fuzzy Logic Control and Decision System
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >
5592923
Temperature control with a neural fuzzy inference network
Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance.
15658650
A reinforcement neuro-fuzzy combiner for multiobjective control
This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch; it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as "success (good)" or "failure (bad)" for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme.
9707968
Abstractions for Portable, Scalable Parallel Programming
In parallel programming, the need to manage communication, load imbalance, and irregularities in the computation puts substantial demands on the programmer. Key properties of the architecture, such as the number of processors and the cost of communication, must be exploited to achieve good performance, but coding these properties directly into a program compromises the portability and flexibility of the code because significant changes are then needed to port or enhance the program. We describe a parallel programming model that supports the concise, independent description of key aspects of a parallel program-including data distribution, communication, and boundary conditions-without reference to machine idiosyncrasies. The independence of such components improves portability by allowing the components of a program to be tuned independently, and encourages reuse by supporting the composition of existing components. The isolation of architecture-sensitive aspects of a computation simplifies the task of porting programs to new platforms. Moreover, the model is effective in exploiting both data parallelism and functional parallelism. This paper provides programming examples, compares this work to related languages, and presents performance results.
14630306
Fuzzy neural network classification design using support vector machine
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network classification (SVFNNC) is proposed. The SVFNNC combines the superior classification power of support vector machine (SVM) in high reasoning of FNN in handling uncertainty information. The learning algorithm consists of two learning phases. In the phase 1, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the phase 2, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. To investigate the effectiveness of the proposed SVFNNC, it is applied to the iris, vehicle and dna datasets. Experimental results show that the proposed SVFNNC can achieve good classification performance with drastically reduced number of fuzzy kernel functions.