338397
Design a congestion controller based on sliding mode variable structure control
As an effective mechanism acting on the intermediate nodes to support end-to-end congestion control, Active Queue Management (AQM) takes a trade-off between link utilization and delay experienced by data packets. Most existing AQM algorithms are heuristic, and lack a systematic and theoretical design and analysis approach. From the viewpoint of control theory, the AQM system can be regarded as a typical regulating system. Although the PI controller for AQM outperforms the RED algorithm, the mismatches in a simplified TCP flow model inevitably degrade the performance of controller designed using classical control theory. In this paper, a robust controller for AQM is developed based on Sliding Mode Variable Structure Control (SMVS). Because it is insensitive to noise and variance of parameters, it is very suitable for time-varying network systems. The principles and design of the SMVS controller are presented in detail. The integrated performance is evaluated using ns simulations. The results show that the SMVS is responsive and robust against disturbance. At the same time, a complete comparison between SMVS controller and PI controller is made. The conclusion is that both transient and steady performance of the SMVS controller is superior to those of PI controller, thus the SMVS controller can readily achieve to the AQM objectives.
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%.
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.
12254119
Wireless network security
16150333
Self-healing group key distribution with time-limited node revocation for wireless sensor networks
Abstract A novel key distribution scheme with time-limited node revocation is proposed for secure group communications in wireless sensor networks. The proposed scheme offers two important security properties: the seal-healing re-keying message distribution which features periodic one-way re-keying with implicitly authentication, efficient tolerance for the lost re-keying messages, and seamless Traffic Encryption Key (TEK) switch without disrupting ongoing data transmissions; and the time-limited dynamic node attachment and detachment, so that both forward and backward secrecy is assured by dual directional hash chains. It is shown that the communication and computation overhead of the proposed protocol is light, and the protocol is robust under poor communication channel quality and frequent group node topology change.
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.
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.
2262504
Video personalization and summarization system
A video personalization and summarization system is designed and implemented to dynamically generate a personalized video summary. The personalization system adopts the three-tier server-middleware-client architecture in order to select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. These semantic metadata are provided through the use of our VideoAnnEx MPEG-7 video annotation tool. When the user initiates a request for content, the client communicates the user request and usage environment descriptions to the middleware. The middleware is powered by the personalization engine and the content adaptation engine. Our personalization engine includes the VideoSue summarization on usage environment engine that selects the optimal set of desired contents according to user preferences. Afterwards, the adaptation engine performs the required transformations and compositions of the selected contents for the specific usage environment using our VideoEd editing and composition tool. Finally, a personalization and summarization system is demonstrated on the IBM Websphere Portal Server for PCs and pervasive devices.
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. >
9253913
BEARR: Batch Extraction and Analysis of cis-Regulatory Regions
Transcription factors play important roles in regulating biological and disease processes. Microarray technology has enabled researchers to simultaneously monitor changes in the expression of thousands of transcripts. By identifying specific transcription factor binding sites in the cis-regulatory regions of differentially expressed genes, it is then possible to identify direct targets of transcription factors, model transcriptional regulatory networks and mine the dataset for relevant targets for experimental and clinical manipulation. We have developed web-based software to assist biologists in efficiently carrying out the analysis of microarray data from studies of specific transcription factors. Batch Extraction and Analysis of cis-Regulatory Regions, or BEARR, accepts gene identifier lists from microarray data analysis tools and facilitates identification, extraction and analysis of regulatory regions from the large amount of data that is typically generated in these types of studies. The software is publicly available at http://giscompute.gis.a-star.edu.sg/~vega/BEARR1.0/.
11669518
Error checking with client-driven pointer analysis
This paper presents a new client-driven pointer analysis algorithm that automatically adjusts its precision in response to the needs of client analyses. Using five significant error detection problems as clients, we evaluate our algorithm on 18 real C programs. We compare the accuracy and performance of our algorithm against several commonly used fixed-precision algorithms. We find that the client-driven approach effectively balances cost and precision, often producing results as accurate as fixed-precision algorithms that are many times more costly. Our algorithm works because many client problems only need a small amount of extra precision applied to selected portions of each input program.