In this letter, we reveal redundant control traffic in the optimized link state routing protocol (OLSR) for MANET. Topology control (TC) messages, which occupy a part of control traffic in OLSR, are used to exchange topology information with other nodes. TC messages are generated and forwarded by only nodes that have been selected as multipoint relays (MPRs) by at least one neighbor node. These nodes selected as MPRs are called TC message senders in this letter. One of solutions to reduce the number of TC messages is to reduce the number of TC message senders. We describe a non-distributed algorithm to minimize the number of TC message senders. Through simulation of static-node scenarios, we show 18% to 37% of TC message senders in RFC-based OLSR are redundant. By eliminating redundant TC message senders, the number of TC packets, each of which contains one or more TC messages, is also reduced from 19% to 46%. We also show that high density scenarios have more redundancy than low density scenarios. This observation can help to consider a cooperative MPR selection in OLSR.

It is known that wireless ad hoc networks employing omnidirectional communications suffer from poor network throughput due to inefficient spatial reuse. Although the use of directional communications is expected to provide significant improvements in this regard, the lack of efficient mechanisms to deal with deafness and hidden terminal problems makes it difficult to fully explore its benefits. The main contribution of this work is to propose a Medium Access Control (MAC) scheme which aims to lessen the effects of deafness and hidden terminal problems in directional communications without precluding spatial reuse. The simulation results have shown that the proposed directional MAC provides significant throughput improvement over both the IEEE802.11DCF MAC protocol and other prominent directional MAC protocols in both linear and grid topologies.

This paper proposes a medium access control (MAC) mechanism for the recently developed IEEE 802.15.4 standard, a promising candidate to become the physical (PHY) and MAC layer standard for Wireless Sensor Networks (WSNs). The main concern in WSNs is the energy consumption, and this paper presents a mechanism that adapts properly the duty cycle operation according to the traffic conditions. Various traffic adaption mechanisms have been presented for the MAC layer of the IEEE 802.15.4. However these conventional mechanisms only consider the temporal traffic fluctuations. The proposed mechanism outperforms the conventional mechanism when applied to cluster-tree based WSNs, because it considers not only the temporal fluctuations but also the spatial (geographical) fluctuations, which are intrinsic characteristics of traffic in WSNs with the cluster tree topology. Evaluations showed that the proposed mechanism achieves less energy consumption than the conventional traffic adaptation mechanism, with maintaining almost the same transmission performance.

This paper presents an autonomous traffic engineering framework, named ATE, a highly efficient data dissemination mechanism for multipath data forwarding in Wireless Sensor Networks (WSNs). The proposed ATE has several salient features. First, ATE utilizes three coordinating schemes: an incipient congestion inference scheme, an accurate link quality estimation scheme and a dynamic traffic diversion scheme. It significantly minimizes packet drops due to congestion by dynamically and adaptively controlling the data traffic over congested nodes and/or poorer quality links, and by opportunistically exploiting under-utilized nodes for traffic diversion, while minimizing the estimation and measurement overhead. Second, ATE can provide with high application fidelity of the network even for increasing values of bit error rates and node failures. The proposed link quality estimation and congestion inference schemes are light weight and distributed, improving the energy efficiency of the network. Autonomous Traffic Engineering has been evaluated extensively via NS-2 simulations, and the results have shown that ATE provides a better performance with minimum overhead than those of existing approaches.

In this paper, we reveal inherent robustness issues of XCP (eXplicit Control Protocol), and propose extensions to XCP for increasing its robustness. XCP has been proposed as an efficient transport-layer protocol for wide-area and high-speed network. XCP is a transport-layer protocol that performs congestion control based on explicit feedback from routers. In the literature, many performance studies of XCP have been performed. However, the effect of traffic dynamics on the XCP performance has not been fully investigated. In this paper, through simulation experiments, we first show that XCP has the following problems: (1) the bottleneck link utilization is lowered against XCP traffic dynamics, and (2) operation of XCP becomes unstable in a network with both XCP and non-XCP traffic. We then propose XCP-IR (XCP with Increased Robustness) that operates efficiently even for dynamic XCP and non-XCP traffic.

In this paper, we discuss the validity of the multi-scale gamma model and characterize the differences in host-level application traffic with this model by using a real traffic trace collected on a 150-Mbps transpacific link. First, we investigate the dependency of the model (parameters α and β, and fitting accuracy ε) on time scale Δ, then find suitable time scales for the model. Second, we inspect the relations among α, β, and ε, in order to characterize the differences in the types of applications. The main findings of the paper are as follows. (1) Different types of applications show different dependencies of α, β, and ε on Δ, and display different suitable Δs for the model. The model is more accurate if the traffic consists of intermittently-sent packets than other. (2) More appropriate models are obtained with specific α and β values (e.g., 0.1 < α < 1, and β < 2 for Δ = 500 ms). Also, application-specific traffic presents specific ranges of α, β, and ε for each Δ, so that these characteristics can be used in application identification methods such as anomaly detection and other machine learning methods.

A novel approach for fast traffic classification for the high speed networks is proposed, which bases on the protocol behavior statistical features. The packet size and a new parameter named "Estimated Protocol Processing Time" are collected from the real data flows. Then a set of joint probability distributions is obtained to describe the protocol behaviors and classify the traffic. Comparing the parameters of an unknown flow with the pre-obtained joint distributions, we can judge which application protocol the unknown flow belongs to. Distinct from other methods based on traditional inter-arrival time, we use the "Estimated Protocol Processing Time" to reduce the location dependence and time dependence and obtain better results than traditional traffic classification method. Since there is no need for character string searching and parallel feature for hardware implementation with pipeline-mode data processing, the proposed approach can be easily deployed in the hardware for real-time classification in the high speed networks.

For optimization problems with irregular and complex multimodal landscapes, Estimation of Distribution Algorithms (EDAs) suffer from the drawback of premature convergence similar to other evolutionary algorithms. In this paper, we propose an adaptive niching EDA based on Affinity Propagation (AP) clustering analysis. The AP clustering is used to adaptively partition the niches and mine the searching information from the evolution process. The obtained information is successfully utilized to improve the EDA performance by using a balance niching searching strategy. Two different categories of optimization problems are used to evaluate the proposed adaptive niching EDA. The first one is solving three benchmark functional multimodal optimization problems by a continuous EDA based on single Gaussian probabilistic model; the other one is solving a real complicated discrete EDA optimization problem, the HP model protein folding based on k-order Markov probabilistic model. Simulation results show that the proposed adaptive niching EDA is an efficient method.

Intrusion Detection Systems (IDS) have been received considerable attention among the network security researchers as one of the most promising countermeasures to defend our crucial computer systems or networks against attackers on the Internet. Over the past few years, many machine learning techniques have been applied to IDSs so as to improve their performance and to construct them with low cost and effort. Especially, unsupervised anomaly detection techniques have a significant advantage in their capability to identify unforeseen attacks, i.e., 0-day attacks, and to build intrusion detection models without any labeled (i.e., pre-classified) training data in an automated manner. In this paper, we conduct a set of experiments to evaluate and analyze performance of the major unsupervised anomaly detection techniques using real traffic data which are obtained at our honeypots deployed inside and outside of the campus network of Kyoto University, and using various evaluation criteria, i.e., performance evaluation by similarity measurements and the size of training data, overall performance, detection ability for unknown attacks, and time complexity. Our experimental results give some practical and useful guidelines to IDS researchers and operators, so that they can acquire insight to apply these techniques to the area of intrusion detection, and devise more effective intrusion detection models.

Cooperative relaying (CR) is a promising technique to provide spatial diversity by combining multiple signals from source and relay stations. In the present paper, the impact and use of the asymmetric property in bi-directional CR under asymmetric traffic conditions are discussed assuming that CR involves one communication pair and one relay station in a time division duplex (TDD) system. The asymmetric property means that the average communication quality differs for each transmission direction because of the difference in signal power between the combined signals for each direction. First, numerical results show the asymmetric property of bi-directional CR. Next, in order to evaluate the impact of the asymmetric property, the optimal relay position and resource allocation are compared to those in simple multi-hop relaying, which does not have the asymmetric property. Numerical results show that, in order to maximize the overall quality of bi-directional communication, the optimal relay position in CR depends on the offered traffic ratio, which is defined as the traffic ratio of each transmission direction, while the offered traffic ratio does not affect the optimal relay position in multi-hop relaying. Finally, the asymmetric property is used to enhance the overall quality. Specifically, a high overall quality can be achieved by, for example, opportunistically switching to the transmission direction with higher quality. Under asymmetric traffic conditions, weighted proportionally fair scheduling (WPFS), which is proposed in the context of downlink scheduling in a cellular network, is applied to transmission direction switching. Numerical results reveal that WPFS provides a high overall quality and that the quality ratio is similar to the offered traffic ratio.

This paper presents a novel algorithm for Nearest Neighbor (NN) classifier. NN classification is a well-known method of pattern classification having the following properties: * it performs maximum-margin classification and achieves less than twice the ideal Bayesian error, * it does not require knowledge of pattern distributions, kernel functions or base classifiers, and * it can naturally be applied to multiclass classification problems. Among the drawbacks are A) inefficient memory use and B) ineffective pattern classification speed. This paper deals with the problems A and B. In most cases, NN search algorithms, such as k-d tree, are employed as a pattern search engine of the NN classifier. However, NN classification does not always require the NN search. Based on this idea, we propose a novel algorithm named k-d decision tree (KDDT). Since KDDT uses Voronoi-condensed prototypes, it consumes less memory than naive NN classifiers. We have confirmed that KDDT is much faster than NN search-based classifier through a comparative experiment (from 9 to 369 times faster than NN search based classifier). Furthermore, in order to extend applicability of the KDDT algorithm to high-dimensional NN classification, we modified it by incorporating Gabriel editing or RNG editing instead of Voronoi condensing. Through experiments using simulated and real data, we have confirmed the modified KDDT algorithms are superior to the original one.

Capture-safety, (defined as the avoidance of timing error due to unduly high launch switching activity in capture mode during at-speed scan testing), is critical in avoiding test induced yield loss. Although several sophisticated techniques are available for reducing capture IR-drop, there are few complete capture-safe test generation flows. This paper addresses the problem by proposing a novel and practical capture-safe test generation flow, featuring (1) a complete capture-safe test generation flow; (2) reliable capture-safety checking; and (3) effective capture-safety improvement by combining X-bit identification & X-filling with low launch-switching-activity test generation. The proposed flow minimizes test data inflation and is compatible with existing automatic test pattern generation (ATPG) flow. The techniques proposed in the flow achieve capture-safety without changing the circuit-under-test or the clocking scheme.

This paper presents a new approach to precompute points [3]P, [5]P,..., [2k-1]P, for some k ≥ 2 on an elliptic curve over Fp. Those points are required for the efficient evaluation of a scalar multiplication, the most important operation in elliptic curve cryptography. The proposed method precomputes the points in affine coordinates and needs only one single field inversion for the computation. The new method is superior to all known methods that also use one field inversion, if the required memory is taken into consideration. Compared to methods that require several field inversions for the precomputation, the proposed method is faster for a broad range of ratios of field inversions and field multiplications. The proposed method benefits especially from ratios as they occur on smart cards.

With the scaling down of CMOS devices, process variation is becoming the leading cause of CMOS based analog circuit failures. For example, a mere 5% variation in feature size can trigger circuit failure. Various methods such as Monte-Carlo and corner-based verification help predict variation caused problems at the expense of thousands of simulations before capturing the problem. This paper presents a new methodology for analog circuit performance prediction. The new method first applies statistical uncertainty analysis on all associated devices in the circuit. By evaluating the uncertainty importance of parameter variability, it approximates the circuit with only components that are most critical to output results. Applying Chebyshev Affine Arithmetic (CAA) on the resulting system provides both performance bounds and probability information in time domain and frequency domain.

We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.

The manner of a person's eye movement conveys much about nonverbal information and emotional intent beyond speech. This paper describes work on expressing emotion through eye behaviors in virtual agents based on the parameters selected from the AU-Coded facial expression database and real-time eye movement data (pupil size, blink rate and saccade). A rule-based approach to generate primary (joyful, sad, angry, afraid, disgusted and surprise) and intermediate emotions (emotions that can be represented as the mixture of two primary emotions) utilized the MPEG4 FAPs (facial animation parameters) is introduced. Meanwhile, based on our research, a scripting tool, named EEMML (Emotional Eye Movement Markup Language) that enables authors to describe and generate emotional eye movement of virtual agents, is proposed.

In this paper, an efficient radio resource allocation scheme for OFDMA systems is proposed, which follows two steps to take care of real-time traffic characterized with multi-level delay constraints. Urgent packets, those with imminent deadlines, are released first in step 1. After that the remaining channel resources are managed in such a way that overall throughput is maximized at Step 2. In this work, 2-dimensional diversity over multiple sub-bands and multiple users are jointly considered. The proposed scheme is compared with existing schemes designed for real-time traffic such as Exponential Scheduling (EXP) scheme, Modified Largest Weighted Delay First (M-LWDF) scheme, and Round robin scheme in terms of the packet discard probability and throughput. Numerical results show that the proposed scheme performs much better than the aforementioned ones in terms of the packet discard probability, while slightly better in terms of throughput.

This paper compares the performances of optimal routing as yielded by the pipe, hose, and intermediate models. The pipe model, which is specified by the exact traffic matrix, provides the best routing performance, but the traffic matrix is difficult to measure and predict accurately. On the other hand, the hose model is specified by just the total outgoing/incoming traffic from/to each node, but it has a problem in that its routing performance is degraded compared to the pipe model, due to insufficient traffic information. The intermediate model, where the upper and lower bounds of traffic demands for source-destination pairs are added as constraints, is a construction that lies between the pipe and hose models. The intermediate model, which lightens the difficulty of the pipe model, but narrows the range of traffic conditions specified by the hose model, offers better routing performance than the hose model. An optimal-routing formulation extended from the pipe model to the intermediate model can not be solved as a regular linear programming (LP) problem. Our solution, the introduction of a duality theorem, turns our problem into an LP formulation that can be easily solved. Numerical results show that the network congestion ratio for the pipe model is much lower than that of hose model. The differences in network congestion ratios between the pipe and hose models lie in the range from 27% to 45% for the various network topologies examined. The intermediate model offers better routing performance than the hose model. The intermediate model reduces the network congestion ratio by 34% compared to the hose model in an experimental network, when the upper-bound and lower-bound margins are set to 25% and 20%, respectively.

Silicon-quantum-dots (Si-QDs) with an areal density as high as 1012 cm - 2 were self-assembled on thermally-grown SiO2 by low pressure CVD using Si2H6, in which OH-terminated SiO2 surface prior to the Si CVD was exposed to GeH4 to create nucleation sites uniformly. After thermal oxidation of Si-QDs surface, two-dimensional electronic transport through the Si-QDs array was measured with co-planar Al electrodes evaporated on the array surface. Random telegraph signals were clearly observed at constant applied bias conditions in dark condition and under light irradiation at room temperature. The result indicates the charging and discharging of a dot adjacent to the percolation current path in the dots array.

In this paper, we propose four new general constructions of LCZ/ZCZ sequence sets based on interleaving technique and affine transformations. A larger family of LCZ/ZCZ sequence sets with longer period are generated by these constructions, which are more flexible among the selection of the alphabet size, the period of the sequences and the length of LCZ/ZCZ, compared with those generated by the known constructions. Especially, two families of the newly constructed sequences can achieve or almost achieve the theoretic bound.