CHECK END OF SENTENCE Research Articles

Randomized Throughput-Optimal Oblivious Routing for Torus Networks

In this paper, we study the problem of optimal oblivious routing for 1D and 2D torus networks. We introduce a new closed-form oblivious routing algorithm called W2TURN that is worst-case throughput optimal for 2D torus networks. W2TURN is based on a weighted random selection of paths that contain at most two turns. Restricting the maximum number of turns in routing paths to just two enables a simple deadlock-free implementation of W2TURN. In terms of average hop count, W2TURN outperforms the best previously known closed-form worst-case throughput optimal routing algorithm called IVAL [CHECK END OF SENTENCE]. When the network radix is odd, W2TURN achieves the minimum average hop count that can be achieved with 2-turn paths while remaining worst-case throughput optimal. When the network radix is even, W2TURN comes very close to achieving the minimum average hop count while remaining worst-case throughput optimal, within just 0.72 percent on a 12\times 12 torus. We also describe another routing algorithm based on weighted random selection of paths with at most two turns called I2TURN and show that I2TURN is equivalent to IVAL. However, I2TURN eliminates the need for loop removal at runtime and provides a closed-form analytical expression for evaluating the average hop count. The latter enables us to demonstrate analytically that W2TURN strictly outperforms IVAL (and I2TURN) in average hop count. Finally, we present a new optimal weighted random routing algorithm for rings called Weighted Random Direction (WRD). WRD provides a closed-form expression for the optimal distribution of traffic along the minimal and nonminimal directions in a ring topology to achieve minimum average hop count while guaranteeing optimal worst-case throughput. Based on our evaluations, in addition to being worst-case throughput optimal, W2TURN and WRD also perform well in the average case, and outperform the best previously known worst-case throughput optimal routing algorithms with closed-form descriptions in latency and throughput over a wide range of traffic patterns.

Continuous Data Collection Capacity of Dual-Radio Multichannel Wireless Sensor Networks

The performance of data collection in Wireless Sensor Networks (WSNs) can be measured by network capacity. However, few existing works dedicatedly consider the Continuous Data Collection (CDC) capacity for WSNs under the protocol interference model. In this paper, we propose a multipath scheduling algorithm for SDC in single-radio multichannel WSNs and derive its network capacity which is a tighter lower bound compared with the previously best result [CHECK END OF SENTENCE]. We also propose a novel CDC method for dual-radio multichannel WSNs. It significantly speeds up the data collection process, and achieves a capacity of (nW/12M⌈(3.63ρ2+c3 ρ+c4)/H⌉) when Δe ≤ 12 or (nW/MΔe⌈(3.63ρ2+c3 ρ+c4)/H⌉) when Δe >;12, where n is the number of the sensors, M is a constant value and usually M ≪ n, Δe is the maximum number of the leaf nodes having a same parent in the data collection tree, W is the channel bandwidth, H is the number of available orthogonal channels, \rho is the ratio of the interference radius over the transmission radius, c3 = (8π/√(3)) + π + 2, and c4 = (8π/√(3)) + 2π + 6. Extensive simulation results indicate that the proposed algorithms improve network capacity significantly compared with existing works.

Discriminative Feature Selection by Nonparametric Bayes Error Minimization

Feature selection is fundamental to knowledge discovery from massive amount of high-dimensional data. In an effort to establish theoretical justification for feature selection algorithms, this paper presents a theoretically optimal criterion, namely, the discriminative optimal criterion (DoC) for feature selection. Compared with the existing representative optimal criterion (RoC, [CHECK END OF SENTENCE]) which retains maximum information for modeling the relationship between input and output variables, DoC is pragmatically advantageous because it attempts to directly maximize the classification accuracy and naturally reflects the Bayes error in the objective. To make DoC computationally tractable for practical tasks, we propose an algorithmic framework, which selects a subset of features by minimizing the Bayes error rate estimated by a nonparametric estimator. A set of existing algorithms as well as new ones can be derived naturally from this framework. As an example, we show that the Relief algorithm [CHECK END OF SENTENCE] greedily attempts to minimize the Bayes error estimated by the k-Nearest-Neighbor (kNN) method. This new interpretation insightfully reveals the secret behind the family of margin-based feature selection algorithms [CHECK END OF SENTENCE], [CHECK END OF SENTENCE] and also offers a principled way to establish new alternatives for performance improvement. In particular, by exploiting the proposed framework, we establish the Parzen-Relief (P-Relief) algorithm based on Parzen window estimator, and the MAP-Relief (M-Relief) which integrates label distribution into the max-margin objective to effectively handle imbalanced and multiclass data. Experiments on various benchmark data sets demonstrate the effectiveness of the proposed algorithms.

Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem

In this paper, we describe the problem of recommending conference sessions to attendees and show how novel extensions to traditional model-based recommender systems, as suggested in Adomavicius and Tuzhilin [CHECK END OF SENTENCE], can address this problem. We introduce Recommendation Engine by CONjoint Decomposition of ITems and USers (RECONDITUS)-a technique that is an extension of preference-based recommender systems to recommend items from a new disjoint set to users from a new disjoint set. The assumption being that preferences exhibited by users with known usage behavior (e.g., past conference session attendance), which can be abstracted by projections of user and item matrices, will be similar to ones of new (different) users where the basic environment and item domain are the same (e.g., new conference). RECONDITUS requires no item ratings, but operates on observed user behavior such as past conference session attendance. The RECONDITUS paradigm consists of projections of both user and item data, and the learning of relationships in projected space. Once established, the relationships enable predicting new relationships and provide associated recommendations. The approach can encompass several traditional data mining problems where both clustering and prediction are necessary. RECONDITUS has been evaluated using data from the Oracle OpenWorld conference.

ST-CDP: Snapshots in TRAP for Continuous Data Protection

Continuous Data Protection (CDP) has become increasingly important as digitization continues. This paper presents a new architecture and an implementation of CDP in Linux kernel. The new architecture takes advantages of both traditional snapshot technology and recent Timely Recovery to Any Point-in-time (TRAP) architecture [CHECK END OF SENTENCE]. The idea is to periodically insert snapshots within the parity logs of changed data blocks in order to ensure fast and reliable data recovery in case of failures. A mathematical model is developed as a guide to designers to determine when and how to insert snapshots to optimize performance in terms of space usage and recovery time. Based on the mathematical model, we have designed and implemented a CDP module in the Linux system. Our implementation is at block level as a device driver that is capable of recovering data to any point-in-time in case of various failures. Extensive experiments have been carried out to show that the implementation is fairly robust and numerical results demonstrate that the implementation is efficient.

The Digital Marauder's Map: A WiFi Forensic Positioning Tool

Marauder's Map,” a magical map in J.K. Rowling's fantasy series Harry Potter and the Prisoner of Azkaban [CHECK END OF SENTENCE], can be used as a surveillance tool to show all moving objects within the boundary of Hogwarts School of Witchcraft and Wizardry” at a spell. In this paper, we introduce a similar forensic surveillance tool for wireless networks. Our system, the digital Marauder's map, can reveal the locations of WiFi-enabled mobile devices within the coverage area of a high-gain antenna. The digital Marauder's map is built solely with off-the-shelf wireless equipments, and features a mobile design that can be quickly deployed to a new location for instant usage without training. We present a comprehensive set of theoretical analysis and experimental results which demonstrate the coverage and localization accuracy of the digital Marauder's map.

Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity

Current feature selection methods for supervised classification of tissue samples from microarray data generally fail to exploit complementary discriminatory power that can be found in sets of features. Using a feature selection method with the computational architecture of the cross-entropy method, including an additional preliminary step ensuring a lower bound on the number of times any feature is considered, we show when testing on a human lymph node data set that there are a significant number of genes that perform well when their complementary power is assessed, but “pass under the radar” of popular feature selection methods that only assess genes individually on a given classification tool. We also show that this phenomenon becomes more apparent as diagnostic specificity of the tissue samples analysed increases.

Supporting Efficient and Scalable Multicasting over Mobile Ad Hoc Networks

Group communications are important in Mobile Ad hoc Networks (MANETs). Multicast is an efficient method for implementing group communications. However, it is challenging to implement efficient and scalable multicast in MANET due to the difficulty in group membership management and multicast packet forwarding over a dynamic topology. We propose a novel Efficient Geographic Multicast Protocol (EGMP). EGMP uses a virtual-zone-based structure to implement scalable and efficient group membership management. A networkwide zone-based bidirectional tree is constructed to achieve more efficient membership management and multicast delivery. The position information is used to guide the zone structure building, multicast tree construction, and multicast packet forwarding, which efficiently reduces the overhead for route searching and tree structure maintenance. Several strategies have been proposed to further improve the efficiency of the protocol, for example, introducing the concept of zone depth for building an optimal tree structure and integrating the location search of group members with the hierarchical group membership management. Finally, we design a scheme to handle empty zone problem faced by most routing protocols using a zone structure. The scalability and the efficiency of EGMP are evaluated through simulations and quantitative analysis. Our simulation results demonstrate that EGMP has high packet delivery ratio, and low control overhead and multicast group joining delay under all test scenarios, and is scalable to both group size and network size. Compared to Scalable Position-Based Multicast (SPBM) [CHECK END OF SENTENCE], EGMP has significantly lower control overhead, data transmission overhead, and multicast group joining delay.

Packed AES-GCM Algorithm Suitable for AES/PCLMULQDQ Instructions

The level of interest in Galois Counter Mode (GCM) Authenticated Encryption rose significantly within the last few years. GCM is interesting because it is the only authenticated encryption standard that can be implemented in a fully pipelined or parallelized way and it is the most appropriate for encrypting packetized data. McGrew and Viega [CHECK END OF SENTENCE] described (but did not detail) how GHASH can be implemented with more than one multiplier operating in parallel. This paper details how that can be done and shows that, when N multipliers are used, and the multipliers use the approach of multiplying polynomials then applying a modular reduction, a single modular reduction can be used instead on N separate operations. This optimization can be used even when there is a single multiplier, which makes this implementation strategy have a broader appeal. Recently Intel has introduced new ISA instructions into the next generation CPU core, namely: AES family and PCLMULQDQ operating in XMM registers domain. In this paper, we discuss the example implementation of proposed GHASH modifications using above instructions.

Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning

Negative Correlation Learning (NCL) [CHECK END OF SENTENCE], [CHECK END OF SENTENCE] is a neural network ensemble learning algorithm which introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean-square-error (MSE) together with the correlation. This paper describes NCL in detail and observes that the NCL corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This insight explains that NCL is prone to overfitting the noise in the training set. The paper analyzes this problem and proposes the multiobjective regularized negative correlation learning (MRNCL) algorithm which incorporates an additional regularization term for the ensemble and uses the evolutionary multiobjective algorithm to design ensembles. In MRNCL, we define the crossover and mutation operators and adopt nondominated sorting algorithm with fitness sharing and rank-based fitness assignment. The experiments on synthetic data as well as real-world data sets demonstrate that MRNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set. In the experimental discussion, we give three reasons why our algorithm outperforms others.

On the Feature Selection Criterion Based on an Approximation of Multidimensional Mutual Information

We derive the feature selection criterion presented in [CHECK END OF SENTENCE] and [CHECK END OF SENTENCE] from the multidimensional mutual information between features and the class. Our derivation: 1) specifies and validates the lower-order dependency assumptions of the criterion and 2) mathematically justifies the utility of the criterion by relating it to Bayes classification error.

SocioNet: A Social-Based Multimedia Access System for Unstructured P2P Networks

Increasingly, peer-to-peer (P2P) network users expect to be able to search objects by semantic attributes based on their preferences for multimedia content. Partial match search (i.e., search through the use of multimedia content semantic information) has become an essential service in P2P systems. In this paper, we propose SocioNet, a social-based overlay that clusters peers based on their preference relationships as a small-world network. In SocioNet, peers mimic how people form a social network and how they query, by preference, their friends or acquaintances. Hence, SocioNet benefits from two desirable features of a social network: interest-based clustering and small-world properties (i.e., high cluster coefficient among all peers yet short path lengths between any two peers). To realize an interest-based small-world SocioNet, we also investigate the following practical design issues: 1) similarity estimation: we define a quantifiable similarity measure that enables clustering of similar peers in SocioNet; 2) distributed small-world overlay adaptation: peers maintain a small-world overlay under network dynamics; and 3) query strategy under the small-world overlay: we analyze appropriate settings for the Time-to-Live (TTL) value, for TTL-limited flooding, that provides a satisfactory success ratio and avoids redundant message overhead. We use simulations and a real database called AudioScrobbler [CHECK END OF SENTENCE], which tracks users' listening habits, to evaluate the performance of SocioNet. The results show that SocioNet assists peers in locating content at peers with similar interests through short path lengths, and hence, achieves a higher success ratio (than nonsmall-world interest-based overlays and noninterest-based small-world overlays) while reducing message overhead significantly.

An Interconnect Reliability-Driven Routing Technique For Electromigration Failure Avoidance

As VLSI technology enters the nanoscale regime, design reliability is becoming increasingly important. A major design reliability concern arises from electromigration which refers to the transport of material caused by ion movement in interconnects. Since the lifetime of an interconnect drastically depends on the current flowing through it, the electromigration problem aggravates with increasingly growing thinner wires. Further, the current-density-induced interconnect thermal issue becomes much more severe with larger current. To mitigate the electromigration and the current-density-induced thermal effects, interconnect current density needs to be reduced. Assigning wires to thick metals increases wire volume, and thus, reduces the current density. However, overstretching thick-metal assignment may hurt routability. Thus, it is highly desirable to minimize the thick-metal usage, or total wire cost, subject to the reliability constraint. In this paper, the minimum cost reliability-driven routing, which consists of Steiner tree construction and layer assignment, is considered. The problem is proven to be NP-hard and a highly effective iterative rounding-based integer linear programming algorithm is proposed. In addition, a unified routing technique is proposed to directly handle multiple current levels, which is critical in analog VLSI design. Further, the new algorithm is extended to handle blockage. Our experiments on 450 nets demonstrate that the new algorithm significantly outperforms the state-of-the-art work [CHECK END OF SENTENCE] with up to 14.7 percent wire reduction. In addition, the new algorithm can save 11.4 percent wires over a heuristic algorithm for handling multiple currents.

Reducing the Height of Independent Spanning Trees in Chordal Rings

This paper is concerned with a particular family of regular 4-connected graphs, called chordal rings. Chordal rings are a variation of ring networks. By adding two extra links (or chords) at each vertex in a ring network, the reliability and fault-tolerance of the network are enhanced. Two spanning trees on a graph are said to be independent if they are rooted at the same vertex, say, r, and for each vertex v \neq r, the two paths from r to v, one path in each tree, are internally disjoint. A set of spanning trees on a given graph is said to be independent if they are pairwise independent. Iwasaki et al. [CHECK END OF SENTENCE] proposed a linear time algorithm for finding four independent spanning trees on a chordal ring. In this paper, we give a new linear time algorithm to generate four independent spanning trees with a reduced height in each tree. Moreover, a complete analysis of our improvements on the heights of independent spanning trees is also provided.

Distributed Selfish Replication

A commonly employed abstraction for studying the object placement problem for the purpose of Internet content distribution is that of a distributed replication group. In this work, the initial model of the distributed replication group of Leff et al. [CHECK END OF SENTENCE] is extended to the case that individual nodes act selfishly, i.e., cater to the optimization of their individual local utilities. Our main contribution is the derivation of equilibrium object placement strategies that 1) can guarantee improved local utilities for all nodes concurrently as compared to the corresponding local utilities under greedy local object placement, 2) do not suffer from potential mistreatment problems, inherent to centralized strategies that aim at optimizing the social utility, and 3) do not require the existence of complete information at all nodes. We develop a baseline computationally efficient algorithm for obtaining the aforementioned equilibrium strategies and then extend it to improve its performance with respect to fairness. Both algorithms are realizable, in practice, through a distributed protocol that requires only a limited exchange of information.