38237419
Comparison of inter-area rekeying algorithms for secure wireless group communications
Many emerging mobile wireless applications depend upon secure group communications, in which data is encrypted and the group's data encryption key is changed whenever a member joins or leaves the group's session. Hierarchical approaches have recently been proposed to manage the distribution of the data encryption key in a scalable manner for fixed (non-mobile) networks. In this paper, we characterize the impact of mobility on secure rekeying of group communication in a hierarchical key-distribution framework. We propose several rekeying algorithms that preserve confidentiality as members move within the hierarchy. The algorithms differ in the locality of communication, the amount of messages needed to rekey the data key/key-encryption key, the key-encryption key rekey rate, and the number of key-encryption keys held by group members. We develop Markov models to quantify the performance of the proposed algorithms. Our results shows that the FEDRP and SR inter-area rekeying algorithms are superior under different circumstances. In the situation of lower arrival rate and higher mobility, SR has the lowest intra-AS message rate, rekey rate and a low inter-AS message rate. On the other hand, with higher arrival rate and lower mobility, FEDRP has a low rekey rate, inter-AS message rate and the lowest intra-AS message rate. This is achieved by allowing members to hold a small number of keys. In a wireless environment, where bandwidth is often a limiting resource, minimization of communication overhead is of critical importance. This goal could be achieved by using a dynamic strategy to combine the benefits of FEDRP and SR algorithms.
14748826
QoS-Aware Optimization of Composite-Service Fulfillment Policy
In a service-oriented IT infrastructure, functional capabilities of a computing component are externalized via one or more service interfaces. Driven by the demand for business agility and return-on-investment optimization, various dynamic service discovery and composition technologies have been proposed and developed with a common goal of enabling business-aligned fulfillment of customer requests. However, from the viewpoint of capacity planning and IT optimization, much work is still needed in helping an enterprise decide the "optimal" IT resources necessary for deploying the atomic services in support of those composite ones. The service deployment decision must be integrated with the request fulfillment policy so that the differentiated quality-of-service (QoS) requirements of service requests can be met, for instance, with minimum hardware/software cost. In this paper, we propose an approach for QoS-aware optimization of composite-service fulfillment policy. Without loss of generality, we assume that the optimization goal is to minimize the number of machines subject to response time and throughput requirements. After presenting our approach to the optimization problem using the assumption, we show that an NP-hard throughput optimization problem must be attacked. We then illustrate how we attack the problem via an efficient heuristic algorithm. The algorithm decomposes the end-to-end response time requirement for each type of composite service into atomic-service level response time assurance, and co-locate atomic services with similar response time assurance on machines with similar utilization characteristics. The algorithm exemplifies an integrated approach to optimizing service deployment and service composition. We demonstrate that the algorithm achieves a substantially higher throughput than a common baseline algorithm.
10539713
On optimal routing with multiple traffic matrices
Routing optimization is used to find a set of routes that minimizes cost (delay, utilization). Previous work has addressed this problem for the case of a known, static end-to-end traffic matrix. In the Internet, it is difficult to accurately estimate a traffic matrix, and the constantly changing nature of Internet traffic makes it costly to maintain optimal routing by responding to traffic changes. Thus, it is of interest to maintain a set of routes that are "good" for a number of different possible traffic scenarios. In this paper, we explore ways to find an optimal set of routes with multiple traffic matrices to minimize expected cost. We focus on two general approaches, source-destination routing and destination routing. In the case of source-destination routing, we extend existing methods with a single traffic matrix to solve the optimization problem with multiple traffic matrices: we extend the convex optimization solution methods for a single traffic matrix to the multiple traffic matrix case; we also extend the gradient-based solution methods for a single traffic matrix to the multiple traffic matrix case. However, the multiple traffic matrix case requires many more control variables. In the case of destination routing, we encounter many more differences from the single traffic matrix case. The loop-free property, which is valid for the single traffic matrix case, is no longer valid for the multiple traffic matrix case, and it is difficult to extend existing methods for a single traffic matrix to solve the optimization problem with multiple traffic matrices. We show that it is NP-complete even to determine the feasibility of multiple traffic matrices. We thus propose and evaluate a heuristic algorithm for this case.
206924421
Classification of access network types: Ethernet wireless LAN, ADSL, cable modem or dialup?
Ethernet, wireless LAN, ADSL, cable modem and dialup are common access networks, but have dramatically different characteristics. Fast and accurate classification of access network type can improve protocol or application performance significantly. In this paper, we propose a simple and efficient end-end scheme to classify the type of an access network into three categories: Ethernet, wireless LAN and low-bandwidth connection. Our scheme is based on the intrinsic characteristics of the various access networks and utilizes the median and entropy of packet pair inter-arrival times. Extensive experiments show that our scheme obtains accurate classification results in a very short time (10 to 100 seconds).
7423428
Optimal Power Allocation in Wireless Networks with Transmitter-Receiver Power Tradeoffs
For many wireless communication links, such as those employing turbo codes or sequentially-decoded convolutional codes, the power consumption of the decoder at the receiver depends on the received signal power and, hence, on the transmitted signal power. By transmitting a signal at a power higher than the minimum required for successful reception, the transmitter can “assist” the receiver by reducing the receiver decoding effort and thereby reducing the overall power consumption on the link. In this paper, we consider sender-receiver power tradeoffs in data-gathering trees. We formulate an optimization problem to optimally assign power to the nodes in the tree for maximizing the lifetime of the data-gathering tree, which is equivalent to the time until network partition due to battery outage. We propose a Binary Search Algorithm for optimal power assignment among nodes that maximizes the tree lifetime. Our Binary Search Algorithm can be easily extended to handle practical considerations such as node mobility and peak power constraints at nodes. Using turbo codes as an example of a channel coding technique, we demonstrate significant improvements in network lifetimes as a result of sender-receiver power tradeoffs. These improvements are observed under a wide variety of network conditions and are more pronounced in densely deployed networks and networks with asymmetric power costs.
9156106
Blind Signal Separation of Similar Pitches and Instruments in a Noisy Polyphonic Domain
In our continuing work on ”Blind Signal Separation” this paper focuses on extending our previous work [1] by creating a data set that can successfully perform blind separation of polyphonic signals containing similar instruments playing similar notes in a noisy environment. Upon isolating and subtracting the dominant signal from a base signal containing varying types and amounts of noise, even though we purposefully excluded any identical matches in the dataset, the signal separation system successfully built a resulting foreign set of synthesized sounds that the classifier correctly recognized. Herein, this paper presents a system that classifies and separates two harmonic signals with added noise. This novel methodology incorporates Knowledge Discovery, MPEG7-based segmentation and Inverse Fourier Transforms.
6161007
Light field capturing with lensless cameras
We introduce a novel approach to capturing light field with lensless cameras. By moving the cameras back and forth, we capture a set of images. We show that it is possible to reconstruct the light field from these blurry images. The problem is formulized in a way similar to computer tomography, so that the light field can be reconstructed using existing algorithms. The light field can then be used to render 3D scenes. Synthetic examples are presented to show the effectiveness of the proposed method.
83568
Fingerprint Reference Point Detection Based on Local Axial Symmetry
Reference point detection is an important process for fingerprint analysis. In this paper, we propose a novel feature which is named local axial symmetry (LAS) and present an algorithm to calculate reference point of a fingerprint based on this kind of feature. Experimental results demonstrate its feasibility, validity and the ability to detect reference points of all classes of fingerprints including arch-type fingerprints which is difficult to locate a stable reference point with other methods
8800642
Multimedia Data Mining for Traffic Video Sequences
In this paper, a multimedia data mining framework for discovering important but previously unknown knowledge such as vehicle identification, traffic flow, and the spatio-temporal relations of the vehicles at the intersections from traffic video sequences is proposed. The proposed multimedia data mining framework analyzes the traffic video sequences by using background subtraction, image/video segmentation, object tracking, and modeling with multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over an intersection. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data-mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-a-vis traffic operations. A real-life traffic video sequence is used to illustrate the effectiveness of the proposed multimedia data mining framework.
6853058
Markov chain correlation based clustering of gene expression data
An efficient Markov chain correlation based clustering method (MCC) has been proposed for clustering gene expression data. The gene expression data is first normalized and Markov chains (MC) are constructed from the dynamics of the gene expressions, in which the behavior of the genes at each step of the experiment can be taken into account. Based on the correlation of one-step Markov chain transition probabilities, an agglomerative method is employed to group the series that have similar behavior at each point. The proposed MCC clustering method has been applied to four gene expression datasets to obtain a number of clusters. The results show that the MCC method outperforms the commonly used K-means method and produces clusters that are more meaningful in terms of the similarity of the grouped genes. Another advantage of the proposed method over the existing clustering methods is that the knowledge of the group number is not required.