1276171
Using Lagrangean Relaxation for Service Location Planning with QoS Constraints in Large-Scale Networks
Current trends in computing indicate that there is a great potential for service oriented computing and similar technologies, such as Cisco's Application Oriented Networks, where services provide a higher-level of abstraction to traditional applications. In such cases, providing and consuming services and establishing a relationship between consumers (users of services) and producers (providers of services) are still challenging and vastly researched aspects. We define a service location planning problem (SLP) that is novel in the approach it takes to match consumers to producers, such that the cost of providing services is minimized while taking into account quality of service constraints of throughput and delay. In previous work, we presented an integer linear programming (ILP) model to formulate the service location planning problem. In this paper, we present Lagrangean Relaxation of the model for large-scale networks. We use the approach of Trick [3] to decrease computational overheads.
321445
Detection of Masquerade Attacks on Wireless Sensor Networks
We propose two lightweight techniques to detect masquerade attacks on wireless sensor networks (WSN). Our solutions take into consideration, important WSN properties like coverage, connectivity, data aggregation and specific communication patterns. The two proposed techniques complement each other when used concurrently. The mutual guarding (MG) technique does not work when nodes are not completely covered by their neighbors or when adversary has shorter transmission range than the sensor nodes. It also does not protect nodes near the boundary. The SRP technique does not have these drawbacks. In this paper, we present our proposed techniques and analyze their performance in terms of successful masquerade detection rate and traffic and computational overhead.
19327594
Mobility-Pattern-Based Anomaly Detection Algorithm in Mobile Networks
Numerous approaches have been proposed for intrusion detection in immobile networks. However, little research work has been done in actually implementing them, especially for anomaly detection, in mobile networks. In this paper, we present an efficient mobility-pattern-based (MPB) anomaly detection algorithm. It can effectively identify abnormal mobility patterns of the nodes in mobile networks. In the proposed algorithm, the normal mobility profile of a specific node is characterized by a multi-leaf tree structure in which each node corresponds to a possible destination cluster. A normal mobility profile is generated during the training process through data mining and fuzzy logic techniques. More specifically, data mining techniques are used for cluster classifications, and fuzzy logic techniques are used to integrate the 'similar'' pattern strings after the corresponding cluster generation. These two techniques are also used during the testing process for distinguishing abnormal mobility patterns of the node from those of the normal in mobile networks. Simulation results demonstrate that our proposed MPB anomaly detection algorithm can achieve good performance in terms of detection rate and false alarm rate for nodes with regular movement behaviors by fine tuning the design parameter - threshold, that can be chosen efficiently.
155165
Multivariate Integration on Hypercubic and Mesh Networks
Abstract We analyze a class of adaptive algorithms for integration over N-dimensional hyper-rectangular or simplical regions, on distributed systems. An adaptive algorithm attempts to achieve the requested accuracy by refining the subdivision of the integration region, thus allowing for a concentration of subdivisions near singularities. At the subdivision of a region, the error behaves according to a prescribed model, relating the error of the parent region to that of its children. The analysis can also be applied to problems in other areas, as long as the task selection is based on a priority function which behaves according to a suitable model. Using an efficient management of the subregions, we show that an O (p/ log p) speedup can be achieved on a p-processor hypercubic network, such as shuffle exchange, butterfly and hypercube. Furthermore, a speedup of O ( p ) can be achieved on a p × p mesh network. We also show that our algorithms compare favorably with well-known dynamic load balancing strategies.
17746198
Large—scale parallel numerical integration
We present and analyze strategies which can be used for the parallel computation of large numbers of integrals which may be of different levels of difficulty. Parallelization on the integral level, which is generally used for large numbers of integrals, is combined with parallelization on the subregion level, which enables handling local integration difficulties within individual problems. This results in a new, hierarchical algorithm which incorporates load balancing on the integral level and on the subregion level. We report test results of the software and show that the hierarchical approach leads to a scalable integration algorithm.
17889083
Managing computing resources in active intranets
The objective of this paper is to present an economic pricing-based resource management technique for Intranets that has the capability of managing Intranet resources from an organizational perspective. We discuss the adoption of this pricing scheme at three stages: application level, node level, and data-stream level implementations. These three levels demonstrate how a pricing based approach can be used with the current technology and also be migrated to evolving network architectures such as active networks.
62172477
A unified framework for uncertainty propagation in automatic shape tracking
Uncertainty handling plays an important role during shape tracking. We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences [22]. Nevertheless, this approach required user initialization of the tracking process. This paper solves the automatic initialization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework. We show how to propagate the local detection uncertainties of multiple shape candidates during shape alignment, fusion with the predicted shape prior, and fusion with subspace constraints. As a result, we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood. Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart. Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations.
14869645
Multi-modality model-based registration in the cardiac domain
Recent advances in CT technology have made possible the acquisition of high resolution images of the heart in which important anatomical features such as the coronary vessels are visible. The state of the art in MR Imaging techniques, on the other hand, typically provides lower resolution but can facilitate the differentiation of viable (healthy) cardiac tissue from infarcted (injured) tissue. The registration of CT and MR images of the same heart permit the association of anatomy with function. As an example, given an infarcted region, the coronary vessels responsible for irrigating that region may be isolated and targeted for treatment (e.g., coronary bypass). We demonstrate a software package, PROTEUS, which is capable of registering CT and MR images. Since the manifestation of cardiac tissue in the two modalities differs so greatly we rely on features to perform the matching. Specifically, we employ a model-based approach that relies on segmented contours delineating the inner and outer borders of the left ventricle (LV). Given these contours, our technique is completely automatic.
18859582
Auctioning Vertically Integrated Online Services: Computational Approaches for Real-Time Allocation
We develop three auction-based pricing and allocation solution methods for the case where a capacity-constrained online service provider offers multiple classes of unique, one-time services with differentiated quality. Consumers desire exactly one of the many service classes offered. We call such a setting a vertically integrated online services market. Examples of these services are webcasting of special events over the Internet, provision of video-on-demand, and allocation of grid computing resources. We model the pricing and allocation decision faced by firms in such a setting as a knapsack problem with an added preference elicitation dimension. We present a variety of computational solution approaches based on adaptations of the traditional greedy heuristic for knapsack problems. The solution approaches vary in efficacy depending on whether bidders are restricted to bid in one service class or allowed to bid in multiple service classes, as well as on the overall variability of the demand. In the case bidders can bid in multiple classes but are interested in consuming only one service class, a direct application of the heuristics developed for the single service case results in a nonfair allocation. We develop a novel data structure to eliminate the unfair allocation while maintaining the original computation complexity of the simpler setting. The paper contributes by presenting a menu of auction clearing mechanisms for selling vertically integrated online services.
22023969
Some issues in privacy data management
A method and apparatus is provided for aligning and press-fitting connector terminals into a substrate. The terminals are initially supported together by a bandolier strip, and an alignment tool aligns the terminals with apertures in the substrate to which the terminals will be press-fitted. A pressing tool engages the shanks of the terminals to fold the bandolier away from the terminals and thereafter press the terminals into the apertures.