Services such as audio/video streaming, advertising, and software distribution can be delivered much more efficiently by network service providers if multicast data distribution is used. However, accounting for the re...
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Services such as audio/video streaming, advertising, and software distribution can be delivered much more efficiently by network service providers if multicast data distribution is used. However, accounting for the resources consumed by subscribers becomes essential. Using an e-commerce (payment recovery) operational environment, in turn, requires support for security properties such as authentication, authorization, data confidentiality, and non-repudiation. However, multicast data distribution is inherently more susceptible to attack than unicast distribution, given its greater susceptibility to interception of the network traffic. Securing multicast sessions in B2C commerce requires authentication of the end-host, authorization of the subscriber and his e-payments, and accounting for a subscriber's consumed resources. In this paper, we present a comprehensive survey of the literature on e-payment platforms and protocols that provide some form of security to e-commerce transactions, followed by a comparative study of these e-payment platforms and protocols. We show the security problems that are currently unaddressed by the existing multicast protocols and e-payment platforms and protocols, in securing the operating environment of B2C commerce for multicast networks
Autonomic computersystems adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and s...
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Autonomic computersystems adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system. The self adaptation can be achieved by tuning the software, balancing the load or through hardware provisioning. This paper investigates a feed-forward adaptation scheme in which tuning and provisioning decisions are based on a dynamic predictive performance model of the system and the software. The model consists of a layered queuing network whose parameters are tuned by tracking the system with an Extended Kalman Filter. An optimization algorithm searches the system configuration space by using the predictive performance model to evaluate every configuration.
We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed items...
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We propose a new method, called closed multidimensional sequential pattern mining, for mining multidimensional sequential patterns. The new method is an integration of closed sequential pattern mining and closed itemset pattern mining. Based on this method, we show that (1) the number of complete closed multidimensional sequential patterns is not larger than the number of complete multidimensional sequential patterns (2) the set of complete closed multidimensional sequential patterns covers the complete resulting set of multidimensional sequential patterns. In addition, mining using closed itemset pattern mining on multidimensional information would mine only multidimensional information associated with mined closed sequential patterns, and mining using closed sequential pattern mining on sequences would mine only sequences associated with mined closed itemset patterns
An algorithm for time division multiple access (TDMA) is found to be applicable in converting existing distributed algorithms into a model that is consistent with sensor networks. Such a TDMA service needs to be self-...
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In this study, a novel approach is used to identify nondominated solutions to multiobjective optimization problems. The method is composed of a Particle Swarm Optimizer (PSO) coupled with a neural network. The PSO is ...
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In this study, a novel approach is used to identify nondominated solutions to multiobjective optimization problems. The method is composed of a Particle Swarm Optimizer (PSO) coupled with a neural network. The PSO is used to find an initial set of nondominated solutions. These nondominated solutions are then used to construct a general regression neural network that generates a considerably larger set of nondominated solutions. Our neural network enhancement process is demonstrated on a test suite of six instances of bi-criteria semidesirable facility location problems. Results show that the set of nondominated solutions developed by the neural network is, on average, 25 times larger than the initial set found by PSO, and in many instances dominate those identified by PSO. The method developed within is straightforward and general and is a new alternative to multiobjective optimization with decision variables in continuous space.
This paper presents interfacing techniques to integrate a virtual environment (VE), computer graphics, image-based modeling, and other technologies in a networked system for robot control. Technologies such as network...
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Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts, as well as indoor laboratory facilities, in-situ resource plants, power plants, and scientific/surface...
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ISBN:
(纸本)0784408300
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts, as well as indoor laboratory facilities, in-situ resource plants, power plants, and scientific/surface mobility equipment. Transporting the materials and equipment required to build the necessary habitats would be costly and difficult. However, the use of planetary resources to produce construction materials, or even as naturally found (such as lunar regolith utilized as an outer layer for radiation shielding, or regolith in bags as wall structure), could drastically reduce the upmass for habitats. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. For example a 3-layer habitat wall created from 20 possible materials all with varying thicknesses would have #number of layers(possible material types)∧(possible thicknesses) = 320∧inf possible solutions. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces. To overcome the large search regions, a habitat wall design tool using genetic algorithms has been developed. GAs use a "survival of the fittest" philosophy, where the most fit individuals are more likely to survive and reproduce, therefore transmitting their genes to the next generation. This paper focuses on finding optimal GA parameters for the habitat optimization tool. This tool is a multiobjective formulation of structural analysis, heat loss, radiation, and meteoroid protection. Some of the constraint calculations (i.e. safety factor, deflection, and heat loss) require long computation times. Even though each material has a designated thickness range, the number of different values within that range is near infinite;therefore, the searc
Supply chain management (SCM) environments are often dynamic markets providing a plethora of information, either complete or incomplete. It is, therefore, evident that such environments demand intelligent solutions, w...
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Supply chain management (SCM) environments are often dynamic markets providing a plethora of information, either complete or incomplete. It is, therefore, evident that such environments demand intelligent solutions, which can perceive variations and act in order to achieve maximum revenue. To do so, they must also provide some sophisticated mechanism for exploiting the full potential of the environments they inhabit. Advancing on the way autonomous solutions usually deal with the SCM process, we have built a robust and highly-adaptable mechanism for efficiently dealing with all SCM facets, while at the same time incorporating a module that exploits data mining technology in order to forecast the price of the winning bid in a given order and, thus, adjust its bidding strategy. The paper presents our agent, Mertacor, and focuses on the forecasting mechanism it incorporates, aiming to optimal agent efficiency
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