Catálogo de publicaciones - libros

The Rivest-Shamir-Adleman (RSA) algorithm is a very popular and secure public key cryptosystem, but its security relies on the difficulty of factoring large integers. The General Number Field Sieve (GNFS) algorithm is currently the best known method for factoring large integers over 110 digits. Our previous work on the parallel GNFS algorithm, which integrated the Montgomery’s block Lanczos method to solve large and sparse linear systems over GF(2), is less reliable. In this paper, we have successfully implemented and integrated the parallel General Number Field Sieve (GNFS) algorithm with the new look-ahead block Lanczos method for solving large and sparse linear systems generated by the GNFS algorithm. This new look-ahead block Lanczos method is based on the look-ahead technique, which is more reliable, avoiding the break-down of the algorithm due to the domain of GF(2). The algorithm can find more dependencies than Montgomery’s block Lanczos method with less iterations. The detailed experimental results on a SUN cluster will be presented in this paper as well.

In this paper we propose a new key establishment protocol enabling any data aggregation protocol to be operated securely. This is accomplished by a bidirectional key distribution scheme based on and developed by using synchronized broadcast and multi-level key concept. Our protocol, called SPDA(Security Protocol for Data Aggregation) is well suited for any data aggregation algorithms and applications. Our analysis results prove SPDA’s efficiency meaning that its communication cost is manageable.

Device authentication can reinforce the security of the home network services by ensuring that only specific authorized devices by specific authorized users can access the services. And it is also a mandatory technology for context-aware services in which users do not participate in the service flow. In this paper, we propose a device authentication and access control scheme based on two-layered PKI approach for efficient communication and user convenience. The two layers of our model are Global PKI layer and Localized PKI layer. Global PKI layer uses conventional PKI model. There is only one global root CA, and certificate verification is performed by validating the certificate-chain linked to the root CA. Otherwise, in Localized PKI layer, each home gateway takes a role of root CA which is responsible for issuing device certificates to the devices belong to its domain. We use Global PKI layer for device registration and authentication of inter-home-network, but use Localized PKI layer to authenticate each end-device. Based on this separating, our model provides secure, efficient and user friendly multi-domain device authentication protocols. We also provide a convenient access control scheme using Attribute Mapping Certificate.

A programming model for the automatic construction of USN applications based on Nano-Qplus is proposed in this paper. Nano-Qplus is a sensor network platform developed by ETRI. Programs of nodes such as sensors, routers, sinks and actuators in a sensor network are automatically generated through the technique of this paper. Developers can implement USN applications from models of sensor networks. The configuration information of each node is automatically generated from a model. Then, the execution code is automatically generated using the configuration information. Through the technique of this paper, developers can easily implement USN applications even if they do not know the details of low-level information. The development effort of USN applications also will be decreased because execution codes are automatically generated. Furthermore, developers can consistently construct USN applications from USN models using the proposed tool.

This paper presents a GridIMF framework that provides support for adaptive grid services in response to the change of resource supplies and demands in a dynamic computing environment. The framework features a 3-tier hierarchical resource management structure. At the top is a global information manager that serves as a service broker between resource requesters and providers. Resource providers form virtual organizations, each of which is controlled by a local resource manager. The resource managers schedule the execution of tasks adaptively for efficiency and fault tolerance according to the dynamic resource availability information. The framework provides a common API through an application proxy. The proxy decouples grid applications from the implementation details of the framework.

Conventional GRID service is static (no mobility), and it has many drawbacks such as continuous connection, waste of bandwidth, and service overloading. Wireless GRID supports mobility, however it should consider geographic position to support efficient resource sharing and routing. When the devices in the GRID are highly mobile, there will be much traffic to exchange the geographic position information of each mobile node, and this makes adverse effect on efficient battery usage. To minimize the network traffic between mobile users, we use dead reckoning algorithm for each mobile nodes, where each node uses the algorithm to estimates its own movement (also other node’s movement), and when the estimation error is over threshold, the node sends the UPDATE (including position, velocity, etc) packet to other devices. As the estimation accuracy is increased, each node can minimize the number of UPDATE packet transmission. To improve the prediction accuracy of dead reckoning algorithm, we propose Kalman filter based dead reckoning approach. To experiment our scheme, we implement a popular network game (BZFlag) with our scheme added on each mobile node, and the results show that we can achieve better prediction accuracy and reduction of network traffic by 12 percents.

The information systems manager is often constrained by main-taining a certain threshold amount of memory for an organization. However, this requires more than technical and managerial resolutions, encompassing knowledge management for the group, eliciting tacit knowledge from the end users, and pattern and time series analyses of utilization for various applications.

This paper proposes a framework for building an automated intelligent agent for memory management under the client-server architecture. The emphasis is on collecting the needs of the organization and acquiring the application usage patterns for each client involved in real time. Due to dynamic nature of the tasks, incorporation of a neural network architecture with tacit knowledge base is suggested

To design a stable global computing environment supporting reliable job execution and covering unanticipated state changes of hosts, the dynamic characteristics (i.e. volatilities) of hosts should be considered. Since a host is not dedicated to the system, voluntary hosts are free to leave and join autonomously in the middle of execution. As current systems do not relate volatility to a scheduling procedure, global computing systems suffer from performance degradation, reliability loss, job interruption, and execution time delays. For dependable computation, we propose Advanced Stochastic Host State Modeling (ASHSM), which is based on Markov model relating to execution availability quantifying duration and regularity of execution patterns of each host. Through the model, the system predicts desktop activities and allocates jobs according to the host features. Furthermore ASHSM alleviates unreliability due to unstable resource provision and job suspension during execution.

Reducing energy consumption is a key concern in video data centers, in which disk arrays consume a significant portion of the total energy. Disks typically support multiple power modes including a low-power mode in which they use considerably less energy than in any other mode. Therefore, extending the length of time that disks stay in low-power mode is important for energy conservation. We propose a new energy-aware buffer allocation scheme for clustered video servers which use replication. We first present a data retrieval scheme which adaptively retrieves data from the primary and backup copies so as to allow disks to go into low-power mode. We then analyze the relationship between the retrieval period and the buffer size assigned to a cluster, and examine how buffer allocation influences total energy consumption. Based on this, we propose a new buffer partitioning scheme in which the retrieval period for each cluster can be dynamically changed to adjust disk utilization, with the aim of increasing the number of disks that enter low-power mode. Simulations demonstrate that our scheme saves between 22% to 43% of the energy required for conventional video server operation.

Hotspots of energy consumption and network congestions can be caused by load imbalance among sensor nodes in wireless sensor networks. This may lead to loss of data packets and premature death of sensor nodes which may cause the premature death of entire network. Load-balancing techniques can prolong the lifetime of sensor networks and avoid the occurrence of packet congestions. This paper proposes an approach that using load-balancing network to balancing the load in a static data gathering wireless sensor network. Experiments show the effectiveness of our approach.