Catálogo de publicaciones - libros

and have rapidly emerged and affirmed respectively as the paradigm for high performance computing and the paradigm for user-friendly computing. The conjunction of such paradigms are now generating a new one, the , which aims at extending classic grids with characteristics of pervasive computing like spontaneous and transparent integration of mobile devices, context-awareness, pro-activity, and so on. In this paper, we present mechanisms and a software infrastructure for executing tasks in a pervasive grid. In particular, the proposed solution, which provides an implementation of the model, enables users to submit tasks and to pick up results without concerning on requiring and handling hardware resources.

The computing grid no longer encompasses only traditional computers to perform coordinated tasks, as also low-end devices are now considered active members of the envisioned pervasive grid. Wireless sensor networks play an important role in this vision, since they provide the means for gathering vast amounts of data from physical phenomena. However, the current integration of wireless sensor networks and the grid is still primitive; one important aspect in this integration is providing higher-level abstractions for the development of applications, since accessing the data from wireless sensor networks currently implies dealing with very low-level constructs. We propose TinySOA, a service-oriented architecture that allows programmers to access wireless sensor networks from their applications by using a simple service-oriented API via the language of their choice. We show an implementation of TinySOA and some sample applications developed with it that exemplify how easy grid applications can integrate sensor networks.

This paper proposes a bio-inspired approach for the construction of a Grid information system in which metadata documents that describe Grid resources are disseminated and logically reorganized on the Grid. A number of ant-like agents travel the Grid through P2P interconnections and use probability functions to replicate resource descriptors and collect those related to resources with similar characteristics in nearby Grid hosts. Resource reorganization results from the collective activity of a large number of agents, which perform simple operations at the local level, but together engender an advanced form of “swarm intelligence” at the global level. An adaptive tuning mechanism based on the epidemic paradigm is used to regulate the dissemination of resources according to users’ needs. Simulation analysis shows that the epidemic mechanism can be used to balance the two main functionalities of the proposed approach: entropy reduction and resource replication.

Credibility of knowledge grid members who act as a cooperative decision making community, affects the degree of accuracy of the decisions made. Apparently, decisions made by a decision maker should be affected by the degree of the decision maker’s credibility. The problem is how to estimate decision makers’ credibility within a knowledge grid environment, specially, those environments in which the number of decision makers is altered dynamically. In this article, a new approach to estimate the credibility of decision makers based upon the opinion of the other members of decision makers’ community within a dynamic knowledge grid environment is proposed.

The image of a license plate is scanned by the camera attached to a mobile PDA device and the numbers on the plate are detected by the image processing parts in the proposed Mobile system. Then the numbers and the location of a mobile PDA device are encoded and transmitted along with the location information to a remote server through a wireless communication network. Finally, the server decodes the transmitted data as a text format and transmits it to the destination user. Consequently, this paper contributes a case study on the embedded system for designing of intelligent interface between a moving vehicle and a mobile PDA device, using a spatial relative distance scheme. The experimental results show that detection and tracking of a location of moving vehicle can be conducted efficiently with a mobile PDA device in real-time through wireless communication system and Internet.

With the actual applications of the wireless sensor networks growing, the challenges of the actual deployment get more and more. To improve deployment efficiency, reduce the deployment cost and evaluate the deployment risk, a planning and deployment platform for wireless sensor networks has been built. In this paper, the workflow of the planning and deployment platform for wireless sensor networks and its implementation framework are emphasized. The implementation framework, which is based on J-Sim simulator, provides the implementation details of the platform. An integrated workflow for the platform is illuminated to comprehend the framework clearly.

Geoscience analysis is currently limited by cumbersome access and manipulation of large datasets from remote sources. Due to their data-heavy and compute-light nature, these analysis workloads represent a class of applications unsuited to a computational grid optimized for compute-intensive applications. We present the Script Workflow Analysis for MultiProcessing (SWAMP) system, which relocates data-intensive workflows from scientists’ workstations to the hosting datacenters in order to reduce data transfer and exploit locality. Our colocation of computation and data leverages the typically reductive characteristics of these workflows, allowing SWAMP to complete workflows in a fraction of the time and with much less data transfer. We describe SWAMP’s implementation and interface, which is designed to leverage scientists’ existing script-based workflows. Tests with a production geoscience workflow show drastic improvements not only in overall execution time, but in computation time as well. SWAMP’s workflow analysis capability allows it to detect dependencies, optimize I/O, and dynamically parallelize execution. Benchmarks quantify the drastic reduction in transfer time, computation time, and end-to-end execution time.

This paper presents an edge detection method based on bilateral filtering taking into account both spatial closeness and intensity similarity of pixels in order to preserve important visual cues provided by edges and reduce the sharpness of transitions in intensity values as well. In addition, the edge detection method proposed in this paper is achieved on sampled images represented on a newly developed virtual hexagonal structure. Due to the compact and circular nature of the hexagonal lattice, a better quality edge map is obtained. We also present a parallel implementation for edge detection on the virtual hexagonal structure that significantly increases the computation speed.