We propose Computational Sensor Networks as a methodology to exploit models of physical phenomena in order to better understand the structure of the sensor network. To do so, it is necessary to relate changes in the sensed variables (e.g., temperature) to the aspect of interest in the sensor network (e.g., sensor node position, sensor bias, etc.), and to develop a computational method for its solution. As examples, we describe the use of the heat equation to solve (1) the sensor localization problem, and (2) the sensor bias problem. Simulation and physical experiments are described.
It's advantageous for computational scientists to have the capability to perform interactive visualization on their desktop workstations. For data on large unstructured meshes, this capability is not generally available. In particular, particle tracing on unstructured grids can result in a high percentage of non-contiguous memory accesses and therefore may perform very poorly with virtual memory paging schemes. The alternative of visualizing a lower resolution of the data degrades the original high-resolution calculations. This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk les for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that during the streamline construction only a very small amount of data are brought i n to the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is signicantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-20 megabytes. Our test results also show that this approach i s m uch more ecient than relying on virtual memory and operating system's paging algorithms.
Abstract-This paper addresses the model-based localization of sensor networks based on local observations of a distributed phenomenon. For the localization process, we propose the rigorous exploitation of strong mathematical models of distributed phenomena. By unobtrusively exploiting background phenomena, the individual sensor nodes can be localized by only observing its local surrounding without the necessity of heavy infrastructure. In this paper, we introduce two novel approaches: (a) the polynomial system localization method (PSL-method) and (b) the simultaneous reconstruction and localization method (SRL-method). The first approach (PSLmethod) is based on restating the mathematical model of the distributed phenomenon in terms of a polynomial system. These equations depend on both the state of the phenomenon and the node locations. Solving the system of polynomials for each individual sensor node directly leads to the desired locations. The second approach (SRL-method) basically regards the localization problem as a simultaneous state and parameter estimation problem with the node locations as parameters. By this means, the distributed phenomenon is reconstructed and the individual nodes are localized in a simultaneous fashion. In addition, within this framework the uncertainties in the mathematical model and the measurements are considered. The performance of the two different localization approaches is demonstrated by means of simulation results.
Abstract. We summarize the Computational Engineering and Science program at the University of Utah. Program requirements as well as related research areas are outlined. To obtain the MS degree in CES, a student must complete courses and present original research in scientific computing, scientific visualization, mathematical modeling, and the case studies in CES. The outlined research areas include scientific visualization, computational combustion, computational physics, computational chemistry, mathematical and computational biology, and computational medicine. Computational Engineering and Science ProgramThe grand computational challenges in engineering and science require for their resolution a new scientific approach. As one report points out, "The use of modern computers in scientific and engineering research and development over the last three decades has led to the inescapable conclusion that a third branch of scientific methodology has been created. It is now widely acknowledged that, along with traditional experimental and theoretical methodologies, advanced work in all areas of science and technology has come to rely critically on the computational approach." This methodology represents a new intellectual paradigm for scientific exploration and visualization of scientific phenomena. It permits a new approach to the solution of problems that were previously inaccessible.At present, too few researchers have the training and expertise necessary to utilize fully the opportunities presented by this new methodology; more importantly, traditional educational programs do not adequately prepare students to take advantage of these opportunities. Too often we have highly trained computer scientists whose knowledge about engineering and sciences is at the college sophomore, or lower, level. Traditional educational programs in each of these areas stop at the sophomore level -or earlier -in the other area. Also, education tends to be ad hoc, on the job and self-taught.This situation has arisen because the proper utilization of the new methodology requires expertise and skills in several areas that are considered disparate in traditional educational programs. The obvious remedy is to create new programs that do provide integrated training in the relevant areas of science, mathematics, technology, and algorithms. The obvious obstacles are territorial nature of established academic units, entrenched academic curricula, and a lack of resources.At the University of Utah the School of Computing (located in the College of Engineering), with the Departments of Mathematics and Physics (located in the College of Science) have established a graduate program that we consider a first step towards the modernization of the University's curriculum in what we call "Computational Engineering and Science" (CES). Our goal is to provide a mechanism by which a graduate student can obtain integrated expertise and skills in all areas that are required for the solution of a particular problem via the computational methodology.We have recen...
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