Performance Analysis of IEEE 802.15.4 with Non-beacon enabled CSMA/CA

Abstract. This paper proposes an analytical model of IEEE 802.15.4, which is a standard toward low complexity, low power consumption and low data rate wireless data connectivity. In this paper, we concentrate on the MAC performance of the IEEE 802.15.4 LR-WPAN in a star topology with unslotted CSMA/CA channel access mechanism under non-saturated modes. Our approach is to model stochastic behavior of one device as a discrete time Markov chain model. We believe that many WSN applications would benefit from our analytical model because many applications in WSN generate traffic in non-saturated mode. We obtain five performance measures : throughput, packet delay, number of backoff, energy consumption and packet loss probability. Our results are used to find optimal number of devices satisfying some QoS requirements

An efficient computational method for statistical moments of Burger's equation with random initial conditions

The paper is concerned with efficient computation of numerical solutions to Burger's equation with random initial conditions. When the Lax-Wendroff scheme (LW) is expanded using the Wiener chaos expansion (WCE), random and deterministic effects can be separated and we obtain a system of deterministic equations with respect to Hermite-Fourier coefficients. One important property of the system is that all the statistical moments of the solution to the Burger's equation can be computed using the solution of the system only. Thus LW with WCE presents an alternative to computing moments by the Monte Carlo method (MC). It has been numerically demonstrated that LW with WCE approach is equally accurate but substantially faster than MC at least for certain classes of initial conditions

Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence

Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms

A Stochastic Analysis Of The Scale Up Problem For Flow In Porous Media

We present a numerical study of the scale up problem for the fractional flow function in the Buckley-Leverett equation for flow in porous media. The scale up problem is to define an averaged equation by local spatial averages, mapping from a micro-physical description to a mesophysical description and from a fine discretization grid to a coarser one. Scale up leads to the closure problem, which is the definition of the nonlinear terms in the averaged equation, as these terms are not respected by the averaging process. If the micro-physics is specified by a geostatistical probability ensemble, we see that the scaled up and This paper is dedicated to the memory of our colleague and friend, Paulo Paes-Leme. y Supported by the Applied Mathematics Subprogram of the U.S. Department of Energy DE-FG02-90ER25084, the DOE grant DEFG0295ER, the Army Research Office, grant DAAL04-95-10414 and the National Science Foundation, grant DMS-9500568. z Supported by the Applied Mathematics Subprogr..

Celebrating Diversity by Sharing Multiple Solution Methods

A classroom vignette presents teaching practices that develop an appreciation of diverse thinking