Abdulaziz Alsayyari

A Wireless Sensor Networks' Analytics System for Predicting Performance in On-Demand Deployments

The need for advanced tools that provide efficient design of on-demand deployment of wireless sensor networks (WSN) is critical for meeting our nations demand for increased intelligence, reconnaissance, and surveillance. For practical applications, WSN deployments can be time consuming and error prone since they have the utmost challenge of guaranteeing connectivity and proper area coverage upon deployment. This creates an unmet demand for decision-support systems that help manage this complex process. This paper presents research to develop a system for predicting optimal deployments of WSN. Specifically, it presents results of image processing algorithms for terrain classification, results of modeling WSN signal propagation under different terrain conditions, results of optimization and visualization techniques for high-dimensional deployments, and system architecture for efficient integration and future deployment. Results show a feasible approach that can be used to automatically determine areas of high signal obstruction-which is essential to estimate obstruction parameters in simulations-and mapping of accurate WSN path-loss models to enhance the overall decision-making process during predeployment of large-scale WSN.

Comparison of the Propagation Loss of a Real-Life Wireless Sensor Network and Its Complimentary Simulation Model

A hypothesis test based methodology for comparing propagation loss as reported by Modeling and Simulation (M&S) and a real life Wireless Sensor Network (WSN) platform is proposed. The methodology is based on hypothesis testing and statistical evaluation of experimental and simulation data. A software tool for verification of the simulation results is developed in order to automate the process. The comparison tool demonstrates the need for simulation verification by showing cases where simulation and experimental measurements do not agree; hence the simulation model is not representative of actual system being modeled. Experimentation with the platform establishes an empirical path loss model, to be used in simulations.

Engineering education in Shaqra University between theory and application

One of the prominent issues of quality accreditation in academia is evaluation and assessment of the graduates for the conducted education. This evaluation acts as rudder modulator to regulate the gap between field/industry anticipations and the available education. This article introduces statistical analysis for the evaluation and assessment of graduate engineers in civil, mechanical and electrical engineering areas for the quality of education conducted in Shaqra University. A cross sectional study for around 63% of engineering college graduates that practicing engineering work is carried out. A comprehensive and concise questionnaire has been designed to measure the objectives of the study. The study is aimed basically to determine the gap between the theory and application. The quality of conducted education was assessed by the investigation of the effectiveness of elective courses, laboratory courses, graduation projects, industrial training, and English language courses. Yet, the results show that around 70% of the graduates find a significant compatibility between the conducted education and the field, and only 21% of them claim that a reasonable compatibility between their education and industry exist.

Adaptive LMS power series analytical solution for differential algebraic equations

Differential Algebraic Equations (DAEs) are essential in the analysis of many engineering, physical, chemical and mathematical systems. Numerical methods are popular to solve highly nonlinear and even linear DAEs. On the other hand, analytical solutions for DAEs are very limited. This work presents an efficient analytical solution for DAEs based on power series regressions. The coefficients of the estimated power series solution are adaptively computed employing the computationally simple signed least mean squares adaptive algorithm. The DAEs are assumed to be on the general implicit canonical form. The proposed adaptive power series method can solve linear and nonlinear DAEs systems. The efficient and accurate solutions provided by the technique proposed are illustrated through simulated examples. It is shown that the performance of the technique proposed outperforms existing conventional and modern methods.