The Department of Mathematics and Statistics offers degree curricula in mathematics and in applied mathematics (with its various options), as well as minors and a minor in statistics. Majors acquire a firm foundation in mathematics preparing them for further study, or for careers in mathematics or statistics and related fields. For a minor in MATH or STAT see the “Minors” heading earlier in this section.

Mathematics of Statistics.

The Internet of Things is a new concept which will result in a new information technology revolution and economic boom.Many countries have integrated Internet of Things into their national development strategies to promote and lead the future economic development with the construction of Internet of Things.In this paper,the concept,current status,prospects and challenges of Internet of things are overviewed.

Overview of the Internet of Things

Supervisory Control and Data Acquisition (SCADA) is a technology for monitoring and controlling distributed processes. SCADA provides real-time data exchange between a control/monitoring centre and field devices connected to the distributed processes. A SCADA system performs these functions using its four basic elements: Field Instrumentation Devices (FIDs) such as sensors and actuators which are connected to the distributed process plants being managed, Remote Terminal Units (RTUs) such as single board computers for receiving, processing and sending the remote data from the field instrumentation devices, Master Terminal Units (MTUs) for handling data processing and human machine interactions, and lastly SCADA Communication Channels for connecting the RTUs to the MTUs, and for parsing the acquired data. Generally, there are two classes of SCADA hardware and software; Proprietary (Commercial) and Open Source. In this paper, we present the design and implementation of a low-cost, Open Source SCADA system by using Thinger.IO local server IoT platform as the MTU and ESP32 Thing micro-controller as the RTU. SCADA architectures have evolved over the years from monolithic (stand-alone) through distributed and networked architectures to the latest Internet of Things (IoT) architecture. The SCADA system proposed in this work is based on the Internet of Things SCADA architecture which incorporates web services with the conventional (traditional) SCADA for a more robust supervisory control and monitoring. It comprises of analog Current and Voltage Sensors, the low-power ESP32 Thing micro-controller, a Raspberry Pi micro-controller, and a local Wi-Fi Router. In its implementation, the current and voltage sensors acquire the desired data from the process plant, the ESP32 micro-controller receives, processes and sends the acquired sensor data via a Wi-Fi network to the Thinger.IO local server IoT platform for data storage, real-time monitoring and remote control. The Thinger.IO server is locally hosted by the Raspberry Pi micro-controller, while the Wi-Fi network which forms the SCADA communication channel is created using the Wi-Fi Router. In order to test the proposed SCADA system solution, the designed hardware was set up to remotely monitor the Photovoltaic (PV) voltage, current, and power, as well as the storage battery voltage of a 260 W, 12 V Solar PV System. Some of the created Human Machine Interfaces (HMIs) on Thinger.IO Server where an operator can remotely monitor the data in the cloud, as well as initiate supervisory control activities if the acquired data are not in the expected range, using both a computer connected to the network, and Thinger.IO Mobile Apps are presented in the paper.

https://www.mdpi.com/2079-9292/8/8/822/pdf

Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing

Advances in small and low power electronics have created new opportunities for the Internet of Things (IoT), leading to an explosion of physical objects being connected to the Internet. However, there still lacks an indoor localization solution that can answer the needs of various location-based IoT applications with desired simplicity, robustness, accuracy, and responsiveness. We introduce Foglight, a visible light enabled indoor localization system for IoT devices that relies on unique spatial encoding produced when mechanical mirrors inside a projector are flipped based on gray-coded binary images. Foglight employs simple off-the-shelf light sensors that can be easily coupled with existing IoT devices—such as thermometers, gas meters, or light switches—making their location discoverable. Our sensor unit is computation efficient; it can perform high-accuracy localization with minimum signal processing overhead, allowing any low-power IoT device on which it rests to be able to locate itself. Additionally, results from our evaluation reveal that Foglight can locate a target device with an average accuracy of 1.7 mm and average refresh rate of 84 Hz with minimal latency, 31.46 ms on Wi-Fi and 23.2 ms on serial communication. Two example applications are developed to demonstrate possible scenarios as proof of concept. We also discuss limitations, how they could be overcome, and propose next steps.

Foglight: Visible Light-Enabled Indoor Localization System for Low-Power IoT Devices

Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inertial measurement unit (IMU), Bluetooth, Wi-Fi, and ultra-wideband. The major problem with Bluetooth-based fingerprinting is the inconsistency of the radio signal strength, and the IMU-based localization has a drift error that increases with time. To compensate for these drawbacks, in the present study, a novel positioning system with IMU sensors and Bluetooth low energy (BLE) beacon for a smartphone are introduced. The proposed trusted K nearest Bayesian estimation (TKBE) integrates BLE beacon and pedestrian dead reckoning positionings. The BLE-based positioning, using both the K-nearest neighbor (KNN) and Bayesian estimation, increases the accuracy by 25% compared with the existing KNN-based positioning, and the proposed fuzzy logic-based Kalman filter increases the accuracy by an additional 15%. The overall performance of TKBE has an error of <;1 m in our experimental environments.

/pdf/trusted-k-nearest-bayesian-estimation-for-indoor-positioning-uyhxi070mh.pdf

Trusted K Nearest Bayesian Estimation for Indoor Positioning System

Microwave (MW) and millimeter-wave (mmW) propagation are severely affected by dust storms and sand storms in arid and semi-arid areas. Electromagnetic waves may suffer from attenuation due to suspended particles during a dust storm. This paper proposes an empirical model to predict the attenuation due to dust storms based on a one-year measurement of visibility, humidity and their effects on MW links in Sudan. Signal strength variations on two operational MW links at 14 and 22 GHz as well as visibility were monitored simultaneously. The model is developed empirically using measured attenuation and measured storm characteristics (e.g., visibility, dielectric constant, frequency, and moisture content). The predicted attenuation from the proposed empirical model is compared with the attenuation at frequencies ranging from 7.5 to 40 GHz measured at different locations, and good agreement is found. Additionally, this method is characterized by simplicity and capability to predict reliable dust storm attenuation for a wide range of frequencies and moisture levels.

/pdf/dust-storm-attenuation-modeling-based-on-measurements-in-5c5wjrk6ni.pdf

Dust Storm Attenuation Modeling Based on Measurements in Sudan

The empirical propagation path loss models have important influence in mobile communication systems design. They become important tools in both research and industrial communities owing to their speed of implementation and their limited dependence on detailed knowledge of the terrain. The mobile services are increasing rapidly and hence the mobile stations are required to design with best quality of service. Path loss models are considered frequently to estimate power budgets and cell coverages. Four empirical models namely, the Lee model, the Stanford University Interim (SUI) model, the COST-231 Hata model and Egli model have been investigated in this work. The measurements were carried out at University Putra Malaysia (UPM) campus and Gombak area. The measured data is used to validate the applicability of the four models mentioned previously in suburban environments. The results show that Egli model, COST-231 Hata model and SUI model over-predict the path loss in all coverage areas. The Lee model shows the best performance to the measured path loss.

Comparison of empirical propagation path loss models for mobile communications in the suburban area of Kuala Lumpur

Indoor path loss models are playing an important role in the design and planning of the 4th generation of the mobile networks. Moreover, it is an important component of system level simulators used to evaluate and test the network performance before it has been established. Many propagation models were proposed for this purpose. Most of these models are for macro and micro cellular networks. Small cell which is known as femtocell has been launched for future networks and it is wildly deployed by the mobile operators around the world. The available propagation models’ accuracy is at question when applied to femtocell design and engineering. This paper attempts to quantify the accuracy of these models by studying and comparing seven different propagation models for four different implementation scenarios at 2.6 GHz and for different separation distances.

/pdf/comparison-of-empirical-indoor-propagation-models-for-4g-151qqy0myz.pdf

Comparison of empirical indoor propagation models for 4G wireless networks at 2.6 GHz

The need of clean and renewable energy, as well as the power shortage in Gaza strip with few wind energy studies conducted in Palestine, provide the importance of this paper. Probability density function is commonly used to represent wind speed frequency distributions for the evaluation of wind energy potential in a specific area. This study shows the analysis of the climatology of the wind profile over the State of Palestine; the selections of the suitable probability density function decrease the wind power estimation error percentage. A selection of probability density function is used to model average daily wind speed data recorded at for 10 years in Gaza strip. Weibull probability distribution function has been estimated for Gaza based on average wind speed for 10 years. This assessment is done by analyzing wind data using Weibull probability function to find out the characteristics of wind energy conversion. The wind speed data measured from January 1996 to December 2005 in Gaza is used as a sample of actual data to this study. The main aim is to use the Weibull representative wind data for Gaza strip to show how statistical model for Gaza Strip over ten years. Weibull parameters determine by author depend on the pervious study using seven numerical methods, Weibull shape factor parameter is 1.7848, scale factor parameter is 4.3642 ms-1, average wind speed for Gaza strip based on 10 years actual data is 2.95 ms-1 per a day so the behavior of wind velocity based on probability density function show that we can produce energy in Gaza strip.

Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

Wireless Sensor Networks (WSN) are implemented in wide range of applications and one of these important applications is the localization and real-time tracking. The accuracy, cost, and power consumption are the most parameter for any proposed technique. Localization in indoor environment is more challenging than outdoor one, since the known Global Positioning System (GPS) is not working probably in indoor environment. In this paper a fingerprinting method for indoor localization is proposed. Real measurements were conducted using IRIS Zigbee motes in indoor environment. Three different configurations are tested where the measurements are conducted at 0, 1 and 1.6 m height. The three dimensional scenarios in the case of 1 m height show more accurate results than two dimensional one.

Alhareth Zyoud

Papers

Dust Storm Attenuation Modeling Based on Measurements in Sudan

Comparison of empirical propagation path loss models for mobile communications in the suburban area of Kuala Lumpur

Comparison of empirical indoor propagation models for 4G wireless networks at 2.6 GHz

Weibull Probability Distribution of Wind Speed for Gaza Strip for 10 Years

RSS Based Localization Techniques for ZigBee Wireless Sensor Network