Industrial Internet of Things (IIoT)

This course will introduce the students to the basics of Python programming. The goal of the course is to introduce students to basics of Python programming using hands-on experience. Students will learn how to install Python and use different IDEs (Integrated Development Environment) such as PyCharm for writing and debugging programs. The students will also learn to use Python and Jupyter platforms on Raspberry Pi. They will receive the tools necessary to create basic programs and resolve simple problems. Students will apply Python built-in data structures such as lists, dictionaries, and tuples to perform data analysis. Functions, classes and basic libraries of Python will also be covered in this course. After completing this course, students will be ready to take more advanced programming courses in Python.

This course provides the students a full overview of wired computer networking as it relates to IoT. They will have an opportunity to design, implement, and support a LAN network in an IoT setting. By the end of the course, students will be able to build simple LANs, perform configurations on routers and switches, and implement IP addressing schemes using both equipment and emulators.

In the course, students will be introduced to the components and specifics of OT networks commonly found in a manufacturing environment. Students will have the opportunity to set up and establish communication, as well as retrieve and log data from an industrial control unit.

This course will introduce students to the technical approach used for analyzing and designing an application by applying object-oriented programming. Students will develop the ability to select and use programming languages, desktop Integrated Development Environment (IDE) Software Development Kits (SDKs) and Application Programming Interfaces (APIs) for an IIoT environment. Students will also learn to program IoT devices using Arduino and Raspberry Pi platforms with the purpose to control the physical world (various sensors and actuators like, LED module, Motion Sensor, DHT22 Temperature and Humidity Sensor, Digital push button). The Raspberry Pi is typically installed with a Linux-based operating system, so the basics of Linux and its use will be introduced to the students as well as some of its main features including navigating the file system and managing processes. Students will be exposed to the text-based user interface through the shell and have an overview of the graphic user interface that is the default with the Raspian Linux distribution.

This course provides the students an opportunity to design and implement wireless technology, test wireless communications, optimize wireless transmission and support a wireless network in an IIoT setting in compliance with regulations.

In this class, students will learn how to integrate external devices (sensors, motors, GPS, orientation, LCD screens etc.) with the IIoT system to get an IoT device to interact with the real world. Students will use embedded systems, build hardware systems and connect devices to control the environment. They also learn methods to secure the IoT devices. Simple implementation of Industry 4.0 processes such as Digital Twin will also be covered in this course.

This course will provide students with the necessary skills to securely extract, transform and load IIoT Data and DataSets to IIoT oriented Databases in conformity with Industrial Data classification models and Security best practices. Students will explore relational and non-relational databases as well as how to use local or cloud-based IIoT Data ETL (Extract, Transfer & Load) solutions and platforms

This course will provide students with the skills required to use cloud computing in various modes (SaaS, PaaS, etc.) in an IIoT environment. This course describes how to connect, set up and use of cloud resources, e.g. EC2 (Elastic Compute Cloud) for IoT scenarios to enhance the performance of IoT solutions and to better analyze the IoT data. Different services such as telemetry and its relation to the IoT services will be covered in this course. Students will learn to locate IoT services in the cloud and acquire knowledge of the basic steps for setting it up. Moreover, in this course, students will learn to develop a simple IoT application to send and receive data to/from the cloud and to structure the IoT app with a variety of different cloud services. Briefly, this course will help students to integrate the embedded systems into the cloud.

This course provides students with an opportunity to transform IoT data to enhance the performance of industrial operations. This course will expose students to the data analytics practices executed in the Industrial IoT. They will move from the stages of problem identification to planning and applying a variety of data-driven solutions. They will learn to work with data and create an environment in which analytics can flourish. Students will learn how to manipulate and analyze IoT data and explore different use cases in IoT. For instance, they will learn to retrieve data from databases (Static, Real-time) using queries. The basics of Machine Learning for IoT data analysis will be covered. Students will also learn how to use common visualization tools. They will discover how data visualization can be used to better present the IoT data. They will explore the fundamental concepts of data visualization common interfaces and dashboards such as Power BI and Kibana, identifying and applying the various tools dashboards offer. By the end of the course, students will be able to prepare and import data into tables and explain the relationship between data analytics and data visualization.

Students will learn the steps involved in automating tasks based on predetermined conditions. They will also learn about Intelligent Process Automation (IPA) and common event-driven task automation approaches with a focus on applications in a variety of IIoT systems.

In the final capstone project completed in small groups of 2-3 students, students will apply the skills they have learned, by designing, building, controlling and testing in a controlled experimentation environment (IIoT testbed), as well as by collecting, storing, analyzing and visualizing the IIoT data. There will be an emphasis on ensuring that the final project is suitable as a showcase to future employers.

An internship experience provides the student with an opportunity to explore career interests while putting in practice knowledge and skills acquired in the program in a work setting. This course with help students to identify the practical issues of IIoT and provide a better platform to work with technological solutions. The internship provides hands-on training to effectively use and customize device interactions, networks, data management and analysis. Students will understand the essentiality of inter-connected devices and the benefits of task/process automation in industry. Students will get an overview of IoT application deployment in industry. The experience also helps students gain a clearer sense of future learning direction to adapt their knowledge and skills and provides an opportunity to build professional networks.