Internet of Things – Hands-On Development Using Systems Engineering
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This hands-on course will immerse the participant in the design and implementation of both hardware and software subsystems for the design of Internet-of-Things systems. Adriano and Raspberry Pi platforms will be provided. Participants need only a general knowledge of hardware and software systems to benefit from this course.
A system-engineering framework will be used to highlight the need for and use of a proven structured life-cycle approach in IOT product development. Requirements generation, design, testing, debugging, integration, manufacturing and supply chain challenges will be covered. Both commercial and DOD process variations will be highlighted.
A matrix of current life-cycle tools for both hardware-software IOT development and systems engineering will be provided. The importance and application of digital twins, simulation, emulation, prototyping and model-based design-verification will be emphasized. Data analysis, machine learning, edge and cloud-based computing/partitioning and the potential use of algorithm synthesis and artificial intelligence (AI) will be included. Finally, daily exercises and case studies will be used to tie all the concepts, methodologies and tool usage together.
At the end of this course, participants will have hands-on experience with both the hardware and software design of IOT systems. Additionally, participants will understand how the systems engineering approach can be used to manage the complexity of IOT projects as well as handle risk, perform powers-connectivity-data processing trade studies, incorporate reliability and security to deliver a prototype system. Material will also be present that focus on supply chain issues for full production systems.
Attendees will receive a copy of Mr. Blyler’s textbook, “Systems Engineering Management” – 5th Edition (Wiley and Sons, 2017). Attendees will also be provided with an IOT hardware-software development kit.
What You Will Learn:
- Why the intersection of Morse’s Law, Metcalf’s Communication Law and the Learning Curve predicts the future of the IoT.
- How to transform system objectives and service requirements into IoT and IIoT specifications and design elements.
- Conduct hardware-software trade-off studies to decrease risks during implementation.
- Develop and integrate actual IoT hardware-software systems using Arduino and Raspberry Pi platforms.
- Write and debug simple code for Arduino and Raspberry Pi.
- Selection of specific hardware and software tools associated with each phase of the IoT product lifecycle.
- Identify security issues and their impact on your design.
- IoT Hardware and Software Development: Basics, example systems, evaluating constraints, cost estimates, power sources, sensors and actuator fundamental, microcontrollers vs. microprocessor, hw-sw design tradeoffs, connectivity, bandwidth restrictions, networking basics and protocols, online design-networking software tools, basics of C and Phython, using IDEs, debugging embedded systems, learn to use Arduino and Rasberry Pi, understand embedded OSs like Rasbian Linux, WiFi and cellular connections, API interfaces, full system operation.
- Data Design: Incorporating legacy systems, data processing constraints in field-factory, edge/gateway processing vs. cloud, decision points, machine learning and AI overviews, understand privacy requirements, identify and resolve security issues.
- Connectivity: Wired and wireless systems, cellular, network basics, resiliency design, conducting trade-off studies.
- Applied Systems Engineering (tailored to IOT Development): System architecting and analysis, requirements engineering, hardware-software partitioning, system design and integration, hardware testing, software debugging, reliability-resiliency-failure analysis, risk management, prototyping vs. manufacturing, logistics, organizational structures.
- Commercial vs. Industrial vs. Defense IOT Development: process variations, available and emerging standards, global supply chain restrictions, IP theft, counterfeit components recognition tools
- Lifecycle Tools Overview: simulation, emulation, prototyping, QFD, N2, ACH, Extendsim, software IDE – scripts – languages – debugging, hardware and software integration platforms, and more. In-class exercises, integrated case studies, templates and online resources
John E. Blyler, BS (Engineering Physics), MS (EE), Affiliate and Founding Advisor of Systems Engineering at Portland State Univ., lecture and course developer for certificate IoT and Systems Engineering programs at UC-Irvine. Co-author of numerous textbooks for Wiley, Elsevier, IEEE and SAE. John is an experienced physicist, engineer, affiliate professor, author and writer who continues to speak at major conferences and before the camera. He has spent many years leading hardware-software integration teams in commercial, industrial, and DOD electronic semiconductor industries. Finally, he has served as editor-in-chief for a variety for technical semiconductor and embedded trade journal publications.
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