IoT Programming with C Training Course

This training aims to clarify what a 5G network is and its influence on smart technologies. We will highlight both the advantages and limitations of the relationship between 5G and IoT, while exploring the development paths of a network designed from the outset for the smart world.

6G represents the next evolution in wireless communication standards, poised to revolutionize IoT ecosystems by delivering ultra-fast connectivity, sophisticated sensing capabilities, and seamlessly integrated artificial intelligence.

This instructor-led live training, available either online or on-site, is designed for advanced professionals eager to explore and utilize the converging world of 6G technologies and IoT applications.

Upon completing this course, participants will be equipped to:

• Articulate the fundamental technical principles underlying 6G.
• Analyze how 6G will redefine IoT device communication and system architecture.
• Evaluate real-world 6G-enabled IoT use cases across various sectors.
• Develop strategies for incorporating 6G capabilities into current IoT solutions.

Course Format

• Concept-driven lectures complemented by expert-led discussions.
• Practical exercises tailored to reinforce core engineering concepts.
• Guided exploration of case studies and scenario analysis.

Customization Options

• For customized training aligned with your organization’s technology roadmap, please reach out to us to arrange.

Advancements in technology and the exponential growth of data are reshaping business operations across various sectors, including the public sector. Government data generation and digital archiving rates are accelerating due to the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, the management, processing, storage, security, and disposition of this data also grow more intricate. New capture, search, discovery, and analysis tools are enabling organizations to extract valuable insights from unstructured data. The government sector is reaching a tipping point, recognizing information as a strategic asset that must be protected, leveraged, and analyzed—both structured and unstructured—to better serve missions and meet requirements. As government leaders strive to evolve into data-driven organizations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from a combination of disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents an intelligent industry solution that enables government entities to make better decisions by acting on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these goals requires far more than simply accumulating massive quantities of data. As Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote on the OSTP Blog, "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information."

In 2012, the White House took a significant step toward helping agencies identify these technologies by establishing the National Big Data Research and Development Initiative. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the tools needed to analyze it.

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. Efficient data storage is one such challenge. With budgets always tight, agencies must minimize the per-megabyte cost of storage while keeping data easily accessible so users can retrieve it when and how they need it. Backing up massive quantities of data further heightens this challenge.

Effective data analysis presents another major hurdle. Many agencies use commercial tools to sift through vast amounts of data, identifying trends that help them operate more efficiently. A recent study by MeriTalk revealed that federal IT executives believe Big Data could help agencies save over $500 billion while also fulfilling mission objectives.

Custom-developed Big Data tools are also allowing agencies to address their analytical needs. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. This system has helped medical researchers find links that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

This instructor-led, live training session in Czech Republic (online or onsite) targets intermediate-level IT professionals and business managers who wish to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

By the end of this training, participants will be able to:

• Understand the fundamental principles of IoT and edge computing and their role in digital transformation.
• Identify relevant use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and their deployment contexts.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Czech Republic (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

By the end of this training, participants will be able to:

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in Czech Republic (online or on-site) is designed for product managers and developers who wish to use Edge Computing to decentralize data management for improved performance, leveraging smart devices located on the source network.

By the end of this training, participants will be able to:

• Understand the basic concepts and advantages of Edge Computing.
• Identify the use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

Embedded systems are specialized computing platforms engineered to execute specific tasks within larger frameworks. The Internet of Things (IoT) refers to a vast network of physical devices equipped with sensors and software, enabling them to connect, communicate, and exchange data via the internet.

This instructor-led live training, available either online or on-site, is designed for technical professionals at the beginner level who aim to grasp and implement embedded systems and IoT concepts using C programming and microcontroller architectures.

Upon completion of this training, participants will be equipped to:

• Comprehend the architecture and constituent elements of embedded systems.
• Write and compile C code to facilitate interaction with embedded hardware.
• Utilize microcontroller peripherals, including timers and Analog-to-Digital Converters (ADCs).
• Recognize the role embedded systems play within IoT architectures.

Course Format

• Engaging lectures paired with discussions.
• Ample opportunities for exercises and practical application.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For those seeking a customized training experience, please reach out to us to make the necessary arrangements.

This instructor-led, live training in Czech Republic (available online or onsite) is designed for intermediate-level professionals aiming to apply Federated Learning to optimize IoT and edge computing solutions.

Upon completion of this training, participants will be able to:

• Grasp the fundamental principles and advantages of Federated Learning in IoT and edge computing contexts.
• Deploy Federated Learning models on IoT devices to facilitate decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
• Overcome challenges associated with data privacy and network constraints in IoT systems.

The Internet of Things (IoT) represents a network infrastructure that wirelessly links physical objects with software applications, enabling seamless communication and data exchange through network protocols, cloud computing, and data capture capabilities. Java, a versatile general-purpose language renowned for its "write once, run anywhere" philosophy, is highly recommended for IoT development due to its superior portability and efficiency.

During this instructor-led live training, participants will acquire the skills necessary to develop IoT solutions using Java.

Upon completion of this course, participants will be capable of:

• Installing and configuring essential tools and frameworks, such as the Eclipse Open IoT Stack, to program IoT systems with Java
• Comprehending the core principles of IoT architecture
• Leveraging the Eclipse Open IoT Stack for Java to establish connections and manage devices within an IoT solution
• Constructing, testing, and deploying IoT systems utilizing Java

Audience

• Developers
• Engineers

Format of the course

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• For those interested in requesting customized training for this course, please contact us to make arrangements.

Connected devices are disrupting numerous industries, with the power utility sector being no exception. Power utility companies currently confront four primary challenges arising from the proliferation of the Internet of Things (IoT):

1. Vendors are increasingly connecting machines, controllers, HMIs, and SCADA systems to the cloud, promising enhanced analytics and insights for predictive and preventative maintenance. However, due to strict quarantine policies governing critical assets, power companies often cannot leverage these new IoT features provided by machine and controller vendors.
2. As the cost of solar and wind power microgrids continues to decline, utility companies face shrinking revenue from traditional power generation. To offset this loss, companies must aggressively pursue new revenue streams, such as Energy Management as a Service for homes, Energy Storage as a Service, grid services for EV charging, grid services for peer-to-peer (P2P) energy trading between homes, microgrids, and batteries, among others. Facilitating these transactions requires smart metering, smart grids, and secure transactions enabled by Distributed Ledger Technology (DLT) such as IOTA. Furthermore, utilities are exploring the provision of smart city services to municipal authorities.
3. For critical infrastructure like dams, ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM). This enables early warning of potential collapses in dams, rock faces, or tunnels, allowing time to evacuate affected populations.
4. An emerging revenue opportunity lies in EV charging within parking facilities—specifically, how IoT can facilitate smart charging and intelligent parking solutions.

Over the past three years, IoT engineering has undergone massive transformations, primarily driven by tech giants such as Microsoft, Google, and Amazon. These industry leaders have invested billions into developing IoT platforms that are easier to manage and more secure. IoT edge computing has gained significant momentum in both research and deployment as the primary method for practical IoT implementation. Additionally, 5G promises to revolutionize the IoT business landscape. Consequently, there is unprecedented funding for new areas of IoT research. For any practicing engineer, it is essential to understand the IoT platforms developed for major players like AWS, Google, and especially Microsoft.

However, none of these major platforms offer an exhaustive or fully comprehensive solution for scalable IoT. Deploying smart meters to millions of homes, for instance, requires additional technologies to secure the meters, radio networks, IoT management tools, and numerous other secured services. The strategy, cost, and security of any IoT deployment must be optimal and acceptable. Given the interdisciplinary nature of this knowledge, it is nearly impossible for any single company to assemble a team capable of meeting all requirements independently.

This course is a modest attempt to educate key decision-makers, developers, and security experts on the challenges, risks, and practical approaches to deploying IoT for next-generation power utility business models.

Furthermore, scalable deployment has made managing IoT services for thousands of sensors and connections an emerging engineering research subject. Formerly known as managed IoT services, this field is experiencing rapid growth because the challenges of scalable IoT management are significantly greater than simply building the systems. This includes securing over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, identifying the root cause of API failures, and tracking the hardware and service health of distributed systems.

Course objectives

The main objective of this course is to introduce emerging technological options, platforms, and case studies of IoT implementation in power utility companies, covering smart metering, smart cars, SHM (structural health monitoring), power quality diagnosis, and smart contracts. It provides a basic introduction to all elements of IoT, including mechanical components, electronic/sensor platforms, wireless and wired protocols, mobile-to-electronics integration, mobile-to-enterprise integration, data analytics, and control plane applications.

1. IoT Technology Stacks: Devices, Gateways, Edge, Edge Cloud, Public Cloud, IoT databases, Web & Mobile Applications for IoT, Centralized vs. Decentralized IoT
2. IoT ecosystem for business, third-party device management, and risk management of the entire IoT ecosystem
3. M2M Wireless protocols for IoT: WiFi, SigFox, LORA, LPWAN, Zigbee/Zwave, Bluetooth, ANT+ - Guidelines on when and where to use each
4. Fundamentals of IoT Gateways: Risks, Management, and Ecosystem
5. Mobile/Desktop/Web apps for registration, data acquisition, and control – Overview of available M2M data acquisition platforms for IoT: AWS IoT, Azure IoT, Google IoT
6. Security issues and solutions for IoT – Review of security across all technology stacks
7. Enterprise IoT platforms such as Microsoft Azure IoT Suites, AWS IoT, Google IoT, Siemens MindSphere
8. Smart Metering, Open Smart Grid Protocols (OSGP), ANSI C 2.18 Protocols, NIST Standard for HAN (Home Area Network), Home Plug Powerline Alliance, Security Standard for Smart Meter: IEC 62056
9. Distributed Ledger Technology (DLT) such as Blockchain, HyperLedger, and DAG (Directed Acyclic Graph) for smart contracts, P2P transactions, and smart car charging
10. IoT applications for critical infrastructure: Dams, Transformers, Sub-stations, High Tension Wires

This instructor-led, live training in Czech Republic (online or on-site) is aimed at developers and programmers who wish to install, configure, and manage the Kaa platform to build IoT applications.

By the end of this training, participants will be able to build, develop, manage, and implement IoT applications for smart devices and machines using Kaa.

This instructor-led, live training in Czech Republic (online or onsite) is aimed at advanced-level IoT developers and smart home enthusiasts who wish to automate IoT processes and create innovative solutions using n8n.

By the end of this training, participants will be able to:

• Set up and configure n8n for IoT workflow automation.
• Integrate IoT devices and platforms using n8n nodes and connectors.
• Implement custom workflows to automate IoT tasks and processes.
• Use IoT protocols like MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimize IoT automation workflows.

In this instructor-led live training in Czech Republic, participants will learn how to maximize the performance of Nginx while setting it up, configuring it, monitoring it, and troubleshooting it to handle various forms of HTTP and TCP traffic. Covered topics include how to configure the most important parameters in Nginx, the operating system, and a virtual machine to gain maximum value from Nginx.

The aim of the training is to explain what are - and what are not - Smart solutions (Internet of Things, AI, Blockchain, Virtual Reality, Metaverse) and to show the advantages and disadvantages of these technological worlds.

This instructor-led, live training in Czech Republic (online or onsite) targets intermediate-level IoT developers, embedded engineers, and AI practitioners aiming to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

Upon completion of this training, participants will be able to:

• Grasp the core concepts of TinyML and its role in IoT.
• Establish a TinyML development environment for IoT initiatives.
• Create and deploy machine learning models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to maximize power efficiency and minimize memory usage.