Nginx Training Course

This training aims to clarify what the 5G network is and its impact on smart technologies. We will examine both the advantages and disadvantages of the relationship between these technologies (5G / IoT) and explore the developmental directions of a network that has been dedicated to the smart world from its inception.

6G represents the subsequent generation of wireless communication standards, poised to revolutionize IoT ecosystems through ultra-high-speed connectivity, sophisticated sensing capabilities, and seamlessly integrated AI functions.

This instructor-led live training, available both online and onsite, is designed for advanced-level participants seeking to comprehend and capitalize on the emerging convergence of 6G technologies and IoT applications.

Upon completion of this course, learners will be equipped to:

• Articulate the fundamental technical principles underpinning 6G.
• Analyze how 6G will transform IoT device communication and structural frameworks.
• Evaluate 6G-enabled IoT use cases across various sectors.
• Develop strategies for incorporating 6G capabilities into current IoT solutions.

Course Format

• Concept-driven lectures alongside expert-led discussions.
• Practical exercises designed to reinforce core engineering principles.
• Guided exploration of case studies and scenario analysis.

Course Customization Options

• For customized versions of this training that align with your organization's technology roadmap, please contact us to arrange.

Technological advancements and the exponential growth of information are reshaping business operations across numerous sectors, including government. The generation of government data and the pace of digital archiving are accelerating, driven by the surge in mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, managing, processing, storing, securing, and disposing of this data grows increasingly difficult. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from their unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. Government bodies must protect, leverage, and analyze both structured and unstructured information to better serve the public and fulfill mission requirements. As government leaders work to transform into data-driven organizations to successfully achieve their missions, they are establishing the framework to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from a combination of the most 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—whether related or unrelated, structured or unstructured.

However, achieving this requires more than simply accumulating massive quantities of data. "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," wrote Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House moved to assist agencies in finding these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the tools required to analyze it.

The challenges posed by Big Data are nearly as formidable as its promise is encouraging. Efficiently storing data is one such challenge. Budgets are always tight, so agencies must minimize the cost per megabyte of storage while keeping data readily accessible so users can retrieve it when and how they need it. Backing up massive amounts of data further intensifies this challenge.

Effectively analyzing the data is another major hurdle. Many agencies utilize commercial tools that allow them to sift through vast amounts of data, identifying trends that improve operational efficiency. (A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save more than $500 billion while also meeting mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address their data analysis 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 identify 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 match job candidates with hiring managers.

This instructor-led, live training in Norway (online or onsite) targets intermediate-level IT professionals and business managers who want to understand how IoT and edge computing can foster efficiency, real-time processing, and innovation across different industries.

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

• Understand the core principles of IoT and edge computing and their part in digital transformation.
• Identify practical applications for IoT and edge computing in manufacturing, logistics, and energy.
• Differentiate between edge and cloud computing architectures and deployment contexts.
• Implement edge computing solutions to facilitate predictive maintenance and real-time decision-making.

This instructor-led, live training in Norway (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 Norway (online or onsite) is designed for product managers and developers seeking to decentralize data management for improved performance, by leveraging smart devices located on the source network.

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

• Grasp the fundamental concepts and benefits of Edge Computing.
• Recognize practical use cases and real-world examples of Edge Computing applications.
• Design and implement Edge Computing solutions to accelerate data processing and lower operational costs.

Embedded systems are specialized computing devices 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 and share data across the internet.

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

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

• Gaining insight into the architecture and components of embedded systems.
• Writing and compiling C code to facilitate hardware interaction.
• Utilizing microcontroller peripherals, including timers and ADCs.
• Comprehending the role of embedded systems within IoT architectures.

Course Format

• Engaging lectures and discussions.
• Extensive exercises and practical sessions.
• Practical implementation within a live-lab environment.

Options for Customizing the Course

• To arrange a tailored training session for this course, please get in touch with us.

This instructor-led, live training in Norway (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

The Internet of Things (IoT) constitutes a network infrastructure that wirelessly links physical objects with software applications. This connectivity enables these entities to communicate and exchange data through network communications, cloud computing, and data capture mechanisms. C is a general-purpose programming language widely recommended for IoT development due to its pervasive presence and advantages in low-level programming.

In this instructor-led live training, participants will acquire the skills necessary to program IoT solutions using C.

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

• Install and configure NetBeans for developing IoT systems with C
• Grasp the fundamentals of IoT architecture
• Appreciate the benefits of utilizing C in IoT programming
• Build, test, deploy, and troubleshoot an IoT system using C

Audience

• Developers
• Engineers

Format of the course

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

Note

• To request a customized training for this course, please contact us to arrange.

The Internet of Things (IoT) constitutes a network infrastructure that wirelessly links physical objects with software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. Java, a general-purpose language renowned for its "write once, run anywhere" capability, is highly recommended for IoT development due to its portability and efficiency.

In this instructor-led live training, participants will learn how to develop IoT solutions using Java.

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

• Install and configure tools and frameworks, such as the Eclipse Open IoT Stack, for programming IoT systems with Java
• Gain a solid understanding of IoT architecture fundamentals
• Utilize the Eclipse Open IoT Stack for Java to connect and manage devices within an IoT solution
• Construct, test, and deploy an IoT system using Java

Audience

• Developers
• Engineers

Format of the course

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

Note

• For customized training options for this course, please contact us to arrange a session.

Connected devices are disrupting numerous industries, with the power utility sector being no exception. Power utility companies currently face four primary challenges arising from the growth of 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, strict quarantine policies governing critical assets prevent power companies from utilizing these new IoT features offered by machine and controller vendors.
2. As the costs of solar and wind power microgrids continue to decrease, utility companies will soon experience declining revenue from power generation. To compensate for lost power production revenue, companies must aggressively pursue new revenue streams, such as home energy management as a service, energy storage as a service, grid services for EV charging, and grid services for peer-to-peer (P2P) energy trading between homes, microgrids, and batteries. These initiatives require facilitation through smart metering, smart grids, and secure transactions enabled by Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring the provision of smart city services to municipal authorities.
3. For critical infrastructure such as dams, ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM) to identify impending collapse risks in dams, rocks, or tunnels, allowing for the timely evacuation of affected populations.
4. A new emerging revenue area is EV charging in parking facilities. IoT can play a pivotal role in facilitating smart charging and smart parking solutions.

Over the past three years, IoT engineering has undergone massive changes, primarily driven by Microsoft, Google, and Amazon. These tech giants have invested billions to develop IoT platforms that are easier to manage and secure. Furthermore, IoT edge computing has gained significant momentum as the primary means for practical IoT implementation. With 5G promising to transform the IoT business landscape, there has been an unprecedented surge in research funding for new IoT areas. Consequently, it is essential for practicing engineers to understand the IoT platforms developed by major players like AWS, Google, and especially Microsoft.

However, none of these platforms offer an exhaustive or comprehensive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies for securing the meters, radio networks, IoT management systems, and numerous other secured services. The strategy, pricing, and security of any IoT deployment must be optimal and acceptable. Given the interdisciplinary nature of this knowledge, it is challenging for any company to assemble a team capable of meeting all requirements.

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 businesses.

Additionally, as deployment scales, managing IoT services for thousands of sensors and connections is emerging as a separate engineering research subject. This area, formally known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT are significantly greater than the initial build. 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 hardware and service health in distributed systems.

Course objectives

The main objective of the course is to introduce emerging technological options, platforms, and case studies of IoT implementation in power utility companies, including Smart Metering, Smart Cars, SHM (Structural Health Monitoring), Power Quality Diagnosis, and Smart Contracts. It also provides a basic introduction to all IoT elements: mechanical components, electronics/sensor platforms, wireless and wireline 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+ - 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 - available M2M data acquisition platforms for IoT: AWS IoT, Azure IoT, Google IoT.
6. Security issues and solutions for IoT - reviewing the security of all technology stacks.
7. Enterprise IoT platforms such as Microsoft Azure IoT suites, AWS IoT, Google IoT, and 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, and 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 for critical infrastructure like dams, transformers, substations, and high-tension wires.

This instructor-led live training in Norway (online or onsite) targets 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 Norway (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.

The objective of the training is to explain what constitutes intelligent solutions (Internet of Things, AI, Blockchain, Virtual Reality, Metaverse) versus what does not, and to illustrate the advantages and disadvantages of these technological spheres.

This instructor-led, live training in Norway (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.