Nginx Training Course

This training aims to explain what the 5G network is and its impact on smart technologies. It will highlight both the advantages and disadvantages of the relationship between these technologies (5G/IoT) and outline the development directions of the network, which has been dedicated to the smart world from the outset.

6G represents the upcoming generation of wireless communication standards, poised to revolutionize IoT ecosystems with ultra-high-speed connectivity, sophisticated sensing capabilities, and seamless AI integration.

This instructor-led live training (available online or onsite) targets advanced participants seeking to understand and capitalize on the convergence of 6G technologies and IoT applications.

Upon completing this course, learners will be able to:

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

Course Format

• Concept-driven lectures paired with expert-led discussions.
• Practical exercises designed to reinforce key engineering principles.
• Guided case studies and scenario analysis.

Customization Options

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

Advances in technology and the escalating volume of information are reshaping business operations across numerous industries, including the public sector. The rate at which governments generate data and archive digital records is accelerating, driven by the rapid proliferation of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and grows more complex, the management, processing, storage, security, and disposition of that data become increasingly intricate. New tools for capture, search, discovery, and analysis are enabling organizations to derive insights from unstructured data. The government sector is reaching a critical juncture, recognizing that information is a strategic asset. To better serve the public and meet mission requirements, governments must protect, leverage, and analyze both structured and unstructured information. As government leaders strive to evolve into data-driven organizations capable of successfully accomplishing their missions, they are laying the groundwork 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 taking action based on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these outcomes requires far 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," noted 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 took a significant step toward assisting agencies in identifying these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative included more than $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 daunting as its promise is encouraging. Efficient data storage is one such challenge. As always, budgets are tight, so agencies must minimize the per-megabyte cost of storage and keep data easily accessible, ensuring users can retrieve it when and how they need it. Backing up massive quantities of data further heightens this challenge.

Effectively analyzing data is another major hurdle. Many agencies utilize commercial tools that enable them to sift through mountains of data, identifying trends that help them operate more efficiently. (A recent study by MeriTalk found 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 the need to analyze their data. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. 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 in Brazil (online or onsite) is designed for 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:

• Grasp the core principles of IoT and edge computing and their significance in digital transformation.
• Recognize key use cases for IoT and edge computing within manufacturing, logistics, and energy sectors.
• Distinguish between edge and cloud computing architectures and their respective deployment scenarios.
• Deploy edge computing solutions for predictive maintenance and real-time decision-making.

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

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

• Gaining a solid understanding of the fundamental concepts and benefits of Edge Computing.
• Identifying practical use cases and examples where Edge Computing is applicable.
• Designing and implementing Edge Computing solutions to accelerate data processing and lower operational expenses.

Embedded systems are specialized computing devices created to execute specific tasks within larger operational frameworks. The Internet of Things (IoT) refers to a network of physical devices equipped with sensors and software, enabling them to communicate and share data over the internet.

This instructor-led live training, available online or on-site, targets technical professionals at the beginner level who want to grasp and apply the concepts of embedded systems and IoT using C programming and microcontroller architectures.

Upon completing this training, participants will be able to:

• Comprehend the architecture and components 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).
• Understand the role of embedded systems within IoT architectures.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options for the Course

• For tailored training arrangements, please contact us.

This instructor-led, live training in Brazil (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) refers to 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. As a general-purpose programming language, C is highly recommended for IoT development due to its widespread use and advantages in low-level programming.

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

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

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

Audience

• Developers
• Engineers

Course Format

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

Note

• To request customized training for this course, please contact us to make arrangements.

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

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

Upon completing this training, participants will be able to:

• Install and configure tools and frameworks (such as the Eclipse Open IoT Stack) for programming IoT systems in Java
• Grasp the fundamental principles of IoT architecture
• Utilize the Eclipse Open IoT Stack for Java to connect and manage devices within an IoT solution
• Build, 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

• To request 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 face four primary challenges stemming from the growth 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, strict quarantine policies regarding critical assets prevent power companies from fully utilizing these new IoT features provided 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 traditional power generation. To compensate for this loss, 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 services 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 on Large Dams) mandates real-time Structural Health Monitoring (SHM) to provide advance warning of potential collapses in dams, rock faces, or tunnels, allowing for the evacuation of affected populations.
4. A new emerging revenue area is EV charging in parking facilities. This module explores how IoT can facilitate smart charging and intelligent parking solutions.

Over the last three years, IoT engineering has undergone massive changes, primarily driven by Microsoft, Google, and Amazon. These tech giants have invested billions of dollars to develop IoT platforms that are easier to manage and more secure. Furthermore, IoT edge computing has gained significant momentum in both research and deployment as the primary means for practical IoT implementation. 5G promises to further transform the IoT business landscape, leading to unprecedented levels of research funding in this area. Consequently, it is absolutely essential for practicing engineers to understand the IoT platforms developed by major players such as AWS, Google, and especially Microsoft.

However, none of the aforementioned platforms offer an exhaustive or completely comprehensive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies to secure the meters, establish radio networks, manage IoT systems, and provide other secured services. Strategy, pricing, and security for any IoT deployment must be optimal and acceptable. Given the interdisciplinary knowledge required, it is nearly impossible for a single company to assemble a team capable of meeting all these requirements.

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

In addition, as deployments scale, managing IoT services for thousands of sensors and connections is emerging as a separate engineering subject of research. Known formally as managed IoT services, this area is experiencing rapid growth because the challenges of scalable IoT are significantly greater than simply building the infrastructure. This includes securing over-the-top firmware and software updates, managing sensor and system calibration, auto-diagnosing connection issues, identifying the root cause of API failures, and tracking the health of hardware and services within 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 provides a basic introduction to all elements of IoT, including 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+: Understanding when and where to use each one.
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: A review of security across 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 C2.18 Protocols, NIST Standard for HAN (Home Area Network), Home Plug Powerline Alliance, and Smart Meter Security Standard IEC 62056.
9. Distributed Ledger Technology (DLT) such as Blockchain, HyperLedger, and DAG (Direct Acyclic Graph) for smart contracts, P2P transactions, and smart car charging.
10. IoT applications for critical infrastructure like dams, transformers, substations, and high-tension wires.

This instructor-led live training in Brazil (online or onsite) 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 Brazil (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 clarify what constitutes Smart Solutions (Internet of Things, AI, Blockchain, Virtual Reality, Metaverse) and what does not, while highlighting the pros and cons of these technological domains.

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

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

• Grasp the fundamentals of TinyML and its role in IoT.
• Configure a TinyML development environment for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to ensure efficient power and memory usage.