Nginx Training Course

This training aims to clarify what the 5G network is and its influence on smart technologies. It explores both the benefits and drawbacks of the 5G/IoT technological synergy, while highlighting the development trajectory of a network designed from the outset for the smart ecosystem.

6G represents the forthcoming generation of wireless communication standards, poised to revolutionize IoT ecosystems by delivering ultra-high-speed connectivity, sophisticated sensing capabilities, and deeply integrated artificial intelligence.

This instructor-led live training, available both online and on-site, targets advanced participants eager to comprehend and utilize the emerging convergence of 6G technologies and IoT applications.

Upon completing this course, learners will be equipped to:

• Articulate the fundamental technical principles underpinning 6G.
• Analyze how 6G will transform the communication protocols and architectural design of IoT devices.
• Evaluate 6G-enabled IoT use cases across various industry sectors.
• 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 core engineering concepts.
• Guided exploration of case studies and scenario analysis.

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 fundamentally changing how businesses operate across various sectors, including government. The volume of government data generation and digital archiving is accelerating due to the widespread adoption of mobile devices and applications, smart sensors, cloud computing solutions, and public-facing digital portals. As digital information becomes more voluminous and complex, managing, processing, storing, securing, and disposing of this data presents greater challenges. Emerging tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from unstructured information. The government sector is at a critical juncture, recognizing that information is a strategic asset. Governments must protect, leverage, and analyze both structured and unstructured data to better fulfill their missions and serve the public. As government leaders work to transform their organizations into data-driven entities, they are establishing the foundation to connect dependencies across events, personnel, processes, and information.

High-impact government solutions will emerge from the integration of several transformative 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 agencies to make better decisions by acting on patterns identified through the analysis of vast amounts of data—both related and unrelated, structured and unstructured.

However, achieving this requires more than just accumulating large volumes 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 took a significant step toward helping agencies identify these technologies by launching 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 necessary to analyze it.

The challenges posed by Big Data are nearly as formidable as its promise is promising. One such challenge is storing data efficiently. With budgets remaining tight, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible so users can retrieve it efficiently when and how they need it. Backing up massive amounts of data further complicates this challenge.

Effective data analysis is another major hurdle. Many agencies utilize commercial tools to sift through vast data sets, identifying trends that enhance operational efficiency. (A recent MeriTalk study revealed that federal IT executives believe Big Data could help agencies save over $500 billion while also meeting mission objectives.)

Custom-developed Big Data tools are also enabling agencies to meet their analysis needs. For instance, the Computational Data Analytics Group at Oak Ridge National Laboratory 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 routine tasks, such as matching job candidates with hiring managers by sifting through resumes.

This instructor-led, live training in Mexico (online or onsite) is tailored for intermediate IT professionals and business managers who want to explore the potential of IoT and edge computing to enhance efficiency, enable real-time processing, and foster innovation in various industries.

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

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

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

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

• Grasp the fundamental concepts and benefits of Edge Computing.
• Identify practical use cases and examples where Edge Computing can be effectively applied.
• Design and implement Edge Computing solutions to accelerate data processing and minimize operational costs.

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

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

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

• Comprehending the architecture and components of embedded systems.
• Writing and compiling C code to interact with embedded hardware.
• Utilizing microcontroller peripherals, such as timers and ADCs.
• Understanding the role of embedded systems within IoT architectures.

Course Format

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

Customization Options for the Course

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

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

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

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

The Internet of Things (IoT) constitutes a network infrastructure that wirelessly links physical devices with software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. C is a general-purpose programming language recommended for IoT development due to its widespread availability and low-level programming advantages.

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

Upon completing this training, participants will be able to:

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

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 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 technologies. Java, a versatile programming language celebrated 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 gain the skills necessary to program IoT solutions using Java.

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

• Install and configure the necessary tools and frameworks, including the Eclipse Open IoT Stack, for developing IoT systems with Java
• Grasp the fundamental concepts 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 a customized training session for this course, please contact us to arrange.

Connected devices are disrupting numerous industries, and the power utility sector is no exception. Power utility companies currently face four primary challenges driven by the growth of the Internet of Things (IoT):

1. Machines, controllers, HMIs, and SCADA systems are increasingly being connected to the cloud by vendors who promise enhanced analytics and insights for predictive and preventive maintenance. However, the strict quarantine policies governing critical assets prevent power companies from fully utilizing these new IoT features provided by machine and controller vendors.
2. With the continuously decreasing costs of solar and wind power microgrids, utility companies will soon experience declining revenue from traditional power generation. To compensate for this lost revenue, companies must aggressively pursue new revenue streams, such as Energy Management as a Service, Energy Storage as a Service, offering grid services for EV charging, and facilitating peer-to-peer (P2P) energy trading between homes, homes and microgrids, microgrids and batteries, and homes and batteries. All these initiatives require facilitation through smart metering, smart grids, and secure transactions enabled by Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring ways to offer certain 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 provide advanced warning of potential collapses of dams, rock formations, or tunnels, allowing sufficient time to evacuate potentially affected populations.
4. Another emerging revenue area is EV charging in parking facilities. This module explores how IoT can facilitate 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 of dollars to develop IoT platforms that are easier to manage and secure. Furthermore, IoT edge computing has gained significant momentum in both research and deployment as the only viable means for practical IoT implementation. 5G promises to transform the IoT business landscape, leading to unprecedented levels of research funding in this area. Consequently, for any practicing engineer, it is absolutely essential to understand the IoT platforms developed for major players like AWS, Google, and especially Microsoft.

However, none of the aforementioned platforms offer an exhaustive or entirely comprehensive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies to secure the smart meters, radio networks, IoT management technology, and many other secured services. The strategy, price, and security of any IoT deployment must be optimal and acceptable. Given the vast amount of interdisciplinary knowledge required, it is nearly impossible for any company to assemble a single 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, with scalable deployments, managing IoT services for thousands of sensors and connections is emerging as a separate engineering discipline. This area, formerly known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT are much greater than building them. This includes security for over-the-air firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, narrowing down the root cause of API failures, and tracking the hardware and service health of distributed systems, among others.

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: Mechanical, 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 one
4. Fundamentals of IoT Gateways: Risks, Management, and Ecosystem
5. Mobile/Desktop/Web apps for registration, data acquisition, and control – Review of available M2M data acquisition platforms for IoT such as AWS IoT, Azure IoT, and Google IoT
6. Security issues and solutions for IoT: Review of 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 applications for critical infrastructure like DAMs, Transformers, Sub-stations, and High Tension Wires

This instructor-led live training in Mexico (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 Mexico (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 this training is to define what constitutes Smart Solutions (such as the Internet of Things, AI, Blockchain, Virtual Reality, and the Metaverse) versus what does not, and to illustrate the advantages and disadvantages associated with these technological domains.

This instructor-led, live training in Mexico (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.