Nginx Training Course

This training aims to clarify what 5G networks are and their influence on smart technologies. We will explore both the advantages and disadvantages of the relationship between these technologies (5G/IoT), as well as the developmental trajectories of networks originally designed for the smart world.

Positioned to revolutionize IoT ecosystems via ultra-fast connectivity, advanced sensing, and integrated AI capabilities, 6G represents the next-generation wireless communication standard.

Designed for advanced-level participants eager to understand and harness the emerging intersection of 6G technologies and IoT applications, this instructor-led training is available both online and onsite.

Upon completing this course, learners will be able to:

• Articulate the core technical concepts underlying 6G.
• Analyze how 6G will transform IoT device communication and architecture.
• Assess 6G-enabled IoT use cases across various industries.
• Develop strategies for integrating 6G capabilities into existing IoT solutions.

Course Format

• Concept-focused lectures paired with expert discussions.
• Practical exercises designed to reinforce key engineering principles.
• Guided exploration of cases and scenario analysis.

Customization Options

• For customized training aligned with your organization's technology roadmap, please contact us to arrange.

Dive deeper into the innovative realm of edge computing with this advanced course. Explore sophisticated architectures and tackle integration challenges, preparing to leverage the full potential of edge computing across diverse business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific industry needs.

Technological advancements and the exponential growth of information are fundamentally transforming business operations across various sectors, including government. The volume of government data and digital archiving is increasing rapidly, driven by the proliferation of mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, the management, processing, storage, security, and disposition of this data become increasingly challenging. New tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical tipping point, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to better serve the public and meet mission requirements. As government leaders work to evolve into data-driven organizations, they are establishing the foundation to correlate dependencies across events, people, processes, and information.

High-impact government solutions will emerge from the integration of the most disruptive technologies:

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

Big Data represents a key intelligent industry solution, enabling governments to make better decisions by acting on patterns revealed through the analysis of large volumes of related and unrelated, structured and unstructured data.

However, achieving these outcomes requires more than just accumulating massive amounts 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 from 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 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 necessary analytical tools.

The challenges posed by Big Data are nearly as formidable as the promise it offers. One major challenge is efficient data storage. With budgets often tight, agencies must minimize storage costs per megabyte while ensuring data remains easily accessible for users to retrieve when and how they need it. Backing up such massive data volumes adds another layer of complexity.

Effective data analysis presents another significant challenge. Many agencies utilize commercial tools to sift through vast amounts of data, 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 fulfilling mission objectives.

Custom-developed Big Data tools are also empowering agencies to meet their analytical 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 assisted medical researchers in identifying links that alert doctors to aortic aneurysms before they occur. It is also employed for more routine tasks, such as screening resumes to match job candidates with hiring managers.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for intermediate 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.

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

• Understand the core principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in the 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.

In this comprehensive course, combine the transformative potential of AI with the agility of edge computing. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills needed to optimise AI performance for real-time processing and decision-making at the edge.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (available online or on-site) is designed for product managers and developers who aim to leverage Edge Computing to decentralize data management, thereby achieving faster performance through the use of smart devices deployed directly on the source network.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts and key advantages of Edge Computing.
• Recognize practical use cases and real-world examples where Edge Computing can be effectively applied.
• Design and implement Edge Computing solutions that enable faster data processing while reducing operational costs.

Build a strong foundation in designing and managing a resilient edge computing infrastructure. Learn about open hybrid cloud infrastructures, managing workloads across diverse clouds, and ensuring flexibility and redundancy. This training provides essential knowledge on creating a scalable and secure infrastructure that supports the dynamic needs of modern applications with edge computing.

Embedded systems are specialized computing platforms engineered to execute specific tasks within broader systems. IoT (Internet of Things) 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 onsite) is designed for beginner-level technical professionals seeking to understand and apply embedded systems and IoT concepts using C and microcontroller architectures.

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

• Grasp the architecture and components of embedded systems.
• Write and compile C code for interacting with embedded hardware.
• Utilize microcontroller peripherals such as timers and ADCs.
• Understand the role of embedded systems within IoT architectures.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for intermediate-level professionals seeking to leverage Federated Learning to optimize IoT and edge computing solutions.

By the conclusion 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.
• Tackle challenges related to data privacy and network constraints within IoT systems.

The Internet of Things (IoT) refers to 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 technologies. C is a versatile programming language widely recommended for IoT development due to its widespread adoption and advantages in low-level programming.

Through this instructor-led live training, participants will gain the skills necessary to develop IoT solutions using C.

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

• Install and configure NetBeans to facilitate IoT programming with C
• Grasp the core principles of IoT architecture
• Recognize the advantages of utilizing C for IoT system development
• Construct, test, deploy, and troubleshoot IoT systems using C

Target 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.

This hands-on, practical course offers a thorough introduction to the Internet of Things (IoT), spanning from device-level programming to cloud-based data processing and visualization. Participants will navigate the complete IoT architecture—including sensors, communication protocols, microcontrollers, and cloud integration—through guided exercises utilizing development boards such as ESP32 and Raspberry Pi. By the conclusion of the course, learners will be equipped to construct an end-to-end IoT pipeline: capturing sensor data, transmitting it via MQTT/HTTP, processing it on cloud platforms like Azure IoT Hub, AWS IoT Core, or Google Cloud IoT, and visualizing the results using tools such as Grafana or Power BI. The curriculum also addresses security best practices and simulated cyber threats to guarantee robust and secure deployments.

The Internet of Things (IoT) represents a network infrastructure that wirelessly links physical objects and 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.

Through 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 tools and frameworks, such as the Eclipse Open IoT Stack, to program 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.
• Construct, test, and deploy IoT systems using Java.

Audience

• Developers
• Engineers

Format of the course

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

Note

• For inquiries regarding customized training for this course, please contact us to make arrangements.

Connected devices are disrupting many industries, and the power utility sector is no exception. Power utility companies currently face four key challenges stemming from the growth of the Internet of Things (IoT):

1. Machines, controllers, HMI, and SCADA systems are increasingly being connected to the cloud by vendors promising enhanced analytics and insights for predictive and preventative maintenance. However, quarantine policies surrounding critical assets mean that power companies cannot utilize these new IoT features offered by machine and controller vendors.
2. With the continuously decreasing cost of solar and wind power microgrids, utility companies will soon experience declining revenue from power generation. To compensate for lost 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, peer-to-peer (P2P) energy trading between homes, microgrid-to-microgrid exchanges, microgrid-to-battery interactions, and home-to-battery solutions. All of this must be enabled through smart metering, smart grids, and secure transactions, which are only possible via Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring the provision of smart city services to local authorities.
3. For critical infrastructure such as dams, the International Committee of Large Dams (ICOLD) requires real-time Structural Health Monitoring (SHM). This ensures that any impending danger of dam, rock, or tunnel collapse can be detected in advance, allowing affected populations to be evacuated promptly.
4. A new emerging revenue area is EV charging in parking facilities. How can IoT 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 industry giants have invested billions of dollars to develop more manageable and secure IoT platforms. Furthermore, IoT Edge has gained significant momentum in both research and deployment, becoming the only viable path 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 absolutely essential for any practicing engineer to understand the IoT platforms developed by major players like AWS, Google, and especially Microsoft.

However, none of the aforementioned platforms offer a fully exhaustive or comprehensive solution for scalable IoT. For instance, deploying smart metering to millions of homes requires additional technologies to secure the meters, radio networks, IoT management solutions, and various other secure services. The strategy, pricing, 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 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 methods for deploying IoT in next-generation power utility businesses.

In addition, as deployments scale, managing IoT services for thousands of sensors and connections has emerged as a distinct field of engineering research. Known formally as Managed IoT Services, this area is experiencing rapid growth because the challenges of scaling IoT are far greater than simply building it. This includes securing over-the-top firmware and software updates, managing sensor and system calibration, auto-diagnosing connection issues, pinpointing the root causes of API failures, and tracking the hardware and service health of distributed systems.

Course objectives

The main objective of the course is to introduce emerging technological options, platforms, and case studies regarding IoT implementation in power utility companies, covering Smart Metering, Smart Cars, Structural Health Monitoring (SHM), Power Quality Diagnosis, and Smart Contracts. It provides a basic introduction to all elements of IoT, including mechanical and 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, and Centralized vs. Decentralized IoT.
2. IoT Ecosystem for Business: Third-party device management and risk management across the entire IoT ecosystem.
3. M2M Wireless Protocols for IoT: WiFi, SigFox, LORA, LPWAN, Zigbee/Zwave, Bluetooth, ANT+ – Guidance on when and where to use each protocol.
4. Fundamentals of IoT Gateways: Risks, management, and ecosystem considerations.
5. Mobile/Desktop/Web Apps for registration, data acquisition, and control – Overview of available M2M data acquisition platforms for IoT such as AWS IoT, Azure IoT, and Google IoT.
6. Security Issues and Solutions for IoT: A comprehensive review of security across all technology stacks.
7. Enterprise IoT Platforms: Including 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 Standards for HAN (Home Area Network), HomePlug Powerline Alliance, and the IEC 62056 Security Standard for Smart Meters.
9. Distributed Ledger Technology (DLT): Including Blockchain, HyperLedger, and DAG (Directed Acyclic Graph) for smart contracts, P2P transactions, and smart car charging.
10. IoT for Critical Infrastructure: Applications for dams, transformers, substations, and high-tension wires.