Nginx Training Course

This training aims to clarify the nature of 5G networks and their influence on smart technologies. We will examine both the benefits and drawbacks of the synergy between 5G and IoT, while also outlining the developmental trajectory of a network designed from the outset for the smart ecosystem.

6G represents the subsequent generation of wireless communication standards, poised to revolutionise IoT ecosystems through ultra-fast connectivity, advanced sensing, and integrated AI capabilities.

This instructor-led, live training (available online or onsite) is designed for advanced-level participants who wish to understand and leverage the emerging intersection of 6G technologies and IoT applications.

By completing this course, learners will gain the ability to:

• Explain the core technical concepts behind 6G.
• Assess how 6G will reshape IoT device communication and architecture.
• Evaluate 6G-enabled IoT use cases across industries.
• Prepare strategies for integrating 6G capabilities into existing IoT solutions.

Format of the Course

• Concept-focused lectures combined with expert discussion.
• Applied exercises designed to reinforce key engineering principles.
• Case-based exploration and scenario analysis in a guided environment.

Course Customization Options

• For tailored versions of this training aligned with your organizational technology roadmap, please contact us to arrange.

Delve 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 in a variety of 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 numerous industries, including the public sector. Government data generation and digital archiving are accelerating, driven by the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes increasingly complex, the tasks of managing, processing, storing, securing, and disposing of that data also become more intricate. New tools for capture, search, discovery, and analysis 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. Governments must now protect, leverage, and analyze both structured and unstructured data to better serve citizens and meet mission requirements. As government leaders strive to evolve into data-driven organizations to successfully achieve their missions, they are establishing the foundation to correlate dependencies across events, people, processes, and information.

High-impact 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 a transformative industry solution, allowing governments to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these capabilities requires more than just 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," Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote 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 tools necessary to analyze it.

The challenges posed by Big Data are nearly as daunting as the opportunities are encouraging. Efficient data storage is one such challenge. With budgets always tight, agencies must minimize the per-megabyte cost of storage while ensuring data remains easily accessible so users can retrieve it whenever and however they need it. Backing up massive amounts of data further intensifies this challenge.

Effective data analysis is another major hurdle. Many agencies utilize commercial tools that enable them to sift through mountains of data, identifying trends that can enhance operational efficiency. (A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address their analytical needs. For example, 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 Greece (online or onsite) is designed for intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and 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.

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. 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 to optimize AI performance for real-time processing and decision-making at the edge.

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

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

• Grasp the fundamental concepts and benefits of Edge Computing.
• Recognize use cases and examples suitable for Edge Computing implementation.
• Design and construct Edge Computing solutions to accelerate data processing and lower operational expenses.

Establish a solid foundation in designing and managing a resilient edge computing infrastructure. Gain insights into open hybrid cloud systems, workload management across diverse clouds, and the importance of flexibility and redundancy. This course provides essential knowledge for building scalable and secure infrastructure that supports the dynamic needs of modern applications through edge computing.

Embedded systems are specialized computing platforms engineered to execute specific tasks within broader operational frameworks. The Internet of Things (IoT) represents a vast network of physical devices equipped with sensors and software, enabling them to communicate and share data across the internet.

This instructor-led live training, available both online and onsite, 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 parts of embedded systems.
• Draft and compile C code to facilitate hardware interaction.
• Utilize microcontroller peripherals, such as timers and ADCs.
• Grasp the role embedded systems play within IoT architectures.

Course Format

• Engaging lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory environment.

Customization Options

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

This instructor-led, live training in Greece (available online or on-site) 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 principles and advantages of Federated Learning in IoT and edge computing contexts.
• 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.
• Overcome challenges related to data privacy and network limitations in IoT systems.

The Internet of Things (IoT) forms 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. C, a general-purpose programming language, is widely recommended for IoT development due to its ubiquity and low-level programming advantages.

In this instructor-led live training, participants will learn how to develop 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
• Identify the benefits of utilizing C for IoT system programming
• Build, test, deploy, and troubleshoot an IoT system using C

Audience

• Developers
• Engineers

Course Format

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

Note

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

The Internet of Things (IoT) constitutes a network infrastructure that wirelessly connects physical objects with software applications, enabling them to communicate, exchange data via network communications, and leverage cloud computing alongside 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 acquire 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) 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.
• Build, test, and deploy an IoT system using Java.

Audience

• Developers
• Engineers

Format of the course

• Combination of lectures, discussions, exercises, and intensive hands-on practice.

Note

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

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. Machines, controllers, Human-Machine Interfaces (HMI), and SCADA systems are increasingly being cloud-connected by vendors who promise enhanced analytics and insights via their data for predictive and preventative maintenance. However, strict quarantine policies regarding critical assets prevent power companies from utilizing these new IoT features offered by machine and controller vendors.
2. As the cost of solar and wind power microgrids continues to drop, utility companies will soon experience declining revenue from power generation. To offset this lost revenue, companies must aggressively pursue new revenue streams, such as Home Energy Management as a Service, Energy Storage as a Service, offering grid services for EV charging, and grid services for P2P energy trading between homes, between homes and microgrids, between microgrids, from microgrids to batteries, and from homes to batteries. All of these services require facilitation through smart metering, smart grids, and smart, secured transactions, achievable only via Distributed Ledger Technology (DLT) such as IOTA. Additionally, utilities are exploring the provision of certain smart city services to municipal authorities.
3. For critical infrastructure such as dams, the ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM) of dams. This allows for the early detection of impending collapse risks in dams, rock formations, or tunnels, enabling the timely evacuation of potentially affected populations.
4. Another emerging revenue opportunity is EV charging in parking facilities—specifically, 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 industry 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, now considered the only viable means for practical IoT implementation. 5G is poised to transform the IoT business landscape, leading to an unprecedented surge in research funding for new IoT areas. Consequently, for any practicing engineer, it is absolutely essential to understand the IoT platforms developed by major players like AWS, Google, and especially Microsoft.

However, none of the aforementioned platforms offer a completely exhaustive or comprehensive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies to secure the meters, establish radio networks, implement IoT management technology, and provide various other secured services. The strategy, pricing, and security of any IoT deployment must be optimized and acceptable. Given the vast amount of interdisciplinary knowledge required, it is almost 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 on the challenges, risks, and practical methods for deploying IoT for their next-generation power utility business.

In addition, with scalable deployment, managing IoT services for thousands of sensors and connections is emerging as a distinct engineering research subject. This area, formerly known as managed IoT services, is experiencing rapid growth as the challenges of scalable IoT are much greater than building them. This includes securing over-the-top 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.

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 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 one
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: 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 for critical infrastructure like Dams, Transformers, Sub-stations, High Tension Wires

This instructor-led live training in Greece (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.