Nginx Training Course

This training aims to explain the fundamentals of 5G networks and their impact on smart technologies. It highlights both the benefits and drawbacks of the relationship between these technologies (5G and IoT) and outlines the development directions of a network designed from its inception for the smart world.

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

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

Upon completing this course, learners will be able 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.

Technological advancements and the exponential growth of information are reshaping operational practices across numerous sectors, including government. The generation of government data and the rates of digital archiving are climbing, driven by the rapid expansion 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 also become more intricate. New tools for capture, search, discovery, and analysis are enabling organizations to derive meaningful insights from unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. To better serve the public and fulfill mission requirements, governments must protect, leverage, and analyze both structured and unstructured information. As government leaders strive to evolve their organizations into data-driven entities, they are establishing the foundation to correlate dependencies among events, personnel, processes, and information.

High-value government solutions will emerge from the integration of several disruptive technologies:

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

Big Data represents a transformative industry solution that empowers government agencies to make superior decisions by acting on patterns revealed through the analysis of vast volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these outcomes requires more than merely accumulating large 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," 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 assisting agencies in identifying 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 promise it offers is encouraging. Efficient data storage is one such challenge. With budgets remaining tight, agencies must minimize the per-megabyte cost of storage while ensuring data remains easily accessible, allowing users to retrieve information when and how they need it. Backing up massive quantities of data further intensifies this challenge.

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

Custom-developed Big Data tools are also enabling 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 can alert doctors to aortic aneurysms before they occur. It is also employed for routine tasks, such as screening resumes to connect job candidates with hiring managers.

This instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level IT professionals and business managers seeking 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:

• Understand the core 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 Turkey (online or onsite) is tailored for intermediate-level developers, system architects, and industry professionals seeking to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Grasping the fundamentals of Edge AI and its role in IoT.
• Setting up and configuring Edge AI environments for IoT devices.
• Developing and deploying AI models on edge devices for IoT solutions.
• Implementing real-time data processing and decision-making capabilities in IoT systems.
• Integrating Edge AI with various IoT protocols and platforms.
• Addressing ethical considerations and adhering to best practices for Edge AI in IoT.

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

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

• Understand the basic concepts and advantages of Edge Computing.
• Identify the use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

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

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

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

• Comprehend the architecture and core components of embedded systems.
• Write and compile C code to facilitate interaction with embedded hardware.
• Utilize microcontroller peripherals, including timers and ADCs.
• Understand 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 laboratory environment.

Customization Options

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

This instructor-led, live training in Turkey (online or onsite) is designed for intermediate-level professionals aiming 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 applying 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 within edge computing environments.
• Overcome challenges associated with data privacy and network limitations in IoT systems.

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

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

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

• Install and configure NetBeans for programming IoT systems with C
• Grasp the fundamental principles of IoT architecture
• Appreciate the benefits of utilizing C in IoT system programming
• Build, test, deploy, and troubleshoot IoT systems 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 seamless communication and data exchange through network communications, cloud computing, and data capture. Java, renowned for its "write once, run anywhere" capability, is a versatile language highly recommended for IoT development due to its portability and efficiency.

This instructor-led live training equips participants with the skills to program IoT solutions using Java.

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

• Install and configure essential tools and frameworks, such as the Eclipse Open IoT Stack, for Java-based IoT programming
• Grasp the fundamental principles of IoT architecture
• Leverage 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

Course Format

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

Note

• For customized training arrangements for this course, please contact us directly.

Connected devices are transforming numerous industries, with the power utility sector being no exception. Power utility companies currently face four primary challenges arising from the growth of the Internet of Things (IoT):

1. Vendors are increasingly connecting machines, controllers, HMI systems, and SCADA 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 cost of solar and wind power microgrids continues to decrease, utility companies anticipate declining revenue from traditional power generation. To offset these losses, 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) such as IOTA. Additionally, utilities are exploring opportunities to provide smart city services to local authorities.
3. For critical infrastructure like dams, ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM). This allows for the early detection of potential collapse risks in dams, rocks, or tunnels, enabling timely evacuation of affected populations.
4. Another emerging revenue area is EV charging in parking facilities. IoT plays a crucial role in facilitating smart charging and smart parking solutions.

Over the past three years, IoT engineering has undergone massive changes, primarily driven by tech giants Microsoft, Google, and Amazon. These companies have invested billions in developing IoT platforms that are easier to manage and more secure. IoT edge computing has also gained significant momentum in both research and deployment as the primary means for practical IoT implementation. The advent of 5G promises to further transform the IoT business landscape, leading to unprecedented funding for new IoT research 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 a complete, exhaustive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies to secure the meters, manage radio networks, handle IoT management technology, and provide other secured services. The strategy, pricing, and security of any IoT deployment must be optimal and acceptable. Given the interdisciplinary knowledge required, it is nearly impossible for any single company to assemble a team capable of meeting all these requirements.

This course aims 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.

Furthermore, with scalable deployment, managing IoT services for thousands of sensors and connections has emerged as a distinct engineering research subject. This area, formerly known as managed IoT services, is experiencing rapid growth as the challenges for scalable IoT far exceed the challenges of building them. These challenges include securing over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, identifying root causes of API failures, and tracking hardware and service health in distributed systems.

Course objectives

The main objective of this course is to introduce emerging technological options, platforms, and case studies of IoT implementation in power utility companies, covering Smart Metering, Smart Cars, SHM (Structural Health Monitoring), Power Quality Diagnosis, and Smart Contracts. It also provides a basic introduction to all IoT elements, 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, and Centralized vs. Decentralized IoT.
2. IoT Ecosystem for Business: Third-party device management and risk management for 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.
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 the security of 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 applications for critical infrastructure like dams, transformers, substations, and high-tension wires.

This instructor-led live training in Turkey (online or onsite) targets developers and programmers who wish to install, configure, and manage the Kaa platform for building IoT applications.

By the end of the 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 Turkey (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 clarify the definition of 'Smart Solutions' (including IoT, AI, Blockchain, Virtual Reality, and the Metaverse) and delineate what falls outside this scope, while highlighting the pros and cons associated with these technological domains.

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

By the conclusion 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.