Internet of Things (IoT) Training in Varna

This instructor-led, live training in Varna (online or onsite) is designed for advanced IoT developers and smart home enthusiasts who wish to automate IoT processes and create innovative solutions using n8n.

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

• Install and configure n8n for IoT workflow automation.
• Connect IoT devices and platforms utilizing n8n nodes and connectors.
• Develop custom workflows to automate IoT tasks and operational processes.
• Utilize IoT protocols such as MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimize IoT automation workflows.

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

This instructor-led, live training (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.

This instructor-led, live training in Varna (online or onsite) is tailored for 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.

This instructor-led, live training in Varna (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 Varna (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.

This instructor-led, live training in Varna (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 completing this training, participants will be able to:

• Comprehend the core principles of IoT and edge computing and their significance in digital transformation.
• Recognize specific use cases for IoT and edge computing within the manufacturing, logistics, and energy industries.
• Distinguish between edge and cloud computing architectures, as well as their respective deployment scenarios.
• Deploy edge computing solutions to support predictive maintenance and real-time decision-making processes.

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

This instructor-led training session, available both online and onsite, is tailored for entry-level technical professionals eager to grasp and implement embedded systems and IoT principles using C programming and microcontroller architectures.

Upon completing this training, participants will be capable of:

• Gaining a thorough understanding of embedded system architecture and its constituent parts.
• Writing and compiling C code to facilitate interaction with embedded hardware.
• Operating microcontroller peripherals, including timers and ADCs.
• Comprehending the role embedded systems play within IoT architectures.

Course Format

• Engaging lectures and group discussions.
• Extensive practical exercises and hands-on practice.
• Live-lab implementation exercises.

Customization Options

• For tailored training on this subject, please reach out to us to make arrangements.

The primary goal of this training is to clarify what the 5G network entails and its influence on smart technologies. I aim to illustrate both the benefits and drawbacks of this technological synergy (5G / IoT) and outline the development trajectory of the network, which has been oriented toward the smart world from its inception.

The objective of the training is to clarify the definition of Smart Solutions—such as the Internet of Things (IoT), Artificial Intelligence (AI), Blockchain, Virtual Reality, and the Metaverse—while distinguishing them from other technologies. Participants will explore the benefits and drawbacks associated with these technological ecosystems.

Technological advancements and the exponential growth of information are fundamentally reshaping business operations across various sectors, including government. The generation of government data and digital archiving rates are surging, driven by the rapid proliferation of mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing portals. As digital information expands in volume and complexity, the challenges associated with information management, processing, storage, security, and disposition become increasingly intricate. Emerging tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector is reaching a critical tipping point, recognizing that information is a strategic asset. Consequently, governments must protect, leverage, and analyze both structured and unstructured information to better fulfill mission requirements. As government leaders strive to evolve into data-driven organizations to successfully accomplish their missions, they are laying the groundwork to correlate dependencies across events, personnel, 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 one of the intelligent industry solutions that enable governments 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 goals 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 helping agencies identify these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated 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. One significant challenge is efficient data storage. With budgets always tight, agencies must minimize the per-megabyte cost of storage while keeping data easily accessible so users can retrieve it when and how they need it. Backing up massive quantities of data further exacerbates this challenge.

Effectively analyzing data presents another major hurdle. Many agencies utilize commercial tools that allow 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 more than $500 billion while also fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address their data analysis needs. For example, the 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 connect job candidates with hiring managers.

This instructor-led live training in Varna (online or onsite) is designed for product managers and developers who aim to utilize Edge Computing to decentralize data management for improved performance, capitalizing on smart devices situated at the source network.

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

• Understand the core concepts and advantages of Edge Computing.
• Identify use cases and examples suitable for Edge Computing application.
• Design and build Edge Computing solutions to facilitate faster data processing and lower operational costs.

The Internet of Things (IoT) constitutes 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 is a general-purpose programming language widely recommended for IoT development due to its pervasive use 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
• Comprehend the fundamentals of IoT architecture
• Recognize the benefits of utilizing C in IoT programming
• Build, test, deploy, and troubleshoot an IoT system using C

Audience

• Developers
• Engineers

Format of the course

• A combination of lectures, discussions, exercises, and intensive hands-on practice

Note

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

This hands-on 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 examine the entire IoT architecture—including sensors, communication protocols, microcontrollers, and cloud integration—through guided exercises utilizing development boards such as ESP32 and Raspberry Pi. Upon completing the course, learners will be equipped to construct a full 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 it using tools such as Grafana or Power BI. The curriculum also addresses security best practices and simulated cyber threats to guarantee secure and robust deployments.

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 mechanisms. Java, a versatile programming language renowned for its 'write once, run anywhere' capability, is highly recommended for IoT development due to its efficiency and portability.

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

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

• Install and configure the necessary tools and frameworks (such as the Eclipse Open IoT Stack) for building Java-based IoT systems
• Grasp the core principles of IoT architecture
• Utilize the Eclipse Open IoT Stack for Java to connect and manage devices within an IoT environment
• Construct, test, and deploy an IoT system leveraging Java

Audience

• Software Developers
• Engineers

Format of the course

• A blend 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 numerous business sectors, with the power utility industry being no exception. Power utility companies are currently facing four primary challenges driven by the growth of 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 preventative maintenance. However, strict quarantine policies regarding critical assets prevent power companies from 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 will soon experience declining revenue from power generation. To offset this lost revenue, companies must aggressively pursue new income streams, such as home energy management as a service, energy storage as a service, and grid services for EV charging and peer-to-peer (P2P) energy trading between homes, microgrids, and batteries. These services must be facilitated through smart metering, smart grids, and secure transactions enabled by Distributed Ledger Technology (DLT) like 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) requires real-time Structural Health Monitoring (SHM) to detect impending collapse risks in dams, rocks, or tunnels, thereby allowing adequate time to evacuate affected populations.
4. Another emerging revenue area is EV charging in parking facilities. The course will explore 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 in developing IoT platforms that are easier to manage and secure. IoT edge computing has gained significant momentum in both research and deployment as the primary means for practical IoT implementation. 5G promises to transform the IoT business landscape, leading to unprecedented funding for new 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 fully comprehensive solution for scalable IoT. Deploying smart metering to millions of homes requires additional technologies to secure the meters, radio networks, IoT management tools, and various other secured services. The strategy, pricing, and security of any IoT deployment must be optimal and acceptable. Given the vast interdisciplinary knowledge required, it is nearly impossible for any single company to assemble a team capable of meeting all these requirements.

This course makes a modest attempt to educate key decision-makers, developers, and security experts on the challenges, risks, and practical methods for deploying IoT in next-generation power utility businesses.

Furthermore, with scalable deployment, managing IoT services for thousands of sensors and connections has emerged as a distinct engineering subject. This area, formerly known as managed IoT services, is experiencing rapid growth as the challenges of scalable IoT exceed the challenges of building it. This includes securing over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, pinpointing 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 IoT elements: 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. The 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/Z-Wave, Bluetooth, ANT+ : 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: 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 C2.18 protocols, NIST Standard for HAN (Home Area Network), HomePlug 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 Varna (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.

In this instructor-led, live training conducted Varna, participants will learn how to maximize Nginx's performance by setting up, configuring, monitoring, and troubleshooting it to manage various forms of HTTP and TCP traffic. The curriculum covers the configuration of critical Nginx parameters, along with OS and virtual machine adjustments, to ensure optimal utilization of Nginx.