Internet of Things (IoT) Training in Tallinn

This instructor-led, live training in Tallinn (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.

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

This instructor-led, live training (online or onsite) is aimed at 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 Tallinn (online or onsite) is aimed at 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 Tallinn (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 Tallinn (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 Tallinn (online or onsite) is aimed at 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.

Embedded systems are purpose-built computing systems designed to perform dedicated functions within larger systems. IoT (Internet of Things) is a network of interconnected physical devices embedded with sensors and software that communicate and exchange data over the internet.

This instructor-led, live training (online or onsite) is aimed at beginner-level technical professionals who wish 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:

• Understand the architecture and components of embedded systems.
• Write and compile C code for embedded hardware interaction.
• Work with microcontroller peripherals such as timers and ADCs.
• Understand how embedded systems contribute to IoT architectures.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

The aim of the training is to explain what the 5G network is and what impact it has on smart technologies. I want to show you both the advantages and disadvantages of these technological relationships (5G / IoT) and show you the directions of development of the network, which - from the very beginning - was dedicated to the smart world.

The aim of the training is to explain what are - and what are not - Smart solutions (Internet of Things, AI, Blockchain, Virtual Reality, Metaverse) and to show the advantages and disadvantages of these technological worlds.

Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will be created from a mashup of the most disruptive technologies:

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

Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured.

But accomplishing these feats takes 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," 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 step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it.

The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge.

Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.).

Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through resumes to connect job candidates with hiring managers.

This instructor-led, live training in Tallinn (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralize data management for faster performance, leveraging smart devices located 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.

Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. C is a general purpose programming language recommended for IoT due to its ubiquity and low-level programming benefits. 

In this instructor-led, live training, participants will learn how to program IoT solutions with C.

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

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

Audience

• Developers
• Engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. Java is a general purpose language that is known for being "write once, run anywhere." Java is recommended for IoT due to its portability and efficiency.

In this instructor-led, live training, participants will learn how to program IoT solutions with Java.

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

• Install and configure tools and frameworks (Eclipse Open IoT Stack) for programming IoT systems with Java
• Understand the fundamentals of IoT architecture
• Use the Eclipse Open IoT Stack for Java to connect and manage devices in an IoT solution
• Build, test, and deploy an IoT system using Java

Audience

• Developers
• Engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

Connected device is disrupting many business, power utility being no exception. Power Utility companies are essentially faced with four challenges from growth of IoT

1. Machines, Controllers, HMI, SCADA systems are increasingly becoming cloud connected by the vendors who promise to offer more analytics and insight via their data for predictive and preventative maintenance. But quarantine policy of the critical assets means these new IoT features from the Machine/Controller Vendors can’t be utilized by the Power Companies
2. With the ever decreasing cost of solar and wind power microgrid, Utility companies will soon see declining revenue from power generation. To compensate for the lost revenue of power production, the company has to aggressively pursue new areas of revenues such as Energy management of Home as a service, Energy storage as a service, offering grid service for EV charging, grid service for P2P energy trading between the homes, home and microgrid, microgrid to microgrid, microgrid to battery, home to battery etc. All of this need to be facilitated via smart metering, smart grid and smart & secured transactions only possible via DLT ( distributed ledger technology) like IOTA. Also Utilities are exploring to offer some of the smart city services to the city authority
3. For critical infrastructure like dams, ICOLD ( International Committee Of Large Dams) want to see Structural Health Monitoring (SHM) of the dams real time so that any impending danger of collapse of the dam or rock or tunnel can be informed in advance to vacate the people who may be affected
4. Also a new emerging area of revenue will be EV charging in Parking-How IoT can facilitate smart charging and smart parking?

Over the last three years, engineering in IoT has seen massive changes primarily driven by Microsoft, Google and Amazon. These large behemoths have invested billions of dollars to develop IoT platforms that are more easy to manage and secure. Also IoT edge has gained a lot of momentum in both research and deployment as only means for practical IoT implementation. 5G is promising to transform the business of IoT. This has led to an unprecedented large swath of new areas of research funding in IoT. This is why right now for any practicing engineer it is absolutely essential to understand IoT platforms developed for major players like AWS, Google and Specially Microsoft.

However, neither of the above platform offers exhaustive or a totally comprehensive solution for a scalable IoT. Just for Smart Metering to be deployed to millions of homes, additional technology to secure the smart meter, radio networks, IoT management technology and many other additional secured services will be required. Strategy, Price and Security of any IoT deployment must be optimal and acceptable. Given so much of interdisciplinary knowledge, it is almost difficult for any company to deploy a team which can meet all the requirements.

This course is a modest attempt to educate the key decision makers, developers, security experts about what are the challenges, risks and practical way to deploy IoT for their next generation power utility business.

In addition, with scalable deployment, managing IoT services for thousands of sensors and connections are emerging as a separate engineering subject of research. This area , formally known as managed IoT services is experiencing rapid growth as challenges for scalable IoT are much bigger than building them. This includes security of over the top firmware/software update, managing calibration of the sensors and systems, auto-diagnosis of any connection issue, narrowing down on root cause of API failures, tracking the hardware and service health of the distributed system etc.

Course objectives

Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in Power Utility Companies - Smart Metering, Smart Car, SHM ( structural health monitoring), Power Quality Diagnosis and Smart Contracts. Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, 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, risk management of entire IoT ecosystem
3. M2M Wireless protocols for IoT- WiFi, SigFox,LORA, LPWAN, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one
4. Fundamentals of IoT Gateways- Risks, Management and Ecosystem
5. Mobile/Desktop/Web app - for registration, data acquisition and control –Available M2M data acquisition platform for IoT—AWS IoT, Azure IoT, Google IoT
6. Security issues and solutions for IoT- Review of security of all the 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 Ledge Technology ( DLT) such as Blockchain, HyperLedger and DAG ( Direct Acyclic Graph) for smart contract, P2P transactions, smart car charging
10. IoT for critical infrastructure like DAM, Transformer, Sub-station, High Tension Wire

This instructor-led, live training in Tallinn (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.

In this instructor-led, live training in Tallinn, participants will learn how to maximize the performance of Nginx as they set up, configure, monitor and troubleshoot Nginx for handling various forms of HTTP / TCP traffic. Topics covered include how to configure the most important parameters in Nginx, the OS and a virtual machine to gain maximum value out of Nginx.