5G and IoT Training Course

This instructor-led, live training in Sweden (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks enhance Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimized for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

This instructor-led live training in Sweden (online or onsite) targets business managers who need only the critical information required to make pivotal decisions regarding 5G adoption. This is not a technical course but rather a focus on the strategic mindset needed to prepare for the arrival of 5G. The technical and industry coverage will be adapted to the audience.

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

• Identify the opportunities that 5G brings to the organization.
• Identify the risks that 5G poses to business and industry.
• Understand and distinguish the different technologies and standards underlying 5G.
• Understand the role that 5G will play in the further development of AI and IoT.
• Head up 5G initiatives that lead to improvements in customer service and operational efficiency.
• Initiate a strategy for adopting 5G products and services within the organization as well as across external partner organizations.

This instructor-led, live training in Sweden (online or onsite) is aimed at intermediate-level professionals who wish to understand and evaluate the behavior, scope, and implementation of 5G networks in technical environments.

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

• Analyze the behavior of 5G networks in clustered environments.
• Understand the RF environment specific to 5G technology.
• Evaluate real-world cases of 5G implementation from other countries.
• Assess 5G coverage capabilities and limitations.
• Interpret and analyze 5G network quality parameters on a technical level.

5G represents the fifth generation of mobile network technology, designed to deliver higher speeds, reduced latency, and enhanced connection reliability across a diverse array of applications.

This instructor-led live training, available both online and onsite, is intended for intermediate-level IT professionals and technical managers seeking to grasp the fundamentals, architecture, and business implications of 5G networks.

After completing this course, participants will acquire the knowledge and skills to:

• Grasp the core concepts and evolution of 5G technology.
• Identify the primary components and architecture of 5G networks.
• Distinguish between 4G LTE and 5G regarding performance and design.
• Evaluate 5G use cases and applications across various industries.
• Assess 5G deployment strategies and regulatory considerations.

Course Format

• Interactive lectures and group discussions.
• Case studies and scenario-based exercises.
• Hands-on exploration of 5G network architecture through live demonstrations.

Customization Options

• The course can be tailored to focus on specific industry applications or regional 5G deployment contexts upon request.

6G represents the next generation of wireless communication standards, poised to revolutionize IoT ecosystems through ultra-high-speed connectivity, advanced sensing technologies, and integrated AI capabilities.

This instructor-led live training (available online or onsite) is designed for advanced participants seeking to understand and harness the emerging convergence of 6G technologies and IoT applications.

Upon completing this course, learners will be able to:

• Articulate the fundamental technical principles underlying 6G.
• Evaluate 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-driven lectures complemented by expert-led discussions.
• Practical exercises designed to reinforce key engineering principles.
• Guided exploration of case studies and scenario analysis.

Customization Options

• For customized versions of this training aligned with your organization’s technology roadmap, please contact us to arrange.

Technological advancements and the exponential growth of data are reshaping business operations across numerous sectors, including government. The volume of data generated and archived digitally by government entities is surging, driven by the proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands and grows more complex, the challenges associated with its management, processing, storage, security, and disposition intensify. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from unstructured information. The government sector is at a critical juncture, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to better fulfill mission requirements. As leaders work to evolve data-driven organizations, they are establishing the framework to correlate dependencies among events, people, processes, and information.

High-impact government solutions are emerging from the integration of disruptive technologies:

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

Big Data serves as an intelligent industry solution, empowering governments to make superior decisions by acting on patterns revealed through the analysis of vast volumes of data—both related and unrelated, structured and unstructured.

However, achieving these outcomes requires more than merely accumulating large datasets. "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 advanced this effort by launching the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to capitalize on the Big Data explosion and the tools necessary to analyze it.

The challenges posed by Big Data are as formidable as its potential is promising. Efficient data storage is a primary concern. With budgets often constrained, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible for users when and how they need it. Backing up massive data volumes further complicates this challenge.

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

Custom-developed Big Data tools are also enabling agencies to meet their analytical needs. For instance, Oak Ridge National Laboratory’s Computational Data Analytics Group 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 applied to routine tasks, such as screening resumes to match candidates with hiring managers.

This instructor-led live training in Sweden (online or onsite) is designed for intermediate 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:

• Comprehend 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.

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

Embedded systems are specialized computing environments engineered to execute specific tasks within broader apparatuses. The Internet of Things (IoT) represents a vast network of connected physical devices, equipped with sensors and software, enabling them to communicate and exchange information via the internet.

This instructor-led live training (available online or on-site) targets technical professionals at the beginner level who seek to grasp and implement embedded systems and IoT principles using C programming and microcontroller architectures.

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

• Comprehending the structure and constituent parts of embedded systems.
• Writing and compiling C code to facilitate interaction with embedded hardware.
• Utilizing microcontroller peripherals, including timers and ADCs.
• Grasping the role of embedded systems within IoT architectures.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practice sessions.
• Practical implementation in a live laboratory environment.

Customization Options for the Course

• For requests regarding customized training for this course, please get in touch with us to make arrangements.

This instructor-led, live training in Sweden (online or onsite) 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 fundamental 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 within edge computing environments.
• Navigate challenges associated with data privacy and network limitations in IoT systems.

The Internet of Things (IoT) is a network infrastructure that wirelessly connects 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 recommended for IoT due to its widespread use and low-level programming advantages.

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

The Internet of Things (IoT) constitutes a network infrastructure that wirelessly links physical objects with 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 a preferred choice for IoT development due to its portability and efficiency.

During 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 essential tools and frameworks (such as the Eclipse Open IoT Stack) for developing IoT systems with Java
• Grasp the core principles of IoT architecture
• Utilize the Eclipse Open IoT Stack for Java to connect and manage devices within an IoT solution
• Construct, test, and deploy an IoT system utilizing Java

Audience

• Developers
• Engineers

Course Format

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

Note

• For customized training requests, please contact us to arrange your session.

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 IoT:

1. Vendors of machines, controllers, HMIs, and SCADA systems are increasingly connecting these assets to the cloud, promising enhanced analytics and insights for predictive and preventative maintenance. However, strict quarantine policies regarding critical assets prevent power companies from leveraging these new IoT features offered by machine and controller vendors.
2. As the cost of solar and wind power microgrids continues to decline, utility companies are anticipating a drop in revenue from traditional power generation. To offset this loss, companies must aggressively pursue new revenue streams, such as energy management for homes as a service, energy storage as a service, and grid services for EV charging or peer-to-peer (P2P) energy trading between homes, microgrids, and batteries. All these activities require smart metering, smart grids, and secure transactions facilitated by Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring the provision of smart city services to municipal authorities.
3. For critical infrastructure such as dams, ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM). This allows for early warning of potential collapses in dams, rocks, or tunnels, enabling the evacuation of people in affected areas.
4. Another emerging revenue opportunity is EV charging in parking facilities. This module explores 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 to develop IoT platforms that are easier to manage and secure. Furthermore, IoT edge computing has gained significant momentum in both research and deployment as the most practical means of implementing IoT. The advent of 5G promises to further transform the IoT business landscape, leading to unprecedented investment in IoT research. Consequently, it is essential for practicing engineers to understand the IoT platforms developed by major players like AWS, Google, and particularly Microsoft.

However, none of these platforms offer a comprehensive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies to secure the meters, radio networks, IoT management tools, and other secured services. The strategy, pricing, and security of any IoT deployment must be optimized and acceptable. Given the interdisciplinary nature of this knowledge, it is nearly impossible for any single company to assemble a team capable of meeting all requirements.

This course is a modest attempt to educate key decision-makers, developers, and security experts on the challenges, risks, and practical approaches to deploying IoT for the next generation of power utility business.

Additionally, as deployment scales, managing IoT services for thousands of sensors and connections has emerged as a distinct engineering discipline. This area, formally known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT are far greater than simply building them. This includes securing over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, identifying 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 of IoT implementation in Power Utility Companies, including Smart Metering, Smart Cars, SHM (Structural Health Monitoring), Power Quality Diagnosis, and Smart Contracts. Participants will receive a basic introduction to all IoT elements: mechanical components, electronics/sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, and 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+: 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: Review of available M2M data acquisition platforms for IoT—AWS IoT, Azure IoT, Google IoT.
6. Security issues and solutions for IoT: A review of security across all technology stacks.
7. Enterprise IoT platforms such as 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 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, sub-stations, and high-tension wires.

This live, instructor-led training (online or onsite) targets beginner to intermediate telecom engineers and field professionals who wish to use structured deployment methodologies and industry best practices to successfully install, supervise, integrate, and manage multi-vendor wireless networks from 2G to 5G across operator and enterprise environments.

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

• Install and configure multi-vendor BTS systems (Huawei, ZTE, Ericsson) from 2G to 5G.
• Supervise site deployment activities and coordinate RF, transmission, power, civil, and core network teams during integration.
• Prepare telecom sites for ATP (Acceptance Test Procedure) and manage operator handover processes.
• Monitor wireless KPIs and manage cluster-based and region-based network operations within commercial and technical reporting structures.