IoT Programming with C Training Course

This training aims to clarify what the 5G network entails and its influence on smart technologies. We will explore both the benefits and drawbacks of the interplay between 5G and IoT, while also outlining the developmental trajectory of a network designed from the outset for the smart world.

6G represents the forthcoming generation of wireless communication standards, poised to revolutionize 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.

Technological advancements and the exponential growth of information are fundamentally reshaping business operations across various sectors, including the government sector. The volume of data generated and digitized by governments is increasing rapidly, driven by the proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing digital portals. As digital information expands and grows more complex, the management, processing, storage, security, and disposal of that information also become more challenging. Emerging tools for capture, search, discovery, and analysis are enabling organizations to derive valuable insights from unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. To better serve citizens and fulfill mission requirements, governments must protect, leverage, and analyze both structured and unstructured data. As government leaders work to evolve their organizations into data-driven entities, they are establishing the foundation to correlate dependencies across events, personnel, processes, and information.

High-impact government solutions are being developed by integrating the most disruptive technologies:

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

Big Data represents an intelligent industry solution that empowers governments to make superior decisions by acting on patterns identified through the analysis of vast volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these outcomes requires more than just 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," 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 required to analyze it.

The challenges posed by Big Data are as formidable as its potential benefits are encouraging. Efficient data storage is one key challenge. With budgets remaining tight, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible so users can retrieve it whenever and however they need it. Backing up these massive data volumes further intensifies the challenge.

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

Custom-developed Big Data tools are also enabling agencies to meet their data analysis needs. For instance, 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 routine tasks, such as sifting through resumes to match job candidates with hiring managers.

This instructor-led, live training session in Portugal (online or onsite) is designed for intermediate IT professionals and business managers who wish to explore the potential of IoT and edge computing to drive efficiency, real-time processing, and innovation across various industries.

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

• Grasping the core principles of IoT and edge computing and their significance in digital transformation.
• Recognising practical applications of IoT and edge computing within manufacturing, logistics, and energy sectors.
• Distinguishing between edge and cloud computing architectures as well as their respective deployment scenarios.
• Deploying edge computing solutions to support predictive maintenance and real-time decision-making.

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

Upon completing 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 develop Edge Computing solutions to accelerate data processing and lower operational costs.

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

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

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

• Comprehend the architecture and components of embedded systems.
• Write and compile C code to facilitate interaction with embedded hardware.
• Operate microcontroller peripherals, including timers and ADCs.
• Understand the role embedded systems play within IoT architectures.

Course Format

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

Customization Options

• For tailored training on this course, please contact us to arrange your requirements.

This instructor-led, live training in Portugal (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.

The Internet of Things (IoT) constitutes a network infrastructure that links physical objects and software applications wirelessly, enabling communication and data exchange through network communications, cloud computing, and data capture mechanisms. Java is a versatile programming language celebrated for its 'write once, run anywhere' capability. It is particularly recommended for IoT development due to its portability and efficiency.

In this instructor-led live training session, participants will learn how to develop 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
• Grasp the fundamental principles of IoT architecture
• 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

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

Note

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

The proliferation of connected devices is reshaping industries, with power utilities being no exception. Power utility companies currently face four primary challenges driven by the expansion of the Internet of Things (IoT):

1. Vendors are increasingly connecting machines, controllers, Human-Machine Interfaces (HMI), and SCADA systems to the cloud, promising enhanced analytics and insights for predictive and preventative maintenance. However, strict quarantine policies for critical assets prevent power companies from fully leveraging these IoT features offered by machine and controller vendors.
2. As the cost of solar and wind power microgrids continues to fall, utility companies are projected to experience a decline in revenue from traditional power generation. To offset this loss, companies must aggressively pursue new revenue streams such as 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. Facilitating these transactions requires smart metering, smart grids, and secure operations enabled by Distributed Ledger Technology (DLT) like IOTA. Furthermore, utilities are exploring the provision of smart city services to municipal authorities.
3. For critical infrastructure such as dams, ICOLD (International Committee on Large Dams) mandates real-time Structural Health Monitoring (SHM) to detect impending collapse risks in dams, rock formations, or tunnels, allowing for early evacuation of affected populations.
4. Another emerging revenue opportunity lies in EV charging within parking facilities. IoT can facilitate smart charging and smart parking solutions to address this need.

Over the past three years, IoT engineering has undergone significant transformations, largely 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. Additionally, IoT edge computing has gained substantial 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 IoT platforms developed for major players like AWS, Google, and particularly Microsoft.

However, none of these platforms provide an exhaustive or fully comprehensive solution for scalable IoT. Deploying smart metering across millions of homes requires additional technologies for securing smart meters, radio networks, IoT management tools, and other secure services. The strategy, pricing, and security of any IoT deployment must be optimized and acceptable. Given the interdisciplinary knowledge required, it is nearly impossible for a single company to assemble a team capable of meeting all these demands.

This course aims 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 deployments, managing IoT services for thousands of sensors and connections has emerged as a distinct engineering discipline. This area, formerly known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT extend beyond initial construction. 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, including Smart Metering, Smart Cars, SHM (Structural Health Monitoring), Power Quality Diagnosis, and Smart Contracts. It provides a basic introduction to all IoT elements, including mechanical aspects, 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 and 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+: Understanding when and where to use each protocol.
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, such as AWS IoT, Azure IoT, and Google IoT.
6. Security issues and solutions for IoT, including a review of security across all technology stacks.
7. Enterprise IoT platforms such as Microsoft Azure IoT Suite, AWS IoT, Google IoT, and Siemens MindSphere.
8. Smart Metering, Open Smart Grid Protocols (OSGP), ANSI C12.18 Protocols, NIST Standard for HAN (Home Area Network), HomePlug Powerline Alliance, and Smart Meter Security Standard (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 such as dams, transformers, substations, and high-tension wires.

This instructor-led live training in Portugal (online or on-site) is designed for 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.

This instructor-led, live training in Portugal (online or onsite) is designed for advanced IoT developers and smart home enthusiasts looking to automate IoT processes and develop 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.
• Utilise IoT protocols such as MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimise IoT automation workflows.

In this instructor-led live training held in Portugal, participants will learn how to maximise Nginx's performance as they set up, configure, monitor, and troubleshoot Nginx for handling various forms of HTTP and TCP traffic. The curriculum covers how to configure the most essential parameters in Nginx, the operating system, and a virtual machine to gain maximum value from Nginx.

The objective of this training is to clarify what constitutes - and what does not - a Smart solution (including Internet of Things, AI, Blockchain, Virtual Reality, and the Metaverse), while illustrating the advantages and disadvantages inherent in these technological domains.

This instructor-led, live training in Portugal (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:

• Grasp the fundamentals of TinyML and its applications in IoT.
• Configure a TinyML development environment for IoT projects.
• Create 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.