Internet of Things (IoT) Training in France

This instructor-led live training in France (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 completing 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.
• Utilize IoT protocols such as MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimize IoT automation workflows.

As the successor to current wireless standards, 6G is poised to revolutionize IoT ecosystems by delivering ultra-fast connectivity, advanced sensing capabilities, and seamless AI integration.

This instructor-led live training, available online or onsite, targets advanced participants eager to explore and apply the emerging synergy between 6G technologies and IoT applications.

Upon completion, learners will be able to:

• Articulate the fundamental technical principles of 6G.
• Analyze how 6G will transform IoT device communication and system architecture.
• Evaluate IoT use cases enabled by 6G across various industries.
• Develop strategies for integrating 6G capabilities into existing IoT infrastructures.

Course Format

• Concept-driven lectures complemented by expert-led discussions.
• Practical exercises designed to reinforce core engineering principles.
• Guided exploration of case studies and scenario analyses.

Customization Options

• For customized training aligned with your organization's technology roadmap, please contact us to arrange.

This instructor-led, live training in France (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, conducted in France (online or onsite), is designed for intermediate developers, system architects, and industry professionals aiming to utilize Edge AI to enhance IoT applications with intelligent data processing and analytics.

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

• Comprehend 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 France (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want to apply TinyML for predictive maintenance, anomaly detection, and smart sensor solutions.

Upon completing this training, participants will be able to:

• Grasp the core principles of TinyML and its role in IoT ecosystems.
• Configure a TinyML development environment tailored for IoT projects.
• Create and deploy machine learning models on low-power microcontrollers.
• Apply TinyML techniques for predictive maintenance and anomaly detection.
• Refine TinyML models to maximize power efficiency and minimize memory consumption.

This instructor-led, live training in France (online or onsite) is designed for intermediate-level IT professionals and business managers who want to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

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

• Grasp the fundamental principles of IoT and edge computing and their significance in digital transformation.
• Recognize practical applications of IoT and edge computing in manufacturing, logistics, and energy sectors.
• Distinguish between edge and cloud computing architectures and their respective deployment scenarios.
• Deploy edge computing solutions to support predictive maintenance and real-time decision-making.

Embedded systems are specialized computing architectures 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 connect, communicate, and share data via the internet.

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

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

• Comprehend the architecture and core components of embedded systems.
• Draft and compile C code to facilitate interaction with embedded hardware.
• Operate microcontroller peripherals, including timers and Analog-to-Digital Converters (ADCs).
• Grasp the role of embedded systems within IoT architectures.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical drills.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For a tailored training experience, please contact us to arrange your specific requirements.

This training aims to clarify the nature of 5G networks and their influence on smart technologies. We will explore both the advantages and disadvantages of the relationship between 5G and IoT, while highlighting the developmental trajectory of a network designed from its inception for the smart ecosystem.

The objective of this training is to define 'Smart solutions' (Internet of Things, AI, Blockchain, Virtual Reality, Metaverse) and illustrate the benefits and drawbacks of these technological domains.

Advances in technology and the exponential growth of information are reshaping business operations across various sectors, including the government. The generation of government data and the pace of digital archiving are accelerating, 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 management, processing, storage, security, and disposition of this data become increasingly challenging. New tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognizing that information is a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured information to better serve the public and fulfill mission requirements. As government leaders work to evolve their organizations into data-driven entities to successfully achieve their missions, they are establishing the foundation to correlate dependencies across events, personnel, processes, and information.

High-value government solutions will emerge from a convergence 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, enabling government entities to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these objectives 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," 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 assisting agencies in finding 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 nearly as daunting as the promise it offers is encouraging. Efficient data storage is one such challenge. As budgets remain tight, agencies must minimize the per-megabyte cost of storage while keeping data easily accessible so users can retrieve it whenever and however they need it. Backing up massive amounts of data further complicates this 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 fulfilling mission objectives).

Custom-developed Big Data tools are also enabling agencies to meet the need for data analysis. 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 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 France (online or onsite) is designed for product managers and developers who aim to use Edge Computing to decentralize data management for improved 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.

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. As a general-purpose programming language, C is highly recommended for IoT development due to its widespread availability and advantages in low-level programming.

In 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 capable of:

• Installing and configuring NetBeans to program IoT systems with C
• Comprehending the fundamental principles of IoT architecture
• Recognizing the benefits of utilizing C for IoT system programming
• Constructing, testing, deploying, and troubleshooting an IoT system using C

Audience

• Developers
• Engineers

Course Format

• A combination 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 connects physical devices with software applications, enabling mutual communication and data exchange through network communications, cloud computing, and data capture. Java, a general-purpose language renowned for its 'write once, run anywhere' capability, is highly recommended for IoT development due to its portability and efficiency.

In this instructor-led live training, participants will learn how to program IoT solutions using 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 devices are disrupting numerous industries, with power utilities 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 (Human-Machine Interface), and SCADA systems to the cloud, promising enhanced analytics and insights for predictive and preventative maintenance. However, the strict quarantine policies applied to critical assets prevent power companies from fully leveraging these new IoT features offered by machine and controller vendors.
2. With the rapidly declining costs of solar and wind power microgrids, utility companies will soon experience reduced revenue from power generation. To compensate for this loss, companies must aggressively pursue new revenue streams, such as 'Energy Management as a Service' for homes, 'Energy Storage as a Service', and grid services for EV charging and peer-to-peer (P2P) energy trading between homes, microgrids, and batteries. All these services require facilitation through smart metering, smart grids, and secure transactions enabled by Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring opportunities to provide smart city services to local authorities.
3. For critical infrastructure such as dams, ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM). This ensures that impending dangers, such as the potential collapse of a dam, rock face, or tunnel, can be detected in advance, allowing for the timely evacuation of at-risk populations.
4. A new 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 Microsoft, Google, and Amazon. These tech giants have invested billions to develop IoT platforms that are easier to manage and more secure. IoT edge computing has gained significant momentum as the practical means for IoT implementation. Furthermore, 5G promises to transform the IoT business landscape, leading to unprecedented research funding. For practicing engineers, it is essential to understand the IoT platforms developed by major players like AWS, Google, and especially Microsoft.

However, none of these platforms offer a completely exhaustive or comprehensive solution for scalable IoT. For instance, deploying smart meters to millions of homes requires additional technologies for securing the meters, radio networks, IoT management tools, and 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 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 next-generation power utility businesses.

Additionally, scalable deployment has made managing IoT services for thousands of sensors and connections an emerging engineering subject. This area, formally known as managed IoT services, is growing rapidly as the challenges of scalable IoT extend far beyond initial implementation. 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 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: mechanical and 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+ : Guidance on 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 – 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 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 (Direct 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 instructor-led, live training in France (online or onsite) is designed for developers and programmers who want 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 France, participants will explore various aspects of NB-IoT (also known as LTE Cat NB1) while developing and deploying a sample NB-IoT-based application.

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

• Identify the different components of NB-IoT and how they fit together to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

In this instructor-led live training conducted in France, participants will learn how to maximize the performance of Nginx by setting it up, configuring, monitoring, and troubleshooting it to handle various forms of HTTP and TCP traffic. Covered topics include configuring critical parameters in Nginx, the OS, and a virtual machine to gain maximum value from Nginx.