Internet of Things (IoT) Training in Grenoble

This instructor-led live training in Grenoble (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 Grenoble (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 Grenoble (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 Grenoble (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 Grenoble (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 Grenoble (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. Azure provides a comprehensive suite of cloud services, including an IoT Suite that offers preconfigured solutions to help developers accelerate their IoT project development.

In this instructor-led, live training, participants will learn how to develop IoT applications using Azure.

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

• Understand the fundamentals of IoT architecture
• Install and configure Azure IoT Suite
• Learn the benefits of using Azure in programming IoT systems
• Implement various Azure IoT services (IoT Hub, Functions, Stream Analytics, Power BI, Cosmos DB, DocumentDB, IoT Device Management)
• Build, test, deploy, and troubleshoot an IoT system using Azure

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. 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.

Unlike many other technologies, the Internet of Things (IoT) is significantly more complex, spanning nearly every branch of core engineering including Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each of these engineering layers involves aspects of economics, standards, regulations, and the evolving state of the art. For the first time, this modest course offers a comprehensive coverage of all these critical aspects of IoT Engineering.

Summary

An advanced training program covering the current state of the art in the Internet of Things.

Cuts across multiple technology domains to develop awareness of an IoT system, its components, and how it can help businesses and organizations.

Live demo of model IoT applications to showcase practical IoT deployments across different industry domains, such as Industrial IoT, Smart Cities, Retail, Travel & Transportation and use cases around connected devices & things

Target Audience

Managers responsible for business and operational processes within their respective organizations and want to know how to harness IoT to make their systems and processes more efficient.

Entrepreneurs and Investors who are looking to build new ventures and want to develop a better understanding of the IoT technology landscape to see how they can leverage it in an effective manner.

Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined.

In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet.

However, the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT.

Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app.

This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business.

Course Objective

Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas.

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 Total control plane

M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one?

Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc.

Security issues and security solutions for IoT

Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc

Open source /commercial enterprise cloud platform for AWS-IoT apps, Azure -IOT, Watson-IOT cloud in addition to other minor IoT clouds

Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc.

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.

Unlike many other technologies, IoT is significantly more complex, spanning almost every branch of core engineering: Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each of its engineering layers involves considerations of economics, standards, regulations, and the evolving state of the art. This course offers, for the first time, a comprehensive overview covering all these critical aspects of IoT Engineering.

For manufacturing professionals, the most critical aspect is understanding advancements in the Industrial Internet of Things. This includes predictive and preventative maintenance, condition-based machine monitoring, production optimization, energy optimization, supply chain optimization, and ensuring the uptime of manufacturing utilities.

Summary

• An advanced training program covering the current state of the art in the Internet of Things within Smart Factories.
• Crosses multiple technology domains to develop awareness of an IoT system, its components, and how it can benefit manufacturing managerial professionals.
• Live demonstration of model IIoT applications for smart factories.

Target Audience

• Managers responsible for business and operational processes within their respective manufacturing organizations who wish to know how to harness IoT to make their systems and processes more efficient.

Duration 3 Days (8 hours per day)

Estimates for the value of the Internet of Things (IoT) market are massive. By definition, IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The number of IoT connections is growing rapidly: from 1.9 billion devices today to an estimated 9 billion by 2018. In that year, the number of connected devices will roughly equal the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer sector, many products and services have already integrated into the IoT ecosystem, including kitchen and home appliances, parking solutions, RFID technology, lighting and heating products, and various applications within the Industrial Internet.

However, the underlying technologies of IoT are not entirely new, as Machine-to-Machine (M2M) communication has existed since the birth of the Internet. What has changed in recent years is the emergence of numerous inexpensive wireless technologies, coupled with the overwhelming adoption of smartphones and tablets in every home. The explosive growth of mobile devices has driven the current demand for IoT.

Industrial IoT (IIoT) for manufacturing has been widely used since 2014, and numerous IIoT innovations have occurred since then. This course will introduce all important aspects of these innovations in the Industrial IoT area.

This training is intended as a technology and business review of this emerging industry, allowing IoT enthusiasts and entrepreneurs to grasp the basics of IoT technology and business models.

Course Objective

The main objective of the course is to introduce emerging technological options, platforms, and case studies of IoT implementation in smart factories for the manufacturing sectors.

1. Study of the business and technology of common IIoT platforms such as Siemens MindSphere and Azure IoT.
2. Open-source or commercial enterprise cloud platforms for AWS IoT apps, Azure IoT, Watson IoT, and Mindsphere IIoT cloud, in addition to other minor IoT clouds.
3. Open-source or commercial electronics platforms for IoT, including Raspberry Pi, Arduino, and Arm Mbed LPC.
4. Security issues and security solutions for IIoT.
5. Mobile, Desktop, and Web applications for registration, data acquisition, and control.
6. M2M wireless protocols for IoT: WiFi, LoRa, BLE, Ethernet, EtherCAT, and PLC: When and where to use which?
7. Basic introduction to all elements of IoT: Mechanical systems, Electronics/sensor platforms, wireless and wireline protocols, Mobile-to-Electronics integration, Mobile-to-enterprise integration, Data analytics, and the Total control plane.

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

The Internet of Things (IoT) represents 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. Python is a high-level programming language recommended for IoT development due to its clean syntax and extensive community support.

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

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

• Comprehend the fundamentals of IoT architecture
• Learn the basics of using Raspberry Pi
• Install and configure Python on Raspberry Pi
• Understand the advantages of using Python for IoT programming
• Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi

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) represents a network infrastructure that wirelessly connects physical objects with software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture.

In this instructor-led live training, participants will grasp the fundamentals of IoT while building an IoT sensor system using a Raspberry Pi.

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

• Comprehend the core principles of IoT, including its components and communication methods
• Learn how to configure the Raspberry Pi specifically for IoT applications
• Build and deploy their own IoT Sensor System

Audience

• Hobbyists
• Hardware/software engineers and technicians
• Technical professionals across all industries
• Beginning developers

Format of the course

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

Note

• Raspberry Pi supports various operating systems and programming languages. This course will use Linux-based Raspbian as the operating system and Python as the programming language. To request a specific setup, please contact us to arrange.
• Participants are responsible for purchasing the Raspberry Pi hardware and components.

In the past three years, the engineering landscape for IoT has undergone massive transformations, primarily driven by tech giants like Microsoft, Google, and Amazon. These industry leaders have invested billions of dollars to develop IoT platforms that make managing and securing certain data perimeters significantly easier. Additionally, IoT edge computing has gained considerable momentum in both research and deployment, becoming a key mechanism for practical IoT implementation. The advent of 5G is also promising to transform the IoT business landscape, leading to an unprecedented surge in research funding across new areas of IoT.

However, large-scale adoption of IoT remains slow due to security concerns at various levels. Securing firmware and gateways is far from ideal. One of the major issues is the lack of consensus among different large IoT vendors regarding security standards. While Microsoft Azure and Amazon AWS have pushed forward with their own proprietary security standards, NIST is placing a more comprehensive framework. Although the OWASP model for IoT security (covering 10 layers) had some impact, it ultimately failed to gain significant traction due to non-adoption by major IoT platforms like Azure or Google.

A second large area of concern is firmware security. Primarily, most firmware remains vulnerable to patching, whether via OTA (Over-The-Air) updates or locally through hardware ports.

Course Objective

1. Provide an introduction to all technology stacks, data models, and vulnerabilities in IoT.
2. Identify the layers of vulnerability at each stack and between stacks.
3. Analyze vulnerabilities arising from vendors and third-party devices.
4. Learn about the NIST standard for IoT security.

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