Introduction to IoT Using Raspberry Pi Training Course

Delve deeper into the innovative realm of edge computing with this advanced course. Explore sophisticated architectures and tackle integration challenges, preparing to leverage the full potential of edge computing in a variety of business environments. Gain expertise in cutting-edge tools and methodologies to deploy, manage, and optimize edge computing solutions that meet specific industry needs.

Advances in technology and the exponential growth of information are reshaping how businesses operate across various sectors, including government. The rise in data generation and digital archiving within government is driven by the proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As the volume and complexity of digital information grow, the challenges of managing, processing, storing, securing, and disposing of this data also become more intricate. New tools for capturing, searching, discovering, and analyzing unstructured data are enabling organizations to derive valuable insights. The government sector is reaching a critical juncture where it recognizes that information is a strategic asset. Consequently, there is an urgent need to protect, leverage, and analyze both structured and unstructured data to better serve the public and meet mission objectives. Government leaders are working towards building data-driven organizations by establishing connections across events, people, processes, and information.

High-value government solutions will emerge from a combination of the most transformative 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 that enable government to make informed decisions by analyzing large volumes of diverse data, both structured and unstructured. This analysis reveals patterns that can inform better decision-making.

Achieving these goals requires more than just collecting vast amounts of data. "Making sense of these massive volumes of Big Data necessitates advanced tools and technologies capable of extracting valuable insights from a wide array of information," noted Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy in a blog post.

To help agencies access these technologies, the White House launched the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to capitalize on the growth of Big Data and develop the necessary analytical tools.

The challenges posed by Big Data are as significant as its potential benefits. Efficient data storage is a primary concern. Budget constraints mean that agencies must keep storage costs low while ensuring easy access to data for users. Backing up large volumes of data further complicates this challenge.

Effective data analysis is another major hurdle. Many agencies use commercial tools to sift through vast amounts of data, identifying trends that enhance operational efficiency. A recent MeriTalk study 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 aiding agencies in analyzing their data. 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 a link that can alert doctors to potential aortic aneurysms before they occur. It is also used for more routine tasks, such as filtering through resumes to match job candidates with hiring managers.

Insurtech, also known as Digital Insurance, represents the integration of insurance with modern technologies. In this field, "digital insurers" leverage technological advancements to optimize their business and operational models, aiming to reduce costs, enhance customer experience, and increase operational agility.

This instructor-led training equips participants with an understanding of the technologies, methods, and mindset necessary for driving digital transformation within their organizations and the broader industry. The training is designed for managers who need a comprehensive overview, to demystify hype and jargon, and to take the initial steps in formulating an Insurtech strategy.

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

• Engage in intelligent and systematic discussions about Insurtech and its various components.
• Clarify and explain the role of each key technology within Insurtech.
• Develop a general strategy for implementing Insurtech within their organization.

Audience

• Insurers
• Technologists in the insurance industry
• Insurance stakeholders
• Consultants and business analysts

Format of the course

• A combination of lectures, discussions, exercises, and group activities focused on case studies.

This instructor-led, live training in Uzbekistan (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.

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills to optimize AI performance for real-time processing and decision-making at the edge.

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

Build a strong foundation in designing and managing a resilient edge computing infrastructure. Learn about open hybrid cloud infrastructures, managing workloads across diverse clouds, and ensuring flexibility and redundancy. This training provides essential knowledge on creating a scalable and secure infrastructure that supports the dynamic needs of modern applications with edge computing.

This instructor-led, live training in Uzbekistan (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.

Unlike other technologies, the Internet of Things (IoT) is significantly more complex, encompassing a wide range of core engineering disciplines such as Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each layer of its engineering involves various aspects of economics, standards, regulations, and the evolving state of the art. For the first time, this course offers a comprehensive overview of all these critical aspects of IoT Engineering.

Summary

An advanced training program covering the latest advancements in Internet of Things (IoT).

This program spans multiple technology domains to develop an understanding of an IoT system and its components, and how it can benefit businesses and organizations.

Live demonstrations of model IoT applications will showcase practical IoT deployments across various industry sectors, including Industrial IoT, Smart Cities, Retail, Travel & Transportation, and use cases involving connected devices and things.

Target Audience

This course is designed for managers responsible for business and operational processes within their respective organizations who wish to learn how to leverage IoT to enhance the efficiency of their systems and processes.

It is also suitable for entrepreneurs and investors looking to build new ventures and gain a deeper understanding of the IoT technology landscape to effectively utilize it in their projects.

The market value estimates for the Internet of Things (IoT) are enormous, given that by definition, the IoT is an integrated and pervasive layer of devices, sensors, and computing power that spans consumer, business-to-business, and government industries. By 2018, the number of connected devices is expected to reach 9 billion, which will be roughly equivalent to the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer space, numerous products and services have already integrated with the IoT, including kitchen and home appliances, parking solutions, RFID technology, lighting and heating systems, and various applications in the Industrial Internet.

While the underlying technologies of IoT are not new—M2M communication has existed since the inception of the internet—the significant changes in recent years include the emergence of numerous inexpensive wireless technologies, coupled with the widespread adoption of smartphones and tablets. The explosive growth of mobile devices has driven the current demand for IoT.

Due to the vast opportunities in the IoT business, many small and medium-sized entrepreneurs have joined the IoT gold rush. Additionally, the rise of open-source electronics and IoT platforms has made the development and management of IoT systems more affordable. Existing electronic product owners are facing pressure to integrate their devices with the internet or mobile applications.

This training aims to provide a technology and business review of an emerging industry, enabling IoT enthusiasts and entrepreneurs to grasp the fundamentals of IoT technology and its business potential.

Course Objective

The primary goal of this course is to introduce participants to the latest technological options, platforms, and case studies of IoT implementations in areas such as home and city automation (smart homes and cities), Industrial Internet, healthcare, government, mobile cellular networks, and other sectors.

It will cover a basic introduction to all elements of IoT, including Mechanical components, Electronics/sensor platforms, Wireless and wireline protocols, Mobile-to-Electronics integration, Mobile-to-Enterprise integration, Data analytics, and Total control plane.

The course will also delve into M2M wireless protocols for IoT, such as WiFi, Zigbee/Zwave, Bluetooth, and ANT+, explaining when and where to use each one.

Participants will learn about mobile/desktop/web applications for registration, data acquisition, and control, as well as available M2M data acquisition platforms like Xively, Omega, and NovoTech.

The course will address security issues and solutions for IoT, including open-source and commercial electronics platforms such as Raspberry Pi, Arduino, and ArmMbedLPC.

It will also cover open-source and commercial enterprise cloud platforms for AWS-IoT apps, Azure-IOT, Watson-IOT cloud, and other minor IoT clouds.

Finally, the course will include studies of the business and technology behind common IoT devices like home automation systems, smoke alarms, vehicles, military applications, and home health solutions.

The Internet of Things (IoT) is a network infrastructure that connects physical devices and software applications wirelessly, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. Python, a high-level programming language, is highly recommended for IoT due to its clear 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:

• Grasp the fundamentals of IoT architecture
• Learn the basics of working with Raspberry Pi
• Install and configure Python on Raspberry Pi
• Understand the advantages of using Python in programming IoT systems
• Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi

Audience

• Developers
• Engineers

Format of the course

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

Note

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

Machine-to-Machine (M2M) involves the direct, automated exchange of information between interconnected mechanical or electronic devices.

In this instructor-led, live training in Uzbekistan, participants will learn about the various aspects of NB-IoT (also known as LTE Cat NB1) as they develop and deploy 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 to 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.

Raspberry Pi is a compact, single-board computer.

In this instructor-led, live training, participants will learn how to set up and program the Raspberry Pi to function as an interactive and powerful embedded system.

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

• Set up an integrated development environment (IDE) for optimal productivity
• Program the Raspberry Pi to control devices such as motion sensors, alarms, web servers, and printers.
• Understand the architecture of the Raspberry Pi, including its inputs and connectors for add-on devices.
• Explore the various options available in programming languages and operating systems
• Test, debug, and deploy the Raspberry Pi to address real-world challenges

Audience

• Developers
• Hardware/software technicians
• Technical professionals in all industries
• Hobbyists

Format of the course

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

Note

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

ThingsBoard is an open-source IoT platform that provides comprehensive device management, data collection, processing, and visualization capabilities for your IoT solutions.

In this instructor-led, live training, participants will learn how to integrate ThingsBoard into their IoT projects.

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

• Install and configure ThingsBoard
• Understand the core features and architecture of ThingsBoard
• Develop IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for efficient telemetry data routing from devices
• Integrate ThingsBoard with Apache Spark to aggregate data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Format of the course

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

Note

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