Introduction to IoT Using Raspberry Pi Training Course

Advances in technology and the growing volume of information are reshaping business practices across various industries, including government. The generation and digital archiving of government data have increased due to the rapid proliferation of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information becomes more extensive and complex, the challenges in managing, processing, storing, securing, and disposing of this data also grow. New tools for capturing, searching, discovering, and analyzing unstructured data are enabling organizations to derive valuable insights. The government sector is at a critical juncture, recognizing that information is a strategic asset. Governments need to protect, leverage, and analyze both structured and unstructured data to better serve their citizens and meet mission requirements. As leaders strive to transform into data-driven organizations to achieve their goals, they are laying the foundation to understand the interdependencies among events, people, processes, and information.

High-value government solutions will emerge from a combination of the most disruptive 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 better decisions by acting on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

Achieving these outcomes requires more than just accumulating vast amounts of data. "Making sense of these volumes of Big Data necessitates cutting-edge tools and technologies that can analyze and extract useful knowledge from diverse and extensive information streams," 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 to help agencies find these technologies by launching the National Big Data Research and Development Initiative in 2012. The initiative allocated more than $200 million to harness the potential of Big Data and develop the necessary tools for its analysis.

The challenges posed by Big Data are as formidable as its promise is encouraging. Efficient data storage is one such challenge. Given tight budgets, agencies must minimize the cost per megabyte of storage while ensuring that data remains easily accessible to users when and how they need it. Backing up large volumes of data further complicates this task.

Effectively analyzing the data is another major challenge. Many agencies use commercial tools to sift through vast amounts of data, identifying trends that can 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 achieving their mission objectives.

Custom-developed Big Data tools are also aiding agencies in analyzing their data. For example, 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 aortic aneurysms before they occur. It is also used for more routine tasks, such as screening resumes to match job candidates with hiring managers.

Insurtech, also known as Digital Insurance, represents the integration of insurance with modern technologies. In this domain, "digital insurers" leverage technological innovations in their business and operational models to cut costs, enhance customer experience, and increase operational flexibility.

This instructor-led training aims to provide participants with a comprehensive understanding of the technologies, methods, and mindset necessary for driving digital transformation within their organizations and across the industry. The course is designed for managers who need an overarching view, demystify buzzwords, and take initial steps in formulating an Insurtech strategy.

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

• Discuss Insurtech and its various components with intelligence and a systematic approach
• Identify and clarify the role of each key technology within Insurtech
• Develop a general strategy for implementing Insurtech within their organization

Audience

• Insurers
• Technologists in the insurance sector
• 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 Slovakia (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.

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

This instructor-led, live training in Slovakia (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.

In this instructor-led, live training in Slovakia, participants will learn the fundamentals of IoT as they step through the creation of an Arduino-based IoT sensor system.

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

• Understand the principles of IoT, including IoT components and communication techniques.
• Learn how to use Arduino communication modules that can be used for different IoT systems.
• Learn how to use and program a mobile app to control Arduino.
• Use a Wi-Fi module to connect the Arduino to another device.
• Build and deploy their own IoT Sensor System.

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

Summary

An advanced training program covering the current advancements in Internet of Things technology.

This program delves into multiple technological domains to develop an awareness of an IoT system and its components, demonstrating how it can benefit businesses and organizations.

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

Target Audience

The course is designed for managers responsible for business and operational processes within their organizations who wish to understand how to leverage IoT to enhance system efficiency.

It is also ideal for entrepreneurs and investors looking to build new ventures by gaining a deeper understanding of the IoT technology landscape, enabling them to utilize it effectively.

Estimates for the value of the Internet of Things (IoT) market are substantial. 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. The number of IoT connections is expected to grow from 1.9 billion devices today to 9 billion by 2018, roughly equaling the combined total of smartphones, smart TVs, tablets, wearable computers, and PCs.

In the consumer space, many products and services have already transitioned into 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 (Machine-to-Machine) communication has existed since the inception of the internet—the recent surge in inexpensive wireless technologies and the widespread adoption of smartphones and tablets in homes have driven the current demand for IoT.

The vast opportunities in the IoT business have attracted a large number of small and medium-sized entrepreneurs, contributing to what is often referred to as an IoT gold rush. The emergence of open-source electronics and IoT platforms has made it increasingly affordable to develop and manage IoT systems on a significant scale. Existing electronic product owners are feeling pressure to integrate their devices with the internet or mobile apps.

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

Course Objective

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

The course covers 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.

It also explores M2M (Machine-to-Machine) wireless protocols for IoT, such as WiFi, Zigbee/Zwave, Bluetooth, and ANT+, explaining when and where to use each one.

The course discusses the development of Mobile/Desktop/Web applications for registration, data acquisition, and control, along with available M2M data acquisition platforms like Xively, Omega, and NovoTech.

It addresses security issues and solutions for IoT systems.

The training covers open-source and commercial electronics platforms for IoT, such as Raspberry Pi, Arduino, and ArmMbedLPC.

Additionally, it explores open-source and commercial enterprise cloud platforms like AWS-IoT apps, Azure-IOT, Watson-IOT cloud, and other minor IoT clouds.

The course includes case studies of the business and technology aspects of common IoT devices, such as home automation systems, smoke alarms, vehicles, military applications, and home health solutions.

Unlike other technologies, the Internet of Things (IoT) is far more complex, encompassing almost every branch of core engineering, including Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics, and Mobile. Each layer of IoT engineering involves aspects of economics, standards, regulations, and evolving state-of-the-art practices. This course is a pioneering effort to cover all these critical aspects of IoT Engineering.

For manufacturing professionals, the most crucial aspect is understanding advancements in Industrial Internet of Things (IIoT), which includes predictive and preventative maintenance, condition-based monitoring of machines, production optimization, energy efficiency, supply-chain optimization, and the uptime of manufacturing utilities, among others.

Summary

• An advanced training program that covers the current state-of-the-art in Internet of Things (IoT) for smart factories.
• Spans multiple technology domains to develop awareness of an IoT system and its components, and how it can benefit manufacturing managerial professionals.
• Live demonstrations of model IIoT applications for smart factories.

Target Audience

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

Duration 3 Days (8 hours/day)

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

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

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

Industrial IoT (IIoT) has been widely used in manufacturing since 2014, leading to a significant number of IIoT innovations. This course will introduce all the key aspects of these innovations in the Industrial IoT sector.

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

Course Objective

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

1. Studies on the business and technology aspects of common IIoT platforms like Siemens MindSphere and Azure IoT.
2. Overview of open-source and commercial enterprise cloud platforms such as AWS-IoT, Azure-IOT, Watson-IOT, Mindsphere IIoT cloud, and other minor IoT clouds.
3. Exploration of open-source and commercial electronics platforms for IoT, including Raspberry Pi, Arduino, ArmMbedLPC, etc.
4. Discussion of security issues and solutions for IIoT.
5. Development of mobile/desktop/web applications for registration, data acquisition, and control.
6. Examination of M2M wireless protocols for IoT, such as WiFi, LoPan, BLE, Ethernet, Ethercat, PLC, and guidance on when and where to use each one.
7. 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 the total control plane.

Internet of Things (IoT) is a network infrastructure that links physical objects and software applications wirelessly, 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 due to its clear syntax and extensive community support.

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

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

• Understand the fundamentals of IoT architecture
• Learn the basics of working with Raspberry Pi
• Install and configure Python on Raspberry Pi
• Discover 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, exercises, and extensive hands-on practice

Note

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

Over the past three years, engineering in the Internet of Things (IoT) has undergone significant changes, primarily driven by major companies like Microsoft, Google, and Amazon. These giants have invested billions to develop IoT platforms that are easier to manage and secure various data perimeters. Additionally, IoT edge computing has gained considerable momentum in both research and deployment, becoming a practical necessity for IoT implementation. The advent of 5G also promises to revolutionize the business landscape of IoT. This has resulted in a broad range of new areas receiving substantial research funding in IoT.

However, the widespread adoption of IoT is slow due to security concerns at multiple levels. Securing firmware and gateways remains far from optimal. A significant issue is the lack of agreement among major IoT vendors regarding security standards. Microsoft Azure and Amazon AWS have each introduced their own security protocols, while NIST proposes a more comprehensive approach. The OWASP model of 10 layers of IoT security has had some impact but failed to gain widespread adoption due to resistance from major platforms like Azure or Google.

Another critical area of concern is the security of firmware. Most firmware remains vulnerable to patching, whether through over-the-top (OTA) updates or via local hardware ports.

Course Objective

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

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

In this instructor-led, live training in Slovakia, 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:

• Configure an IDE (integrated development environment) for optimal development efficiency
• 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 various programming languages and operating system options
• Test, debug, and deploy the Raspberry Pi to address real-world challenges

Audience

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

Format of the course

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

Note

• Raspberry Pi supports multiple operating systems and programming languages. This course will use a Linux-based Raspbian 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.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualization 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 device data routing
• Integrate ThingsBoard with Apache Spark for aggregating data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Format of the course

• Combination of lecture, discussion, practical exercises, and extensive hands-on practice

Note

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