IoT and Blockchain Training Course

Duration

7 hours (usually 1 day including breaks)

Requirements

  • Basic familiarity with Internet-of-Things (IoT) and/or Blockchain Concepts

Overview

Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture.

Blockchain is a decentralized database system which stores data in ledgers distributed across many nodes.

Using blockchain technology with IoT allows accessibility and supply of IoT data without the need for central control. This integration opens up a suite of new possibilities and multiple benefits for business organizations.

In this instructor-led, live training (remote), participants will learn how blockchain and IoT can work together as they step through a series of hands-on live-lab exercises.

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

  • Understand how blockchain and IoT can work together to provide solutions for their organization
  • Explore various tools and resources to implement a blockchain-based IoT solution for their organization

Audience

  • Developers
  • Managers

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.

Course Outline

Introduction

Overview of Internet-of-Things (IoT) Technology

Overview of Blockchain Technology

Benefits of Integrating IoT and Blockchain Technology

Overview of the Flowchain Framework: A Case Study on Building a Blockchain for the IoT

Required Architecture for Blockchain and IoT Integration

IoT Device Interoperability and How It Affects the Blockchain Implementation

Applying Blockchain’s Distributed Ledger for IoT

Implementing Blockchain’s Consensus System for IoT

Using the Flowchain SDK to Implement an Iot and Blockchain Solution for Your Organization

Overview of Other IoT and Blockchain Tools and Solutions for Your Organization

  • IBM IoT on Blockchain
  • Microsoft Azure IoT
  • AWS IoT Platform
  • Google Cloud IoT

Summary and Conclusion

Internet of Things and Blockchain Training Course

Duration

7 hours (usually 1 day including breaks)

Requirements

General understanding of IoT or Blockchain.

Overview

GOAL

The aim of the training is to introduce into the world of the Internet of Things (smart solutions) and blockchain as well as to show the advantages and disadvantages of these technological worlds.

During the training, you will get acquainted with the existing, ready-made tools thanks to which you can implement these smart solutions in your company (contrary to appearances – it does not have to be very difficult) and you will gain the ability to consciously choose the best solutions.

By learning about practical examples of both IoT and Blockchain applications, you will gain unique skills related to smart technologies on the Polish market (e.g. you will learn good practices related to implementation).

You will gain specific knowledge – technical and business – that will allow you to find yourself in the technological market, you will learn practical tips necessary to start a conscious digital transformation and you will gain knowledge that will strengthen your business skills in the smart area.

The training will be especially useful:

  • for managers who want to know the business benefits of adapting smart solutions,
  • for people who want to strengthen their knowledge in the field of modern technologies,
  • for managers who plan to transform the company but do not know where to start and whether it is profitable,
  • for people who need specifics: how the technology works, what are its advantages and disadvantages, how much can I earn on it, how much are the costs,
  • for employees who will have to start working with smart solutions in a short time,
  • for decision-makers to be aware of what and how to talk to sellers about IoT / blockchain

TRAINING DISTINCTIONS

  • Practical knowledge gained in large-scale projects
  • Technical and business perspective
  • Common pitfalls and best practices

Course Outline

What is the Internet of Things?

What stacks / layers / elements does the IoT consist of?

  • UX layer
  • Technological layer
  • Market layer
  • Business layer
  • Physical Layer

What’s your business?

  • What you need to know before a smart transformation
  • How to conduct an effective assessment and audit of your company?
  • What to look for when choosing a manufacturer / buying IoT?

What is blockchain technology and where is it used?

  • Is blockchain for me?
  • Advantages and disadvantages of blockchain integration – on the example of a specific company

What is the essence of communication and what acronyms do you need to know to freely navigate the world of blockchain / IoT?

  • dApp,
  • RPC,
  • MQTT,
  • COAP

What is the interoperability problem (why some devices don’t work with others)?

  • Open IoT solutions
  • Closed IoT solutions

What are the problems with smart devices and blockchain?

  • User Experience
  • Legislation
  • Ethics
  • Work methodology

How to use IoT and blockchain (example):

  • Amazon (order blockchain)
  • MediLedger network (medical blockchain)
  • Alibaba / Walmart (nutritional blockchain)
  • Wien Energie (city blockchain)
  • Everledger (blockchain of diamonds and precious resources)
  • Deloitte (order blockchain)

Which supplier to choose (overview of available companies in the USA, Europe and Asia)?

  • FlowChain,
  • SkeyNetwork,
  • others

What is the idea of ​​tokenization?

How – in terms of IoT – are they different …?

  • Microsoft,
  • AWS,
  • Google

Q&A session

Artificial Intelligence (AI) vs. Machine Learning

Artificial intelligence (AI) and machine learning are often used interchangeably, but machine learning is a subset of the broader category of AI.

Put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data. 

Computer programmers and software developers enable computers to analyze data and solve problems — essentially, they create artificial intelligence systems — by applying tools such as:

  • machine learning
  • deep learning
  • neural networks
  • computer vision
  • natural language processing

Below is a breakdown of the differences between artificial intelligence and machine learning as well as how they are being applied in organizations large and small today.

What Is Artificial Intelligence?

Artificial Intelligence is the field of developing computers and robots that are capable of behaving in ways that both mimic and go beyond human capabilities. AI-enabled programs can analyze and contextualize data to provide information or automatically trigger actions without human interference.

Today, artificial intelligence is at the heart of many technologies we use, including smart devices and voice assistants such as Siri on Apple devices. Companies are incorporating techniques such as natural language processing and computer vision — the ability for computers to use human language and interpret images ­— to automate tasks, accelerate decision making, and enable customer conversations with chatbots.

What Is Machine Learning?

Machine learning is a pathway to artificial intelligence. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions.

By studying and experimenting with machine learning, programmers test the limits of how much they can improve the perception, cognition, and action of a computer system.

Deep learning, an advanced method of machine learning, goes a step further. Deep learning models use large neural networks — networks that function like a human brain to logically analyze data — to learn complex patterns and make predictions independent of human input.

How Companies Use AI and Machine Learning

To be successful in nearly any industry, organizations must be able to transform their data into actionable insight. Artificial Intelligence and machine learning give organizations the advantage of automating a variety of manual processes involving data and decision making.

By incorporating AI and machine learning into their systems and strategic plans, leaders can understand and act on data-driven insights with greater speed and efficiency.

AI in the Manufacturing Industry

Efficiency is key to the success of an organization in the manufacturing industry. Artificial intelligence can help manufacturing leaders automate their business processes by applying data analytics and machine learning to applications such as the following:

  • Identifying equipment errors before malfunctions occur, using the internet of things (IoT), analytics, and machine learning
  • Using an AI application on a device, located within a factory, that monitors a production machine and predicts when to perform maintenance, so it doesn’t fail mid-shift
  • Studying HVAC energy consumption patterns and using machine learning to adjust to optimal energy saving and comfort level

AI and Machine Learning in Banking

Data privacy and security are especially critical within the banking industry. Financial services leaders can keep customer data secure while increasing efficiencies using AI and machine learning in several ways:

  • Using machine learning to detect and prevent fraud and cybersecurity attacks
  • Integrating biometrics and computer vision to quickly authenticate user identities and process documents
  • Incorporating smart technologies such as chatbots and voice assistants to automate basic customer service functions

AI Applications in Health Care

The health care field uses huge amounts of data and increasingly relies on informatics and analytics to provide accurate, efficient health services. AI tools can help improve patient outcomes, save time, and even help providers avoid burnout by:

  • Analyzing data from users’ electronic health records through machine learning to provide clinical decision support and automated insights
  • Integrating an AI system that predicts the outcomes of hospital visits to prevent readmissions and shorten the time patients are kept in hospitals
  • Capturing and recording provider-patient interactions in exams or telehealth appointments using natural-language understanding