Internet of Things (IoT) Training

App Dev

Course Description

Internet of Things, commonly referred to as IoT, is the network of physical objects, devices, vehicles, buildings, and other items that’s been integrated into the technology of modern electronics, software, sensors, and other “things” with network connectivity that enables them to collect and exchange data. Once collected, this data becomes a powerful resource, which companies and technologies are tapping into, in revolutionary ways. The technology promises to enhance data resources, improve efficiencies, and increase productivity for organizations globally. If there’s ever been a fantastic time to leap into this path-breaking technology, it’s now.

Course introduces advanced concepts and methodologies to design, build, and deploy IoT solutions, and discusses various technologies and protocols used for communication – including next-generation, IoT-friendly applications and physical-layer protocols. Participants will gain a thorough understanding of widely accepted IoT frameworks and standards.

3 Days

  • Gain expert-level knowledge of IoT technology and tools
  • Build a sound understanding of core concepts, background technologies, and the different features of the IoT landscape
  • Learn about sensors, microcontrollers and communication interfaces to design and build IoT devices
  • Obtain knowledge and skills to design and build a network based on the client server, as well as how to publish/subscribe to connect, collect data, monitor and manage assets
  • Learn how to write device, gateway and server-side scripts and apps, enabling them to aggregate and analyze sensor data
  • Learn how to select application-layer protocols and web services architectures for a seamless integration of various components within an IoT ecosystem
  • Review standard development initiatives and reference architectures
  • Explore how to deploy various types of analytics on machine data to define context, find faults, ensure quality, and extract valuable actionable insights
  • Understand cloud infrastructure, services, APIs and architectures of commercial and industrial cloud platforms
  • Build an understanding of prevalent computing architectures, including distributed, centralized, and edge/fog computing
  • IT professionals, electrical and electronics engineers, designers and solution architects.
  • Entrepreneurs who are interested in building smart solutions for their customers.
  • Professionals working in sectors such as pharmaceuticals, real estate, sales, finance, designing, manufacturing, electrical equipment, retail, healthcare, etc. can also benefit from learning about IoT solutions
  • Fresh graduates and newcomers can also start their career on the right foot with an Internet of Things
  • The course is designed for professionals with at least a basic level of understanding of electronic circuit design, microcontrollers and programming languages
  • A well as knowledge of computer fundamentals.

Introduction to Internet of Things

  • Concept and definitions
    • Embedded Systems, Computer Networks, M2M (Machine to Machine Communication), Internet of Everything (IoE), Machine Learning, Distributed Computing, Artificial Intelligence, Industrial Automation
    • Interoperability, Identification, localization, Communication, Software Defined Assets
  • Understanding IT and OT Convergence: Evolution of IIoT & Industry 4.0
  • IoT Adoption
    • Market statistics, Early adopters, Roadmap
  • Business opportunities: Product + Service model
    • Development, deployment and monetization of applications as service
  • Use cases

Concept of Data, Information, Knowledge and Wisdom

  • Knowledge discovery process
  • DIKW pyramid and relevance to IoT
  • Microcontrollers: cost, performance and power consumption
    • Commercial microcontroller-based development boards
    • Selection criteria and tradeoffs
  • Industrial networks, M2M networks

Sensor Data Mining and Analytics

  • Transducer: Sensor and Actuator
    • Sensors – Types of sensors, sampling, analog to digital conversion, selection criteria of sensor and ADC
  • Data acquisition, storage and analytics
    • Signals and systems
    • Signal processing, systems classification, sampling theorem
  • Ensuring quality and consistency of data
  • Real-time analytics
    • Understanding fundamental nuances of IoT and Big data
    • Usage of IoT data in various business domains to gain operational efficiency
  • Edge analytics
  • Data aggregation on edge gateway

Wireless Sensor Area Networks (WSAN): Evolution of M2M and IoT Networks and Technologies

  • Sensor nodes
    • Sensor node architecture
  • WSN/M2M communication technologies
    • Bluetooth, Zigbee and WiFi communication technologies
    • Cellular communication and LPWAN (LoRa and LoRaWAN) technologies
  • Topologies
  • Applications

Design and Development of IoT Systems

  • IoT reference architectures
    • Standardization initiatives
    • Interoperability issues
  • IoT design considerations
    • Architectures Device, Network and Cloud
    • Centralized vs distributed architectures
  • Networks, communication technologies and protocols
  • Smart asset management: Connectivity, Visibility, Analytics, Alerts

Cloud Computing and Platforms

  • Public, Private and Hybrid cloud platforms and deployment strategy
  • Industrial Gateways
    • Commercial Gateways solutions from various vendors
    • Cloud-based Gateway solutions
  • IaaS, SaaS, PaaS models
  • Cloud components and services
    • Device Management
    • Databases, Visualization
    • Reporting
    • Notification/Alarm management
    • Security management
    • Cloud resource monitoring and management
  • Example platforms: ThingSpeak, Pubnub, AWS IoT
    • AWS IoT Services
      • Device Registry
      • Authentication and Authorization
      • Device Gateway
      • Rules Engine
      • Device Shadow

IoT Security

  • Standards and best practices
    • Common vulnerabilities
    • Attack surfaces
    • Hardware and Software solutions
    • Open-source initiatives

Analytics

  • Descriptive, Diagnostic, Predictive, and Prescriptive
  • Analytics using Python advance packages: NumPy, SciPy, Matplotlib, Pandas and Sci-kit learn
  • CASE STUDIES AND ROADMAP
  • Cold Chain monitoring
  • Asset tracking using RFID and GPRS/GPS

Hands-on/Practical Exercises

  • Programming microcontrollers (Arduino, NodeMCU)
  • Building HTTP and MQTT based M2M networks
  • Interfacing Analog and Digital sensors with microcontrollers for real-time data acquisition, as well as storage and analysis on IoT endpoints and edges
  • Developing microcontroller-based applications to understand event-based, real-time processing and in-memory computations
  • Setting up Raspberry Pi as gateway to aggregate data from thin clients
  • Python programming on Raspberry Pi to analyze collected data
  • GPIO programming using Python and remote monitoring /control
  • Pushing collected data to cloud platforms
  • Uploading data on local gateway as cache
  • Uploading data on cloud platforms
  • Monitoring and controlling devices using android user apps and Bluetooth interfaces
  • Building wireless sensor networks using WiFi
  • Remote controlling machines using cloud based apps
  • Remote controlling machines using device based apps through cloud as an intermediate node
  • Interfacing Raspberry Pi with AWS IoT Gateway service to exchange messages
  • Data cleaning, subsetting and visualization
  • Set of Python exercises to demonstrate descriptive and predictive analytics

Hardware Kit

  • Development Boards
    • Raspberry Pi 3
    • Arduino Mega (ATMega2560) with USB cable
    • ESP8266 NodeMcu
  • Electronic Components
    • Sensors – Analog temperature sensor (LM35)
    • IR Proximity Sensor
    • Switches – Push Button (10)
    • Breadboard
    • LEDs (10)
    • Resistors (10)
    • Connecting leads (25)
    • Memory Card (16 GB)
    • HDMI – VGA Converter
    • 1A Power Adapter
  • Communication Modules
    • WiFi – ESP01
    • Bluetooth – HC05
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