Internet of Things

Learn the skills to become an Internet of Things expert

Course Code : 0110

$1895

Overview

The Internet of Things course offered by Cognixia introduces participants to advanced IoT concepts, methodologies, and protocols used for communication. This includes next-generation, IoT-friendly applications, physical-layer protocols, and widely accepted IoT frameworks and standards. The program covers popular, service-rich cloud platforms and focuses on how to build and deploy IoT solutions. Practical use-cases and case studies are included to ensure that the participant develops an ability to work through real-life scenarios.

Schedule Classes

Delivery Format
Starting Date
Starting Time
Duration

Live Virtual Classroom
Saturday, 20 July 2019
09:30 AM - 12:30 PM EST
16 Days (Sat - Sun)

Delivery Format
Starting Date
Starting Time
Duration

Live Virtual Classroom
Friday, 16 August 2019
10:30 PM - 01:30 AM EST
16 Day (Fri - Sat)

Looking for more sessions of this class?

Course Delivery

This course is available in the following formats:

Live Classroom 
Duration: 16 days

Live Virtual Classroom
Duration: 16 days

*shipping charges extra

What You'll learn

  • IoT technology and tools
  • Core concepts and background technologies
  • Features of the IoT landscape
  • Sensors, microcontrollers and communication interfaces to design and build IoT devices
  • Designing and building a network based on the client server
  • Publishing/subscribing to connect, collect data, monitor and manage assets
  • Writing device, gateway and server-side scripts and apps, enabling them to aggregate and analyze sensor data
  • Selecting application-layer protocols and web services architectures for a seamless integration of various components within an IoT ecosystem
  • Reviewing standard development initiatives and reference architectures
  • Deploying various types of analytics on machine data to define context, find faults, ensure quality and extract valuable actionable insights
  • Cloud infrastructure, services, APIs and architectures of commercial and industrial cloud platforms
  • Prevalent computing architectures, including distributed, centralized, and edge/fog computing

Outline

  • 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
  • Market statistics
  • Early adopters
  • Roadmap
  • Development
  • Deployment and monetization of applications as service
  • Knowledge discovery process
  • DIKW pyramid and relevance to IoT
  • Microcontrollers: Cost, performance and power consumption
  • Commercial microcontroller-based development boards
  • Selection criteria and trade-offs
  • Industrial networks, M2M networks
  • Transducer: Sensor and Actuator
  • 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
  • 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
  • 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
  • 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
  • Standards and best practices
  • Common vulnerabilities
  • Attack surfaces
  • Hardware and software solutions
  • Open-source initiatives
  • 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
  • Programming micro-controllers (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, sub-setting and visualization
  • Set of Python exercises to demonstrate descriptive and predictive analytics
1. Development Boards

  • Raspberry Pi 3
  • Arduino Mega (ATMega2560) with USB cable
  • ESP8266 NodeMcu

2. 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

3. Communication Modules

  • WiFi – ESP01
  • Bluetooth – HC05
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Prerequisites

Cognixia’s Internet of Things course is designed for professionals with a basic understanding of electronic circuit design, microcontrollers, and programming languages, as well as knowledge of computer fundamentals.

Who Should Attend

Cognixia’s Internet of Things course is highly recommended for current and aspiring –

  • IT professionals
  • Electrical and electronics engineers
  • Designers
  • Solution architects
  • Existing and budding entrepreneurs keen to build smart solutions for customers
  • Fresh graduates who meet the prerequisite criteria

Interested in this course? Let’s connect!

Certification

Participants will be awarded with an exclusive certificate upon successful completion of the program. Every learner is evaluated based on their attendance in the sessions, their scores in the course assessments, projects, etc. The certificate is recognized by organizations all over the world and lends huge credibility to your resume.

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